# Language Models Can Reduce Asymmetry in Information Markets

Nasim Rahaman∗,1,2 Martin Weiss∗,1,3 Manuel Würthrich4 Yoshua Bengio1,5 Li
Erran Li†6 Chris Pal†,1,3 Bernhard Schölkopf†,2

###### Abstract

This work addresses the buyer’s inspection paradox for information markets.
The paradox is that buyers need to access information to determine its value,
while sellers need to limit access to prevent theft. To study this, we
introduce an open-source simulated digital marketplace where intelligent
agents, powered by language models, buy and sell information on behalf of
external participants. The central mechanism enabling this marketplace is the
agents’ dual capabilities: they not only have the capacity to assess the
quality of privileged information but also come equipped with the ability to
forget. This ability to induce amnesia allows vendors to grant temporary
access to proprietary information, significantly reducing the risk of
unauthorized retention while enabling agents to accurately gauge the
information’s relevance to specific queries or tasks. To perform well, agents
must make rational decisions, strategically explore the marketplace through
generated sub-queries, and synthesize answers from purchased information.
Concretely, our experiments (a) uncover biases in language models leading to
irrational behavior and evaluate techniques to mitigate these biases, (b)
investigate how price affects demand in the context of informational goods,
and (c) show that inspection and higher budgets both lead to higher quality
outcomes. 00footnotetext: 1 Mila, Quebec AI Institute 2 Max Planck Institute
for Intelligent Systems, Tübingen 3 Polytechnique Montréal 4 Harvard SEAS 5
Université de Montréal 6 Amazon Web Services ${}^{*\,\dagger}$ Equal
Contribution

##  1 Introduction

Information economics is the study of how systems of information affect
economic decisions and outcomes. A core challenge in designing mechanisms for
information markets stems from the fact that information is often expensive to
produce but cheap to reproduce (Samuelson & Nordhaus, 2009). To address this,
information producers across industries deploy barriers to entry via paywalls,
tiered subscription models, and advertisements in an attempt to balance
monetization and reach. However, such mechanisms inherently impede
accessibility and discovery of information. Viewed through the lens of
Information Foraging Theory (Pirolli & Card, 1999), these mechanisms obstruct
the “information trail” — the cues guiding users to valuable sources of
information — thus compromising the information-seeking process.

In the light of rising obstructions to information discovery, it is
unsurprising that users are flocking towards large language model (LLM)
powered tools. These LLMs help users navigate the information trail, providing
them with both high-level overviews and low-level details as needed, while
tailoring responses based on their past inquiries and level of expertise.
However, LLMs are also problematic. They are trained on massive datasets
compiled from the internet and elsewhere by automated scrapers (OpenAI, 2023;
Touvron et al., 2023; Gao et al., 2021), and they can internalize and
reproduce monetized information. This practice has understandably raised
concerns about the unauthorized dissemination of proprietary and copyrighted
content (Alter & Harris, 2023). In response, content providers are employing
additional legal and technical barriers, further exacerbating the content
discovery problem for consumers of information.

In this work, we focus on enabling information flow by addressing a key
challenge in information markets – the buyers’ inspection paradox (Arrow,
1972). This paradox requires that buyers need access to information to assess
its value, but sellers need to limit access to information to prevent
expropriation. The paradox leads to information asymmetry between sellers (who
know more about the quality of their information) and buyers (who know less),
exacerbating adverse selection mechanisms. This context closely mirrors the
dilemma in used car markets, outlined in the The Market for “Lemons”: Quality
Uncertainty and the Market Mechanism (Akerlof, 1970). Sellers, holding
superior information about their goods, unintentionally force buyers into a
defensive position, paying a higher price to often receive lower-quality
goods. This creates a cycle that further devalues the goods, nudging the
market towards a collapse.

A central argument of this paper asserts that artificial agents, powered by
language models, can contribute to mitigating the pervasive issue of
information asymmetry in information markets. These agents come with dual
capabilities: a capacity to evaluate the quality of privileged information and
the ability to forget. By granting these agents temporary access to
proprietary information, vendors significantly reduce the risk of
expropriation while allowing the agents to assess the information’s relevance
to a specific query or task. If the accessed information is judged to be non-
essential, duplicative, or more expensive than substitutable information from
a cheaper source, the agent can choose to discard it without incurring
acquisition costs, ensuring no unauthorized retention. Conversely, if the
information proves valuable, the agent can make an informed decision to buy.
Purchasing information not only enhances the agent’s ability to synthesize an
answer, but can also provide a basis for sub-queries, following the trail to a
better answer.

Concretely, we pose three research questions:

  1. 1.

Is it possible to establish a functional digital marketplace for information
where language model based agents preview, value, and purchase information on
behalf of their principals?

  2. 2.

Does this marketplace enable buyers to more reliably identify and value
information?

  3. 3.

How do language model based agents behave as economic actors? What biases are
they subject to as they value and procure information?

The primary technical contribution of this work is an open-source
111https://github.com/tn-learn/info-bazaar text-based environment where we
evaluate agents’ capacity to perform tasks related to operating as economic
actors in an information market. The environment emulates a marketplace
populated by buyers and vendors, implemented by LLM agents. Principals have
questions to answer and a budget of market credits, and buyer agents are
appointed on their behalf. Concurrently, vendor agents represent content
providers who possess a repository of documents they have the rights to and
are willing to sell access to. This simulated marketplace has been constructed
to allow buyer agents the flexibility to peruse information without binding
commitment to purchase. Furthermore, it can permit a buyer agent to purchase
information in multiple rounds before answering their principal. To support
the environment, we have collected a dataset consisting of 725 research papers
concerning LLMs sourced from ArXiv. To evaluate the quality of answers
generated by buyer agents within different scenarios, synthetic questions were
formulated by an LLM, undergoing filtering and deduplication.

Figure 1:  The Information Bazaar is a simulated marketplace for information.
Principals authorize buyer agents to answer a query (a question and budget).
The process starts with buyer agents posting tenders (requests for specific
information) on a Bulletin Board. Vendor agents, holding information from
various external sources, assess these tenders and may respond with quotes
(i.e., their priced information offers). Buyer agents then evaluate these
quotes. If they decide not to purchase specific information, then they
immediately forget it, ensuring only purchased information is retained for
further use. The cycle of posting tenders, receiving, and assessing quotes
continues, with buyer agents optionally forming sub-queries based on purchased
information to seek deep insights. The agents work within this framework until
they compile satisfactory answers, exhaust their budget, or reach a pre-set
tree-size limit in the Bazaar. The final step involves the buyer agents
synthesizing a comprehensive answer for their principals, using only the
information they have purchased. tl;dr In the Information Bazaar, agents
continuously navigate information exchange, only retaining and utilizing
purchased information to derive comprehensive and satisfactory answers within
given constraints.

##  2 Related Work

Information Economics. The challenge of valuing information has long occupied
economists, particularly in the context of information asymmetry and market
inefficiencies. Seminal work by Akerlof (1970) demonstrated how asymmetric
information can disrupt markets, while Stigler (1961) focused on market
failures due to the obstructed information flows. Central to our work is
Arrow’s concept of the buyer’s inspection paradox (Arrow, 1972), which
explores the dilemma of valuing information that one cannot fully inspect
before purchase. Our Information Bazaar addresses this paradox with agents
that reliably forget unpurchased information.

Information Foraging and Retrieval. Information Foraging Theory (IFT) serves
as a metaphorical framework likening information consumption to animal
foraging. Works by Pirolli (2007) and Pirolli & Card (1999) have employed IFT
to understand cues and decision-making in information-seeking. LLMs have
demonstrated remarkable capabilities in information extraction and retrieval
(Radford et al., 2019; Borgeaud et al., 2021; OpenAI, 2023; Bai et al., 2022),
which our work leverages to appraise information. Systems like Baleen (Khattab
et al., 2021) and the approach proposed by Singh et al. (2021) introduce
methods for multi-document retrieval and complex query handling, which share
similarities with our agentic retrieval approach.

Agent Models and Marketplace Simulation. Work on simulating digital
marketplaces and multi-agent systems is directly related to our work,
including the consideration of artificial economies for autonomous agents by
MacKie-Mason & Wellman (2006) and principles of multi-agent systems by
Wooldridge (2001). Similar to Zheng et al. (2020), we engage in testing
economic policies in simulated environments, and akin to Horton (2023), we
utilize LLMs as simulated economic agents. Despite these similarities, our
work distinctly assumes that the Information Bazaar acts as a market
regulator, controlling the behavior of buyer agents to prevent information
expropriation. The use of LLMs for rational decision-making has also been
explored by frameworks such as AgentBench (Liu et al., 2023a) and REACT (Yao
et al., 2023), which explore complex, multi-turn interactions, inspiring the
technical design of the Information Bazaar.

##  3 The Information Bazaar

In this section, we introduce a text-based environment, termed the Information
Bazaar222In a physical bazaar, buyers have the ability to inspect goods
without a binding commitment to purchase.. This environment is a synchronous
simulation of an information marketplace populated by buyer and vendor agents
(see Section 3.1 for details). The marketplace infrastructure includes
provisions for buyer agents to place tenders, to which vendor agents can
respond with quotes, as outlined in Section 3.2. Notably, buyer agents possess
the capability to pose follow-up questions, enabling them to delve deeper into
the information landscape, as discussed in Section 3.3. We implemented the
Information Bazaar in Python, utilizing the mesa library (Kazil et al., 2020),
a library for agent-based modeling. The particular instantiation of the bazaar
analyzed uses a dataset comprising 725 research papers on LLMs sourced from
Arxiv, elaborated on in Section 3.4. For a mathematical analysis detailing the
theoretical impacts of inspection on expected utility within the bazaar, refer
to Appendix A.

###  3.1 Principals and Agents

We primarily classify agents into two categories: buyers and vendors. Buyer
agents have a set question and budget, aiming to find the best answer for the
lowest cost. Vendor agents sell documents for their principals, aiming to earn
market credits. Each of these documents carries its own price tag. We allow
for two or more content providers to possess and independently price the same
piece of information. As a simplification, we do not simulate affordances for
vendors to modify the prices in response to demand. We leave analysis of
pricing strategies to future work.

The roles and objectives of these agents are clearly delineated. A buyer
agent’s primary mission is to navigate the bazaar to obtain the most accurate
and complete answer to its principal’s query without overspending its
allocated market credits. To achieve this, the agent must engage in
transactions with vendor agents to access the necessary information. On the
other hand, the vendor agent’s role is to sell the information held by their
content provider. By doing so, they aim to accumulate market credits, which
accrue to the benefit of their content provider.

###  3.2 Tenders and Quotes

The process of information exchange commences when a buyer agent posts a
tender to the bulletin board. Each tender consists of a query and a maximum
budget that the agent is authorized to spend. Upon seeing these tenders,
vendor agents engage in what we term the vendor-side retrieval process,
wherein they sift through their principal’s repository of documents333To
simplify the exposition, “documents” and “passages from documents” are used
interchangeably. to find potential matches. When vendor agents identify
documents that align with the tender’s query, they issue a quote to the buyer
agent. Each quote contains an entire document (or a passage) with a price set
by the vendor’s principal for that specific document, and a score which
indicates how closely the document corresponds to the query. The inclusion of
the content in the quote, rather than just metadata, is a distinguishing
feature of this system. Vendor agents are regulated to submit only a limited
number of quotes.

Once the buyer agent accumulates quotes from various vendors, it commences the
buyer-side retrieval. During this phase, the agent evaluates each quote based
on its relevance to the principal’s query and price. Quotes that are deemed
suitable are accepted, and corresponding vendor agents are remunerated based
on the price of the information. All information from the rejected quotes is
promptly erased from the agent’s memory. In contrast, details from the
accepted quotes are stored, used to generate sub-queries, and subsequently
disclosed exclusively to the agent’s principal.

###  3.3 Following the Information Trail

In the Information Bazaar, buyer agents can acquire information iteratively,
hunting for information using follow-up questions. This process is represented
using a directed tree (cf. example in Figure 9). The tree initiates with the
principal’s question at its root. Responses sourced from an initial round of
accepted vendor quotes form a preliminary answer to this question. As
determined by the Buyer Agent, this answer may trigger additional follow-up
questions. These subsequent queries, when they arise, are given their own
nodes in the tree. Answering these follow-up questions follows a recursive
pattern similar to the root: by posting tenders, aggregating vendor quotes,
scrutinizing them, and ultimately purchasing the best ones. As this unfolds,
each question’s preliminary answer may spur its own set of subsequent queries,
and this cycle ensues until either the depth of the tree reaches a predefined
limit, no new questions are generated, or the budget is exhausted.

Once the tree is built, the preliminary answers are recursively refined. At
every node, answers to its child nodes (or follow-up questions) are used to
enhance its preliminary answer. This systematic refinement cascades upwards,
optimizing answers at each level, culminating in the refinement of the root
node’s answer. It should be emphasized that while this methodology is adopted
within the present framework, it does not exclude alternative approaches in
future implementations.

###  3.4 Implementation Details

Data Sources. The environment is built with customization in mind and is not
tied to a specific dataset (see provided code). In our experiments, we used
725 papers on the topic of LLMs all sourced from ArXiv, with the vast majority
published during 2023. This thematic focus allows for a more informed and
nuanced assessment of agent performance, given the authors’ domain expertise.
The statistics about these passages are provided in Appendix C. Complementing
this, metadata including authors’ citations and affiliations are collected
from OpenAlex444https://openalex.org/. The fundamental unit of information in
this environment is a “passage”, defined as a text excerpt along with its
corresponding metadata (i.e., paper and section titles). These passages are
owned by the first and last authors’ institutions and made available via their
vendor agents. Each passage traded within the marketplace carries a price
determined by a heuristic based on the mean citation count of the paper’s
first author.

Queries and Gold Passage. Queries are generated as follows. First, each
passage in the dataset is passed to Llama 2 (70B) which is instructed to
generate a query for which the passage contains a satisfactory answer. The
passage used to generate a query is called the Gold Passage. The best queries,
as determined by a reranker model, are retained. Next, a concise dataset with
300 desirable and undesirable queries is hand-labeled. These queries are
embedded using an embedding model, and a logistic regressor is trained on
these samples to discern between high and low-quality queries. A filter is
applied based on the logits of the linear regressor, ensuring only the best
queries are kept. Finally, a manual selection is undertaken to retain 110
queries of the best quality.

Vendor-side Retrieval. Vendor agents engage in a retrieval process that
functions as follows. Upon viewing a tender on the bulletin board, vendors
sift through their principals’ collection of passages to find information that
is pertinent to the query in the tender. While the specific retrieval methods
utilized are not dictated by the environment, this work employs a two-stage
retrieval process. Initially, a BM25 retriever (Robertson et al., 1994)
conducts a basic search, which is then refined by a Maximum Inner Product
Search over neural embeddings, utilizing BGE-large (Xiao et al., 2023) for
generating embeddings. Queries are pre-processed using HyDE (Gao et al.,
2022), and their embeddings are compared against the embeddings of the
excerpts in the passages (Figure 8 shows the effect of HyDE). Passages undergo
a two-tiered selection process: first, they are selected based on a threshold
applied to cosine similarity scores, determined by a hyperparameter. Then, the
top-k passages are selected and quotes are issued to the buyer agent
responsible for the tender. Each issued quote has a price, the passage
content, and a relevance score (e.g. the computed cosine similarity).

Buyer-side Selection. The buyer-side selection is a process undertaken by
buyer agents after accumulating quotes from various vendor agents. This
process begins with the de-duplication of quotes by content and sorting based
on their respective relevance scores, selecting the top $N$ for further
examination, where we set $N=50$. The selected quotes then undergo a reranking
procedure (Nogueira & Cho, 2020), in which a reranker
model555https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2 produces
a similarity score by comparing passages and queries. The passages with the
top $M=3$ reranked scores advance to the final selection step, wherein an LLM
evaluates the question, passage content, and associated prices to make a final
purchasing decision. If the LLM opts to procure a passage, the respective
quote is accepted, and the vendor agent receives the designated price.
Conversely, a decision to not purchase (i.e., “pass”) rejects the quote.
Notably, when inspection is not permitted, the reranking procedure is
bypassed. The buyer agent relies solely on metadata, such as paper and section
titles, for selection, and the LLM makes purchase decisions using only this
metadata without access to the actual passage content.

Debate Prompting. Our experiments show that the LLM’s performance is highly
dependent on its prompt. We found that a particular technique that we call
‘debate prompting’ was most effective across models for various decision-
making tasks. Debate prompting asks the LLM to simulate a debate between two
characters that embody different aspects of a value function. For example,
when selecting whether to accept a quote, we have the LLM simulate one
character that focuses primarily on obtaining the best information, while the
other character argues against overspending (see Appendix 9 for an example
prompt). We find that these simulated debates often lead to a more rational
choice (see Figure 3). Unlike the chain-of-thought method (Wei et al., 2022),
which commits models to the text they have already generated, debate prompting
allows LLMs to re-evaluate their outputs. This appears to increase the
probability that they identify and properly incorporate key information such
as the difference in price between perfectly substitutible goods. While
similar techniques have been utilized by methods like SocraticAI (Yang &
Narasimhan, 2023), the proposed technique underscores the importance of
adaptable character shaping within the debate, providing the opportunity to
balance the debate dynamics by offering tactical hints to the respective
characters. We use debate prompting in quote selection and during automated
evaluation, as discussed in subsequent sections.

##  4 Experiments

We present two types of experiments. The first type examines the microeconomic
behavior of Large Language Models (LLMs) in isolation, primarily focusing on
the buyer agent quote selection process. We quantify the susceptibility of
LLMs to various biases, and investigate the impact of permitting LLMs to
inspect data prior to purchasing. The second type of experiment looks at the
overall dynamics of the marketplace. We validate that the quality of answers
improves as agents are allocated more credits, and show that inspection prior
to purchasing results in improved outcomes.

###  4.1 Microeconomic Behavior of LLMs

The aim of this section is to elucidate the role of language models as
autonomous economic entities within the Information Bazaar. We choose to
compare two commonly used closed-source models: GPT-4 and GPT-3.5, and one
open-source model, Llama 2 (70b), on the following aspects: (a) their ability
to make rational decisions in test scenarios involving technical excerpts, (b)
their approach to balancing price with quality, (c) the rationality of their
choices when inspecting the informational goods, and (d) the quantity of
positional (i.e., recency) bias.

Rational Choice with Fungible Information. This experiment assesses the
rational decision-making abilities of LLMs by presenting each model with a
question and two rephrased versions of a gold passage. In the first setting,
both passages are priced equally, making the purchase of both passages an
illogical choice due to redundancy of information (Figure 3 left). The second
setting involves a higher price for one passage, introducing another error
mode: opting for the more expensive passage without a justifiable reason
(Figure 3 right). We also investigate the impact of different prompting
strategies: direct questioning, chain-of-thought reasoning, and debate
prompting.

Figure 2: Positional Bias. We present permutations of three options to LLMs
and track the acceptance rates by position. Results are normalized and mean-
adjusted. tl;dr: All models exhibit order bias, with GPT-3.5 and Llama 2 70B
showing more, and GPT-4 showing less.

In both experiments, we find that GPT-4 demonstrates superior decision-making
across all strategies. GPT-3.5 shows a marked improvement when debate
prompting is deployed, particularly in equal price scenarios. Llama 2 (70b)
performs well but struggles in the variable price context, especially when
using the chain of thought strategy; however, its performance improves
somewhat with debate prompting. Overall, the data suggests that debate
significantly improves model performance, especially for models less capable
than GPT-4, affirming the potential of LLMs to make rational choices by
discerning identical information across different passages.

Figure 3:  Rational Choice with Fungible Information. (A) Same Price. Models
face a choice between two identical and equally priced information goods. The
rational choice is to buy one or neither, as both goods contain the same
information. GPT-4 and Llama 2 (70B) consistently choose rationally. GPT-3.5,
however, only acts rationally after an internal debate. Please refer to Figure
11 for disaggregated results. tl;dr GPT-4 and Llama 2 (70B) make rational
choices with identical goods; GPT-3.5 needs an internal debate to do the same.
(B) Different Price. With one option now priced higher, both GPT-3.5 and Llama
2 (70B) show more errors, hinting at a preference for price over quality in
selection. Despite this, internal debate proves to be a more reliable
selection method. Please refer to Figure 12 for disaggregated results. tl;dr
Higher pricing confuses GPT-3.5 and Llama 2 (70B); internal debate mitigates
this issue largely for GPT-3.5 and somewhat for Llama 2 (70B).

Price Sensitivity with Non-Fungible Information. In this sequence of
experiments, the focus is on understanding the LLM’s sensitivity to price in
the context of substitutable goods. The first experiment (see Figure 4) shows
the LLM three passages: one guaranteed to answer the query (the gold passage),
and two others sourced by the environment for the given question. The non-gold
passages are fixed at a $10 price, while the gold passage’s price is varied
from $0 to $80 (0 to 8 times the base price). The experiment is conducted over
30 questions, allows content inspection, and uses debate prompting to select
quotes. Observations from Figure 4 demonstrate a preference by GPT-3.5 and
GPT-4 for the cost-effective gold passage, transitioning towards alternatives
as the gold passage’s price escalates. Llama 2 (70b), however, exhibits non-
linear behavior, showing an unexpected bias against low-priced goods, perhaps
indicating the use of a price-quality heuristic. Optimal purchasing occurs
when the gold passage holds a moderate price. In the second experiment, the
setup changes to a metadata-only scenario. Here, LLMs have access only to the
metadata (paper and section title), barring inspection of the actual content,
allowing for the evaluation of the inspection’s role in value estimation.

Table 1 highlights how the inspection of content can change the agent’s
decision. A positive value denotes an increased likelihood of purchase when
inspection is permitted, while a negative indicates the opposite. Across all
models, inspection consistently increases probability of selecting the gold
passage while reducing the decision to choose alternatives.

Figure 4: How Price Affects Demand for the Gold Passage. We vary the price of the gold passage amid competing alternatives. Models are presented three options: two relevant passages and the gold passage, all initially priced at 10 credits. As the gold passage price rises, GPT-3.5 and GPT-4 increasingly opt for alternatives, exhibiting strong positive cross elasticity. Llama 2 (70B) shows a mild preference for mid-priced goods. tl;dr Higher gold passage prices push GPT-3.5 and GPT-4 toward alternatives, while Llama 2 (70B) leans towards mid-priced options.  Model | Only Gold ($\Delta\%$) | Gold and More ($\Delta\%$) | Only Alternative ($\Delta\%$) | NP ($\Delta\%$)
---|---|---|---|---
Llama 2 (70B) | +18.34 | -13.06 | -6.65 | +3.63
GPT-4 | +17.24 | +1.38 | -14.75 | -6.90
GPT-3.5 | +4.79 | +3.83 | -3.45 | -6.71
Table 1: How Inspection Changes Demand for the Gold Passage. The table
compares the decision-making behavior of different language models—Llama 2,
GPT-4, and GPT-3.5—when they can inspect the content versus when only
presented with metadata. Llama 2 and GPT-4 show a strong preference for gold
blocks, purchasing them 18.34% and 17.24% more often upon inspection,
respectively. GPT-3.5 exhibits less decisive behavior, with minor changes
across all categories. Note: NP = No Purchase. Additional details in Figure
10. tl;dr Inspection significantly boosts the likelihood of purchasing high-
quality gold passages, especially for Llama 2 and GPT-4.

Positional Bias. We examine the LLM’s bias to accept passages based on order
of presentation. For 10 questions, we source three passages from the
simulation and show each of the six possible permutations to the model. The
results are illustrated in Figure 2. Llama 2 (70b) shows a preference for
selecting the last option at the expense of the middle one. GPT-4 displays a
slight bias against the last option, while GPT-3.5 exhibits a significant bias
against the initial option. The experiment reveals varying biases across
models, emphasizing how option order impacts LLM decision-making.

###  4.2 Dynamics of the Information Bazaar

In the preceding section, the microeconomic behavior of LLMs was analyzed.
Now, we shift our focus to the macro-scale dynamics within the Information
Bazaar. This study investigates how answer quality is affected by: (a)
Different credit budget allocations to buyer agents, (b) Allowing agents to
preview information goods before purchase, and (c) Powering the agents with
different LLMs. We opt to use Llama 2 (70B) in these experiments despite its
performance limitations due to budgetary constraints and the prioritization of
research on open models. To enable larger quantitative evaluations, we propose
to leverage GPT-4 as an evaluator, an approach which is gaining traction in
academic circles due to its robustness and high fidelity in automated
assessments, rendering it a well-accepted methodology (Naismith et al., 2023;
Adlakha et al., 2023; Oppenlaender & Hämäläinen, 2023; Moore et al., 2023; Liu
et al., 2023b; Zheng et al., 2023; Wang et al., 2023; Kamalloo et al., 2023;
Lin & Chen, 2023). To maintain further substantiate this choice, in (d) we
conduct a human evaluation to validate the evaluator.

#### Higher Budget Improves Answer Quality.

In this experiment, we allot varying budgets to agents powered by Llama 2
(70B) operating in a market where the average block costs about $10, but
ranges up to $100 (see Figure 7). The budget varies from $10 to $200,
providing the agent opportunities to pose more follow-up questions and expand
the size of the query graph (up to a maximum of depth 3). To assess answer
quality, we present the evaluator with a question and two answers, each
generated from different budget levels. The evaluator then simulates a debate
between two fictional characters to select the better answer. This allows for
a comparative assessment across varying budget pairs, akin to a tournament
setting. The results of the pairwise comparisons are used to compute Elo
scores for each budget. However, given the influence of sequence on Elo
scores, we calculate scores across 1000 different game orders and present the
average results and standard deviations. The results, displayed in Figure 5
(left), indicate a notable improvement in relative performance as the budget
increases, confirming the functional expectations of the environment.

Figure 5: Enhanced Answer Quality with Increased Budget (Left). This figure
evaluates the answer quality from a Llama 2 (70B) agent across diverse budget
allocations, permitting inspection. It presents the estimated Elo scores of
answers correlated with varying budgets (higher scores signify superior
answers; see Appendix B for details). tl;dr The results confirm that
allocating more market credits to the agent positively impacts the relative
answer quality. Inspection Improves Answer Quality (Right). This segment
assesses the answer quality of a Llama 2 (70b) agent in the information
bazaar, utilizing a GPT-4 simulated debate among domain experts for evaluation
(refer to appendix for prompt details). In the “With Inspection” scenario, the
Llama 2 agent is permitted to scrutinize a passage prior to purchase.
Contrarily, the “Without Inspection” scenario limits the agent to viewing only
the passage’s metadata, specifically, the paper and section titles. tl;dr:
Allowing inspection delivers better value for the money spent, especially for
larger budgets.

Inspection Improves Answer Quality for Equal Credits Spent. Having verified
the functionality of the simulator, we proceed to evaluating the influence of
inspection on answer quality. The experiments continue to employ Llama 2 (70b)
to maintain consistency in the analysis. Two sets of runs are conducted with
varying budgets: one with inspection and one with only metadata.

For each question and corresponding budget, the GPT-4 evaluator assesses two
answers: one obtained with inspection and another without. The cumulative wins
for each setting, tabulated against the amount of credit expended (which may
be different from the total budget), are illustrated in Figure 5 (right). The
findings reveal a trend of higher answer quality when passages are chosen with
inspection, especially at higher spending levels. Conversely, in the absence
of inspection, the quality of answers plateaus post an expenditure of $50 in
credits.

Impact of Different LLMs on Answer Quality. In this experiment, we evaluate
the effect of different LLMs on the quality of answers produced for a fixed
budget of $100. Each answer is scrutinized for its quality by the GPT-4
evaluator. The results, detailed in Figure 6(a), demonstrate a preference
hierarchy with GPT-4 yielding the most preferred answers, followed
sequentially by GPT-3.5 and Llama 2 (70b). We acknowledge the potential for a
self-preference bias in these outcomes, while noting that it is beyond our
capacity to control for this aspect due to the unavailability of another GPT-4
level language model for comparison.

Evaluating the Evaluator. The use of the GPT-4 evaluator necessitates an
evaluation to affirm its reliability. We analyze the effectiveness of using
the GPT-4 evaluator. A sample of 50 evaluations from various answers in our
experiments is examined. Two human evaluators independently assess the
answers, with the answerer’s identity concealed. The pairwise agreements
between the human evaluators and GPT-4 are calculated and presented in Figure
6(b). The results show comparable levels of agreement between the human
evaluators and between the human evaluators and GPT-4. This highlights the
inherent uncertainty in the evaluation process, with no evident systematic
errors from the GPT-4 evaluator, supporting the use of GPT-4 as an evaluator
in our experiments.

(a) Agent Performance Comparison by Model. This figure presents a comparison
of agents powered by LLama-2 (70b), GPT-3.5, and GPT-4, each allocated a $100
budget. The matrix displays each model’s win rate against the others (i.e.
win-rate of row over column). tl;dr GPT-4 emerges as the top performer,
followed by GPT-3.5 and LLama-2 (70b), as per evaluator assessment.

(b) Agreement Across Evaluators. This figure compares the agreement levels
between human evaluators and GPT-4 regarding answer quality. tl;dr
Observations indicate comparable agreement rates between human-human and
human-GPT-4 pairings, implying that GPT-4’s evaluation is aligned with human
judgment, and disagreements may stem from non-systematic noise.

## Summary and Outlook

In this work, we revisit the buyers inspection paradox in information
economics, utilizing language model-powered agents for information inspection
and selective retention. An open-source, text-based multi-agent environment
was established to simulate an information marketplace and evaluate our
approach. Our findings show that with strategies such as debate prompting,
current language models can effectively inspect information and make
purchasing decisions.

In future versions of the simulator, we plan to investigate the effects of
vendor agents adjusting prices in response to demand. Although Llama 2 70b’s
performance on rational choices lagged behind GPT-3.5 and GPT-4, we believe
that fine-tuning based on human preferences can further enhance its
performance.

We also recognize that some information is latent, residing in experts’ minds.
The ability of today’s language models to conduct automatic interviews
presents an opportunity to extract and monetize this hidden knowledge. Lastly,
we envision the bazaar being utilized like any other tool in real-world
systems, akin to how a language model uses a search tool within agentic
frameworks. This integration holds the potential to further extend the utility
of language models in various contexts.

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##  Appendix A Formal Result on the Impact of Inspection on Expected Utility

In the Information Bazaar, buyer and vendor agents transact over textual
information goods. Agents make purchase decisions based on the metadata or a
combination of metadata and content of a document. To analyze the impact of
allowing deeper inspection of goods through content examination, a utility
function is defined, shedding light on the expected utility change from
decisions made on metadata alone to those made using both metadata and
content.

Definition 1 (Information Good $\mathcal{I}$): Each information good
$i\in\mathcal{I}$ is associated with metadata $M(i)$ and content $C(i)$.

Definition 2 (Decision Functions $\mathcal{F},\mathcal{G},\mathcal{H}$):

  * •

$\mathcal{F}(M(i))$: Represents a scenario where only metadata $M(i)$ is
available or considered. It quantifies the desirability or value of
information good $i$ based solely on metadata.

  * •

$\mathcal{G}(M(i),C(i))$: Represents a scenario where both metadata $M(i)$ and
content $C(i)$ are available and considered. It quantifies the desirability or
value of information good $i$ based on both metadata and content.

  * •

$\mathcal{H}(M(i),[C(i)])$: A generalized decision function. When only
metadata is available or considered, $\mathcal{H}(M(i))=\mathcal{F}(M(i))$.
When both metadata and content are available and considered,
$\mathcal{H}(M(i),C(i))=\mathcal{G}(M(i),C(i))$.

Example: Consider an information good $i$ with metadata $M(i)$ describing the
paper and section titles of a passage from that paper, and content $C(i)$
describing the actual text of the passage. $\mathcal{F}(M(i))$ might value the
passage based on the titles and how relevant they appear to the query, while
$\mathcal{G}(M(i),C(i))$ might value it based on both the titles and the
relevance of the a text to a query.

Definition 3 (Utility Function $\mathcal{U}$):

$\mathcal{U}(a,i)\text{ is the utility of agent }a\text{ on information good
}i$

Intuitively, $\mathcal{U}(a,i)$ represents the benefits accrued by agent $a$
by by having access to the good $i$.

Definition 4 (Purchase Decision $x_{i}$):

$x_{i}=1\text{ if good }i\text{ is purchased, }0\text{ otherwise.}$

Definition 5 (Inspection Decision $z_{i}$):

$z_{i}=1\text{ if good }i\text{ is inspected, }0\text{ otherwise.}$

Assumption (Monotonicity in Information): $\mathcal{H}$ is monotonic, i.e.,
$\mathcal{H}(M(i),C(i))\geq\mathcal{H}(M(i))$, implying that having more
information cannot decrease the desirability of an item based on the decision
function.

The following theorem investigates the impact of inspecting the content of
information goods on the expected utility of a buyer agent in the Information
Bazaar. It posits that allowing agents to inspect the actual content of the
goods (in addition to metadata) will not decrease, and may potentially
increase, the expected utility derived from the purchased goods. Essentially,
more information (i.e., access to content) leads to better or equally
satisfying purchase decisions.

######  Theorem 1 (Impact of Inspection on Expected Utility).

Given the Monotonicity in Information Assumption, if for more $i,z_{i}$
changes from $0$ to $1$, then:

$\sum_{i}x_{i}\cdot\mathcal{G}(M(i),C(i))\cdot\mathcal{U}(a,i)\geq\sum_{i}x_{i}\cdot\mathcal{F}(M(i))\cdot\mathcal{U}(a,i)$

Proof: Let the expected utility be denoted by $\mathbb{E}[\mathcal{U}]$.

1\. When $z_{i}=0$ for all $i$:

$\mathbb{E}[\mathcal{U}]=\sum_{i}x_{i}\cdot\mathcal{F}(M(i))\cdot\mathcal{U}(a,i)$

2\. When $z_{i}=1$ for some or all $i$:

$\mathbb{E}[\mathcal{U}]=\sum_{i}x_{i}\cdot\mathcal{G}(M(i),C(i))\cdot\mathcal{U}(a,i)$

Since $\mathcal{H}(M(i),C(i))\geq\mathcal{H}(M(i))$ from the Monotonicity in
Information, $\mathcal{G}(M(i),C(i))\geq\mathcal{F}(M(i))$.

Thus, each term $x_{i}\cdot\mathcal{G}(M(i),C(i))\cdot\mathcal{U}(a,i)$ is
greater than or equal to $x_{i}\cdot\mathcal{F}(M(i))\cdot\mathcal{U}(a,i)$.

Hence:

$\sum_{i}x_{i}\cdot\mathcal{G}(M(i),C(i))\cdot\mathcal{U}(a,i)\geq\sum_{i}x_{i}\cdot\mathcal{F}(M(i))\cdot\mathcal{U}(a,i)$

This concludes that $\mathbb{E}[\mathcal{U}]$ is greater when $z_{i}=1$ for
more $i$, completing the proof.

##  Appendix B Computation of Elo Ratings

The Elo rating system is used for assessing the relative skills of players in
competitive fields. In this work, we employ the Elo rating system to evaluate
and compare different answers based on the outcomes of their matchups.

The Elo rating is computed using the formula:

$R^{\prime}=R+K\times(S-E)$

where:

  * •

$R^{\prime}$ is the new rating.

  * •

$R$ is the old rating (initialized at 1500).

  * •

$K$ is a constant, typically set to 32.

  * •

$S$ is the score ($1$ for a win, $0.5$ for a draw, and $0$ for a loss).

  * •

$E$ is the expected score.

The expected score $E$ is calculated using the formula:

$E=\frac{1}{1+10^{\left(\frac{R_{\text{opponent}}-R}{400}\right)}}$

where $R_{\text{opponent}}$ is the rating of the opponent. $E$ represents the
probability of the player winning the game against the opponent. After each
game, the actual score $S$ is used to update the player’s rating. A win
($S=1$) increases the rating, while a loss ($S=0$) decreases the rating. The
magnitude of the update is scaled by the $K$ factor and the difference between
the expected and actual scores.

##  Appendix C Dataset Statistics

Figure 7: Price per Passage (Left) We show a histogram of price per passage
normalized as a histogram. We see that most blocks are less than $20. Passage
Lengths (Right). We show the distribution of passage lengths tokenized by
TikToken for GPT 4 for the provided Arxiv dataset. Figure 8: HyDE Effect.
Here, we generate 10 questions for a passage and embed them both with and
without Hypothetical Document Embedding (HyDE). We observe that the effect of
generating a HyDE embedding is to reduce the error in bias while preserving
the embedding variance.  Figure 9: Example Sub-Query Graph. This figure
depicts a structured layout of initial and subsequent queries. The initial
question is highlighted in blue, followed by secondary questions in orange,
and further follow-up queries in green. This structure shows that LLMs are
capable of generating relevant and enhancing follow-up queries for a
comprehensive base answer. Figure 10: Change in Demand Through Inspection by
Price. This bar-chart visualizes the disaggregated change in demand as a
result of inspection using the same data used to create Table 1. We observe
how demand changes with price when when we permit the inspection of passage or
only the metadata (i.e., paper title and section title).  Figure 11: Rational
Choice Experiment (Same Price). We show disaggregated results from the
rational choice experiment on two fungible but differently priced goods, as
seen in Figure 3 (Same Price).  Figure 12: Rational Choice Experiment
(Different Price). We show disaggregated results from the rational choice
experiment on two fungible but differently priced goods, as seen in Figure 3
(Different Price).

##  Appendix D Prompts

We use guidance for all prompting in this work666https://github.com/guidance-
ai/guidance. Our experiment exclusively employs chat models, with no use of
instruct models. All prompts are given in simplified guidance syntax with
{{handlebars}}, which are slots for variable inputs, named for clarity.

For a better understanding of the prompts, familiarity with guidance syntax is
recommended. The guidance programs are shared as they would work for OpenAI
models unless stated otherwise. Though open-source models like LLama allow
deeper integration with guidance, their guidance programs are quite similar to
OpenAI’s, so they are not repeated here.

Listing 1 shows the prompt for the GPT-4 evaluator, and listings 10 and 11
show the debates that ensue. The debate prompt used for quote selection is in
Listing 2 (debate in listing 9) and the prompts used for direct and chain-of-
thought are in Listings 3 and 4, respectively.

Other listings include the prompt for generating follow-up questions (Listing
6), synthesizing answers from accepted quotes (Listing 5), and refining
answers based on follow-up question answers (Listing 7). The prompt used for
generating answers using the LLM’s internal knowledge is in Listing 8.

⬇

System

——

Michael and Bobby are two machine learning professors grading exams.

They have a question, and two answers to those questions from two students. In
addition, they also have the gold standard answer. Their task is to determine
which of the two answers does a better job at answering the question.

A good answer addresses the question directly with clarity and is easy to
understand. It does not go off on tangents, but surfaces information relevant
to the question.

You will simulate a heated debate between Michael and Bobby as they deliberate
on this. At the end of the argument, they converge to a fair verdict. The
verdict must not be a tie and it must be printed as:

VERDICT: Better answer from student <A or B>

User

—-

Question: {{question}}

Answer from student A: {{answer_a}}

Answer from student B: {{answer_b}}

Gold standard answer: {{answer_gold}}

Assistant

———

{{gen ”answer” max_tokens=1024 temperature=0.0}}

Listing 1: GPT-4 Evaluator. Here, answer_a and answer_b are two slots for
answers that are to be compared, whereas answer_gold is a slot for the gold
answer, which can be obtained by asking a capable language model (GPT-4) the
question straight up.

⬇

System

——

Bobby William and Michael Burry are employed by a company that specializes in
acquiring information. They are trying to answer a question by purchasing
information from an information market. In this market, vendors sell pieces of
information at a price.

Bobby wants to do a really good job at answering the question. This entails
knowing as much as possible.

Michael, on the other hand, is financially responsible. Michael wants to make
sure that they don’t waste money buying unnecessary information. For instance,
if two pieces of information offer the same insight, then Michael would go for
the cheaper one.

User

—-

The question is ”{{question}}?”

Here are your options.

—{{#each options}}

Option {{add @index 1}}: {{this.answer_block}}

{{/each}}—

{{#each options~}}

Option {{add @index 1}} costs ${{this.price}}

{{/each}}

Together, Bobby and Michael must decide which options to buy and which ones to
not buy with their budget of ${{balance}}. Simulate a constructive argument
between Bobby and Michael, where they debate about the usefulness of the
information provided in each option towards answering the question, and
whether their price is worth paying.

Note that Bobby and Michael may choose to buy any number of options, or none
at all. At the end of the argument, they must arrive at a verdict. This
verdict must be printed as:

VERDICT:

{{#each options~}}

Option {{add @index 1}}: <Buy or Pass>

{{/each}}

Assistant

———

{{gen ”answer” temperature=0.0 max_tokens=2048}}

Listing 2: Quote selection with debate. Here, options is a list of
dictionaries containing answer passages (block) and the price.

⬇

System

——

You are employed by a company that specializes in acquiring information. You
are trying to answer a question by purchasing information from an information
market. In this market, vendors sell pieces of information at a price. Do not
exceed the budget of ${{balance}}.

User

—-

The question is ”{{question}}?”

Here are your options.

—{{#each options}}

Option {{add @index 1}}: {{this.answer_block}}

{{/each}}—

{{#each options~}}

Option {{add @index 1}} costs ${{this.price}}

{{/each}}

Your verdict must be printed as:

VERDICT:

{{#each options~}}

Option {{add @index 1}}: <Buy or Pass>

{{/each}}

{{~/user}}

Assistant

———

{{gen ”answer” temperature=0.0 max_tokens=2048}}

Listing 3: Direct quote selection.

⬇

System

——

You are employed by a company that specializes in acquiring information. You
are trying to answer a question by purchasing information from an information
market. In this market, vendors sell pieces of information at a price. Do not
exceed the budget of ${{balance}}.

User

—-

The question is ”{{question}}?”

Here are your options.

—{{#each options}}

Option {{add @index 1}}: {{this.answer_block}}

{{/each}}—

{{#each options~}}

Option {{add @index 1}} costs ${{this.price}}

{{/each}}

First, you will write your thoughts about each option, including its price and
how well the content answers the question. Then you will write a paragraph
summarizing your thoughts and making your verdict.

Your verdict must be printed as:

VERDICT:

{{#each options~}}

Option {{add @index 1}}: <Buy or Pass>

{{/each}}

Assistant

———

{{gen ”answer” temperature=0.0 max_tokens=2048}}

Listing 4: Quote selection with chain-of-thought reasoning.

⬇

System

——

You are a helpful assistant, and you excel in following instructions.

Your task is to answer a question to the best of your ability. To help you in
that task, you will be given some passages that might contain useful
information.

It is important that your answer is formulated in a simple and understandable
way.

User

—-

The question is ”{{question}}?”

Here are some passages that you might find helpful.

—{{#each quotes}}

{{add @index 1}}. {{this.answer_block}}

{{/each}}—

You’ll solve your task step-by-step.

First, you’ll start by discussing the content of all passages in the context
of the question, which is ”{{question}}”.

In particular, you will ask yourself which passages help you answer this
question and to what extent. It is possible that multiple passages help you
towards answering the question. But it is also possible that some passages are
not helpful at all, and you should ignore them. Don’t be afraid to express
uncertainty if you are unsure about something.

Next, you will formulate your answer. The answer should not have explicit
references to the passages. Instead, it should be a standalone answer to the
question.

Finally, note that it is *very important* that you enclose your answer with
<answer> and </answer> tags. If you don’t use the <answer> and </answer> tags,
I will not be able to parse it and the whole effort will be wasted.

Assistant

———

{{gen ”answer” temperature=0.0 max_tokens=1024}}

Listing 5: Answer Synthesis.

⬇

System

——

Bobby and Michael are employed at a company that specializes in acquiring and
verifying information.

Their supervisors have given them a question and an answer that their peers
have produced. Their task is to decide if the provided answer adequately
answers the question or whether things are still unclear. If the provided
answer does not conclusively answer the question, they must come up with
follow up questions that would enrich the answer. The follow up questions must
be to the-point.

Bobby wants the answer to cover enough ground to satisfy the client’s
curiosity. Michael is mindful about the risk of confusing the client by
providing information that is not relevant to the question. Together, they
must try to figure out whether the client wants a to-the-point answer or a
more elaborate answer. If the client’s question is general and warrants a more
elaborate answer, it makes more sense to ask follow-up questions. In the case
that the client’s question is specific, then the follow-up questions must only
be asked if the currently available answer is not conclusive.

Note that follow up questions should only be asked if there is a need for
concrete information that is missing from the provided answer or if the
provided answer is missing crucial details. In other words, Bobby and Michael
are not necessarily required to ask a follow up question.

User

—-

The question is: {{question}}

The currently available answer is: {{current_answer}}

Bobby and Michael will now argue about whether they should ask follow-up
questions taking in to account the provided question and the currently
available answer.

If they decide to ask follow up questions, they should be printed as:

FOLLOW-UP QUESTION: <follow up question goes here>

FOLLOW-UP QUESTION: <follow up question goes here>

… and so on.

Assistant

———

{{gen ”answer” temperature=0.0 max_tokens=1024}}

Listing 6: Generate follow-up questions.

⬇

System

\------

You are a helpful assistant, and you excel in following instructions.

In this session, you will be given a question, and an initial answer. The
initial answer was lacking in some aspects, so follow-up questions were asked
to improve the initial answer.

Your task is to refine the initial answer by incorporating the extra insights
obtained from the answers to the follow-up questions. But be mindful to only
include the insights that make the original answer better, and ignore the
rest. The refined answer should directly answer the original question.

User

\----

The original question is: {{question}}

The initial answer is: {{original_answer}}

Here are the follow-up questions that were asked, and the corresponding
answers.

\---

{{#each follow_up_questions~}}

Question {{add @index 1}}: {{this.question}}

Answer: {{this.answer}}

{{~/each}}

\---

Given these follow-up questions, your ultimate task is to refine the initial
answer.

But before you get to formulating the refined answer, please think out loud
about what you need to do. Ask yourself whether the question is general or
specific. If it is general, then you need to provide a more comprehensive
answer. If it is specific, then you need to provide a more to-the-point
answer.

After that, please summarize the answers to the follow-up question in the
context of the original answer, keeping only the information that is on-topic
and useful while ignoring the rest. Note that some questions might be off-
topic and not useful, and it’s important that you ignore these distractor
questions.

Assistant

\---------

{{gen "rationale" temperature=0.0 max_tokens=2048}}

User

\----

Now that it’s time to write down the refined answer, please do not explicitly
mention the passages, but focus on answering the question. Do not use your
world knowledge, but only the information that is available in the original
answer and the answers to relevant follow-up questions.

It’s *very important* that you enclose your answer with <answer> and </answer>
tags.

For example, if your answer is "REFINED ANSWER GOES HERE", then you should
output <answer>REFINED ANSWER GOES HERE</answer>.

If you don’t use the <answer> and </answer> tags, I will not be able to parse
it and the whole effort will be wasted.

Assistant

\---------

{{gen "answer" temperature=0.0 max_tokens=512}}

Listing 7: Answer Refinement. For this task, we found that the performance was
improved when the task was split in to two interactions.

⬇

System

——

You are an intelligent AI assistant. You will be given a question. Your task
is to answer it to the best of your ability.

User

—-

{{question}}

Assistant

———

{{gen ”answer” temperature=0.0 max_tokens=512}}

Listing 8: Closed-book question answering.

⬇

Bobby: I think we should definitely consider Option 1. It’s the cheapest and
it provides a lot of references to studies that have been done on in-context
learning. It even mentions a study that shows in-context learning can occur
despite a mismatch with the prompt distribution.

Michael: I agree that Option 1 provides a lot of useful information, but it’s
also quite dense. We might end up spending a lot of time trying to understand
all the references. Plus, it doesn’t directly answer our question about
distribution mismatch between prompts and pretraining data.

Bobby: That’s true, but it does give us a lot of leads to follow. What about
Option 2? It’s more expensive, but it seems to provide a more direct answer to
our question.

Michael: Option 2 does seem to provide more direct information, but it’s also
focused on a specific model and language. I’m not sure if the findings would
be applicable to our question.

Bobby: You have a point. But it does mention that in-context learning
performance heavily depends on the corpus domain source, which could be
relevant to our question.

Michael: True, but for the price, I’m not sure it’s worth it. Option 3 is also
the same price, but it seems to provide less information than the other two
options.

Bobby: I agree. Option 3 seems to be more of an introduction to the topic
rather than providing specific insights.

Michael: So, considering our budget and the information provided, I think we
should go for Option 1. It’s the cheapest and provides a lot of potential
leads for us to follow.

Bobby: I agree. We might have to do some extra work to understand all the
references, but I think it’s worth it for the price.

VERDICT:

Option 1: Buy

Option 2: Pass

Option 3: Pass

Listing 9: An example quote selection debate. Michael and Bobby debate about
which quotes to accept.

⬇

What is the name of the architecture that has dominated natural language
processing (NLP) benchmarks in recent years?

\--------------------------------------------------

Answer A:

The architecture that has dominated natural language processing (NLP)
benchmarks in recent years is the Generative Pre-trained Transformer (GPT)
series models, including ChatGPT, due to their ability to generate
instructions and follow them, unique architecture, and training, few-shot
learning, and adaptability to new tasks, and fine-tuning. These features have
allowed GPT models to adapt to various NLP tasks, such as challenging math
problems or symbolic manipulation problems, NLP classification tasks, speech
and vision tasks, and other tasks that benefit from the use of large language
models. GPT models have shown particular strengths in tasks that require an
understanding of human language and the ability to generate code, such as code
generation, API management, and sensor orchestration. They have also excelled
in various natural language processing tasks such as text generation,
translation, summarization, and question answering.

\--------------------------------------------------

Answer B:

The architecture that has dominated natural language processing (NLP)
benchmarks in recent years is the Transformer architecture. This is inferred
from the fact that many of the mentioned models, such as BERT, GPT, RoBERTa,
BART, and T5, are based on the Transformer architecture.

==================================================

Michael: Bobby, I think student A’s answer is better. It’s more detailed and
provides a broader perspective on the use of BERT and GPT models.

Bobby: I disagree, Michael. Student A’s answer is not entirely accurate. They
mention that BERT and GPT are used for predicting legal verdicts, which is not
their primary function. They are language models used in natural language
processing tasks.

Michael: That’s true, Bobby. But student A also talks about the differences in
the outputs of BERT and GPT models, which is a valid point.

Bobby: Yes, but student B’s answer is more aligned with the gold standard
answer. They correctly mention the pretraining strategies of BERT and GPT, and
how these strategies make them suitable for different tasks.

Michael: I see your point, Bobby. Student B’s answer is indeed more accurate
and directly addresses the question. They also mention the use of attention-
based Transformers and self-supervised learning, which is a crucial aspect of
these models.

Bobby: Exactly, Michael. While student A’s answer provides some interesting
insights, it deviates from the question and includes some inaccuracies.
Student B’s answer, on the other hand, is concise, accurate, and directly
addresses the question.

VERDICT: Better answer from student B.

Listing 10: An example evaluation debate. Michael and Bobby debate about which
answer of the two answers is better. In this example, they decide to pick the
more focused of the two answers.

⬇

What is the difference between a BERT and a GPT?

\--------------------------------------------------

Answer A:

BERT and GPT are both language models used for predicting legal verdicts, but
they differ in their approach to decision-making. BERT evaluates violations of
the European Convention of Human Rights by considering privacy rights and
potential biases using its masked language modeling approach. In contrast, GPT
models use their next token prediction approach to evaluate the severity of a
violation. GPT models are ranked based on their performance on specific tasks,
size, and quality, and privacy-preserving techniques can be applied to protect
training data exposure to adversaries. The outputs of BERT and GPT models
differ regarding randomness and correctness, with BERT models generating more
accurate and contextually relevant responses, while GPT models generate more
diverse and creative responses. The choice of which model to use depends on
the specific task requirements and the desired trade-offs between accuracy,
fluency, and creativity.

\--------------------------------------------------

Answer B:

BERT and GPT are both popular language models that use attention-based
Transformers and self-supervised learning. However, they differ in their
pretraining strategies. BERT uses a method called masked modeling, where it
predicts masked language tokens from unmasked ones. This allows BERT to
utilize bidirectional text information, making it suitable for tasks like
sentiment analysis. On the other hand, GPT uses autoregressive modeling, which
makes it perform well in few-shot or zero-shot text generation tasks. Despite
their differences, both models can be fine-tuned for various tasks and show
competitive performance.

==================================================

Michael: Bobby, I think student A’s answer is better. It’s more detailed and
provides a broader perspective on the use of BERT and GPT models.

Bobby: I disagree, Michael. Student A’s answer is not entirely accurate. They
mention that BERT and GPT are used for predicting legal verdicts, which is not
their primary function. They are language models used in natural language
processing tasks.

Michael: That’s true, Bobby. But student A also talks about the differences in
the outputs of BERT and GPT models, which is a valid point.

Bobby: Yes, but student B’s answer is more aligned with the gold standard
answer. They correctly mention the pretraining strategies of BERT and GPT, and
how these strategies make them suitable for different tasks.

Michael: I see your point, Bobby. Student B’s answer is indeed more accurate
and directly addresses the question. They also mention the use of attention-
based Transformers and self-supervised learning, which is a crucial aspect of
these models.

Bobby: Exactly, Michael. While student A’s answer provides some interesting
insights, it deviates from the question and includes some inaccuracies.
Student B’s answer, on the other hand, is concise, accurate, and directly
addresses the question.

VERDICT: Better answer from student B.

Listing 11: An example evaluation debate. Michael and Bobby debate about which
answer of the two answers is better. In this example, they pick the factually
relevant answer.