# Persona-DB: Efficient Large Language Model Personalization for Response
Prediction with Collaborative Data Refinement

Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi R. Fung
Hou Pong Chan, ChengXiang Zhai, Heng Ji
University of Illinois Urbana-Champaign
{chenkai5, czhai<EMAIL_ADDRESS>

###### Abstract

The increasing demand for personalized interactions with large language models
(LLMs) calls for the development of methodologies capable of accurately and
efficiently identifying user opinions and preferences. Retrieval augmentation
emerges as an effective strategy, as it can accommodate a vast number of users
without the costs from fine-tuning. Existing research, however, has largely
focused on enhancing the retrieval stage and devoted limited exploration
toward optimizing the representation of the database, a crucial aspect for
tasks such as personalization. In this work, we examine the problem from a
novel angle, focusing on how data can be better represented for more efficient
retrieval in the context of LLM customization. To tackle this challenge, we
introduce Persona-DB, a simple yet effective framework consisting of a
hierarchical construction process to improve generalization across task
contexts and collaborative refinement to effectively bridge knowledge gaps
among users. In the task of response forecasting, Persona-DB demonstrates
superior efficiency in maintaining accuracy with a significantly reduced
retrieval size, a critical advantage in scenarios with extensive histories or
limited context windows. Our experiments also indicate a marked improvement of
over 15% under cold-start scenarios, when users have extremely sparse data.
Furthermore, our analysis reveals the increasing importance of collaborative
knowledge as the retrieval capacity expands.

Persona-DB: Efficient Large Language Model Personalization for Response
Prediction with Collaborative Data Refinement

Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi R. Fung Hou Pong Chan,
ChengXiang Zhai, Heng Ji University of Illinois Urbana-Champaign {chenkai5,
czhai<EMAIL_ADDRESS>

##  1 Introduction

The increasing demand for artificial intelligence-based services that can
accurately predict and adapt to individual user preferences underscores the
importance of personalization in today’s digital landscape. Personalization
enhances user experience, fostering trust and engagement beyond mere
convenience. This need is particularly pronounced in applications involving
large language models (LLMs), which serve as the foundation for a wide range
of services, from personalized content moderation and medical assistance to
educational platforms Chen et al. (2023a); Baek et al. (2023).

In this work, we aim to improve LLM personalization, the model’s capability of
customizing responses based on user background. For instance, understanding a
user’s penchant for concise versus detailed explanations can tailor the
verbosity of responses, while knowledge of their professional domain can
refine the relevance of examples provided. Specifically, we explore from the
perspective of retrieval-augmentation Lewis et al. (2020); Zhang et al.
(2023), which has the distinct advantage of mitigating the need for the
computational burdens associated with training personalized models for a
multitude of users and the magnitude of LLMs. In the retrieval-augmentation
approach, relevant user-related data logs, such as historical social media
posts and user descriptions, are fetched by a retriever. This information is
then integrated into the instruction prompts for downstream LLMs to
personalize responses.

While resource-efficient, the efficacy of the method hinges on the quality of
the underlying database. Current solutions for retrieval augmentation,
however, have limited exploration in the facet and often depend largely on
expansive yet shallow logs of user interactions and summaries, which can
prevent the model from achieving optimal performance. Firstly, such an
approach would make it difficult to capture the depth of user information, a
vital component in the model performance in answering queries. Secondly, the
approach discourages efficiency, since it needs to gather scattered
information pieces to provide sufficient pertinent data for retrieval
augmentation. For instance, a higher-level opinion, such as an attitude toward
a political party, can often be a more broadly applicable and retrieval-
efficient insight compared to an opinion about a specific entity within the
party, where the former requires analyzing historical behaviors. Moreover,
existing methods have yet to explore the potential synergies that could arise
from intelligently leveraging the interconnectedness of different users’ data
to fill knowledge gaps.

Our paper delves into the critical research question: How do we address data
efficiency in retrieval-augmented personalization? In other words, considering
the cost of model inference, how do we maintain the accuracy of the generated
response by retrieving less? To address previous gaps, we propose a solution
based on the premise that the construction of more structured, self-improved,
and interrelated databases can enhance the LLM’s ability to retrieve relevant
information and to generalize. This leads us to the introduction of our
framework Persona-DB, designed to encapsulate extensive user contexts and
histories, fostering more accurate and individualized interactions.

The framework is underpinned by two integral components designed to enhance
user databases for more generalizable inference. The first component involves
a simple yet effective stage that involves distillation and induction on the
user database to encapsulate and extend user personas such as opinions and
preferences. The hierarchical construction process enables the application of
learned insights across diverse task contexts. The second component involves
interconnecting relevant databases among users, drawing inspiration from the
principles of collaborative filtering in recommendation systems. Recognizing
that an individual’s data may sometimes be insufficient to address the query
(e.g., due to sparse data or domain-specific gaps), we facilitate the
amalgamation of other users’ data that may contain relevant interactions,
based on the previous studies that users with analogous mindsets tend to
exhibit similar behaviors and preferences Reimer et al. (2022); Torelli and
Kaikati (2009). For instance, a user who participates in outdoor activities
and values environmental sustainability, yet has only a few interactions
related to renewable energy projects, can be matched with users of a similar
environmental ethos who have engaged in discussions about renewable energy.
This assists us in inferring the original user’s viewpoints on solar energy
initiatives, leveraging shared values to supplement the absence of
interactions in that domain. In our work, we specifically achieve this by
matching users based on shared persona keys identified in the first component
and composing information pieces from the individual’s database and the
collaborative databases. Our approach aims to make databases both more precise
and resource-efficient for downstream LLM usage, reducing the need to retrieve
extensive data.

We evaluate the method on the task of personalized response forecasting, where
the downstream LLM is asked to predict users’ responses to news media. Our
experimental evaluation reveals that Persona-DB consistently outperforms the
baselines across various retrieval sizes, notably achieving better results
when the maximal retrieval size is 10 times smaller than that of the baseline.
The results indicate that our framework can maintain accuracy while requiring
substantially less input data, which is particularly advantageous when dealing
with extensive user histories or limited context windows in LLMs. In scenarios
where the user has starkly scarce data, our framework outperforms the baseline
by a substantial margin (e.g., 15% in the correlation metric), demonstrating
the effectiveness of our approach to generalize under the case of new or
infrequent users. In our analysis, we further explore the impact of knowledge
contributed by collaborator databases, finding that its importance to overall
performance escalates as the retrieval capacity grows.

Figure 1: The image outlines the Persona-DB workflow, which starts by
distilling and inducing abstract personas from users’ interaction histories.
It then leverages the cache layer to facilitate the joining of relevant user
databases, effectively borrowing knowledge to fill contextual gaps in the
primary user’s data. This enriched data pool is subsequently used by the
retrieval for personalized inference.

##  2 Persona-DB

The primary challenge in enhancing retrieval augmentation for personalized LLM
interactions is the effective and accurate representation of user context
(i.e., the interaction data that inform the LLM’s personalization process)
within the constraints of LLM context windows and amidst the vast diversity of
user behaviors. To effectively represent personas, it is crucial to balance
comprehensiveness and efficiency. We aim to encapsulate detailed user
information, such as experiences, and opinions, while ensuring that the data
is efficiently retrievable. In our context, we define personalization
efficiency in terms of information density and predictive power. For instance,
a personalization-efficient database will be able to maintain a downstream
model with fewer retrieval items. Despite advancements in retrievers and
encoders, existing methods, which predominantly rely on user logs and
summaries, do not fully leverage the predictive capabilities of user personas
or facilitate the exploration of relations between users. Recognizing the
limitations, we introduce Persona-DB, a novel framework designed to address
key challenges in efficient user context representation. Our approach is
guided by two key intuitions. Firstly, high-level (or abstract) user personas,
which have been demonstrated by previous social psychology works Reimer et al.
(2022); Torelli and Kaikati (2009) to effectively predict human actions across
different social contexts, can provide a more generalizable basis for persona
representation. Secondly, inspired by collaborative filtering prevalent in
recommendation systems, we explore the potential of applying similar
principles in the context of LLM personalization. Specifically, our framework
consists of two components, (1) the creation of a database by distilling and
extending user data into more generalizable persona constructs and (2) a JOIN
operation to bridge knowledge gaps between users by using keys identified in
the previous stage. The workflow is depicted in Figure 1.

###  2.1 Hierarchical Refinement

User histories, which capture a user’s past behaviors, are often noisy, with
key information dispersed and sometimes only inferable through sequential
analysis. Given the scenario where an LLM (or Agent) can only use a limited
amount of information due to context limitations, it becomes crucial to
appropriately transform the database for more efficient downstream usage. One
natural method for such transformation is creating summaries of events, yet it
discourages generalization at some level. For instance, a person’s nature and
beliefs (e.g., being supportive of an initiative) can typically be more
generalizable across contexts than a reaction under a specific scenario (e.g.,
positive attitude toward a particular entity within the initiative). From
previous works in social psychology, it was suggested that abstract personas
such as values are generalizable to predicting future user actions under
different social contexts Reimer et al. (2022). This indicates that extracting
higher-level variables from user data may introduce additional benefits to
personalization accuracy while requiring lighter retrieval. To achieve this
step, we choose to use an instruction-tuned LLM Wei et al. (2021); Chung et
al. (2022) to structure and infer user facts and opinions to automatically
enhance the database. In our experiment, we use the same LLM for both
hierarchical refinement and downstream prediction.

Our design schema is a hierarchy consisting of History, Distilled Persona,
Induced Persona, and Cache, where an example snapshot is shown in Figure 1.
The History layer contains the original user records. On the other hand, the
Distilled Persona (abbreviated DP) is an induced dictionary of persona-related
facts from History, such as superficial opinions and pattern analysis. The
Induced Persona (abbreviated IP) layer comprises inferred higher-level
information from the previous two components, offering a more abstract view of
the user. For instance, DP can include specific observations, such as the
user’s criticism of a governmental decision to reduce public spending on
community services, while IP can include conjectures such as that the user has
a broad concern regarding social justice, derived from observations in DP.
Lastly, Cache is a subset of IP that contains consistent human-defined high-
level persona categories to assist relevancy matching in the collaboration
stage of the framework. We utilize an LLM as an analyzer to infer and enrich
the database with both user facts and opinions. In particular, each layer is
populated by feeding the information from the lower layers to the LLM for
processing. The structure aims to create more packed user-level features to
assist retrieval efficiency. By being able to utilize high-level,
generalizable personas, the downstream model can apply these insights across a
broad range of contexts, enhancing the model’s ability to personalize
interactions.

###  2.2 Collaborative Refinement

While using a user’s data to predict intention is straightforward, it can
introduce significant challenges when the user’s interactions are irrelevant
to a domain presented in the query or when the user simply does not have many
interactions. In fact, it was discovered previously that only 25% of highly
active users generate 97% of the content on Twitter McClain et al. (2021),
indicating that such a case is dominant in the social media context. To
address the issues, we introduce a stage to interconnect knowledge between
users, under the assumption that users with similar mindsets would make
similar decisions Reimer et al. (2022). By enabling a user to retrieve and
integrate information from relevant users, we establish a collaborative
database. This enriches the user context by the inclusion of potentially more
domain-matching content from collaborators, thereby enhancing the
generalizability and relevance of the user context representation. As
demonstrated in our experiment results, such an approach indeed brings
enormous benefits in cold-start situations when the user has scarce data on
the platform. Our research is also the first to probe the application of this
collaborative filtering-inspired method in the relatively new domain of LLM
personalization, especially in scenarios where a social network or product
graph is not readily available.

Figure 2: During the retrieval-augmentation stage, the retriever selects data
from the user’s and the collaborative databases and composes them at a set
ratio to inform the LLM. This strategy aims to enable the model to address
challenges like sparse user interactions (e.g., cold-start) and domain
irrelevance, offering an effective approach to LLM personalization in
environments lacking user graphs.

In our design, we name this procedure a JOIN stage as an analogy to the JOIN
clause in SQL. Formally, let $\text{DB}_{i}$ be the persona database for user
$i$, let $\text{DB}_{c}$ be the database for the current user, and let
$\mathrm{P}_{i}$ indicate retrieval-based embedding prompt for user $i$. We
utilize instruction-based embedding for matching in order to help the model
understand the intents behind the query instead of merely comparing semantic
similarity. Specifically, the process involves first encoding the Cache of
each user’s persona database using a general domain instruction-tuned
$\text{LLM}_{\text{embed}}$ that has been trained on large-scale text
similarity data Su et al. (2022); Asai et al. (2022),

$\mathbf{X}_{i}=\text{LLM}_{\text{embed}}(\mathrm{P}_{i},\text{DB}_{i}\texttt{[Cache]})\quad\forall
i\in|U|$ (1)

We then use the embedded as the key to compute similarities between the
current user’s database and others, followed by retrieval and joining of the
most relevant databases. Formally,

$\psi(\mathbf{X}_{i},\mathbf{X}_{c})=\frac{\mathbf{X}_{c}\cdot\mathbf{X}_{i}}{\|\mathbf{X}_{c}\|\|\mathbf{X}_{i}\|}$
(2)
$\text{DB}^{c}_{\texttt{Join}}=\operatorname*{\scalerel*{\|}{\sum}}_{i\in\text{TopK}(\psi)}\text{DB}_{i}$
(3)

where $\text{DB}^{c}_{\texttt{Join}}$ is the resulting collaborative database.
During the retrieval stage, we use $\text{LLM}_{\text{embed}}$ (with a
different prompt) to retrieve task-relevant information from both the database
from collaborating users and the current user. We show an example of this
procedure in Figure 2. In the process of amalgamating information from the two
distinct databases, we define a composition ratio denoted by $x$. This ratio
indicates the allocation of the downstream retrieval capacity, $r$, such that
$\lceil x\cdot r\rceil$ represents the proportion of data selected from the
collaborative database, $\text{DB}^{c}_{\texttt{Join}}$, while the remaining
capacity, $r-\lceil x\cdot r\rceil$, is sourced from the individual user
database, $\text{DB}_{c}$. Subsequently, aggregated items are integrated into
a prompt, which is then fed to a downstream LLM to produce personalized
responses relevant to the user’s query.

|  $\phi_{int}$ (%) |  $\phi_{p}$ (%)
---|---|---
Method | $r_{s}$ | $r$ | MiF1 | MaF1
Majority | - | - | 43.41 | 20.18
Random | 0.62 | 0.41 | 35.51 | 30.55
H-Retrieval | 40.96 | 41.17 | 58.52 | 47.36
H-Recency | 40.7 | 41.49 | 58.71 | 47.49
History (Full) | 42.8 | 43.09 | 59.58 | 48.8
Persona-DB | 47.67 | 47.88 | 62.66 | 50.59
w/o JOIN | 44.66 | 45.0 | 61.79 | 50.39
Table 1: The table displays response prediction results, highlighting the best
overall performance in bold. Our framework (with top-40 retrieval)
consistently surpasses baseline methods, notably achieving higher accuracy
than the method using full user history databases. Additionally, the results
include an ablation study demonstrating the standalone effectiveness of
Persona-DB without the JOIN mechanism.

##  3 Experiment

###  3.1 Dataset Overview

To achieve personalization, a model needs to be capable of predicting users’
opinions and preferences regarding a wide range of potential responses or
products. In this work, we utilize the dataset from Sun et al. (2023) (denoted
as RFPN) to evaluate the model’s performance in personalized response
forecasting. The dataset comprises 13.3k responses from 8.4k users to 3.8k
news headlines sourced from Twitter. We utilize the dataset’s test set, in
which the history length can exceed 300. We evaluate the model performance on
predicting the sentiment polarity $\phi_{p}$ (categorized as Positive,
Negative, or Neutral) and intensity $\phi_{int}$ (on a scale from 0 to 3),
based on a given persona and news media message. Here, a persona is
constructed from user attributes, including profiles and historical posts.
Detailed statistics of the dataset are provided in the Appendix. The
prediction task allows for reliable evaluation and analysis of how messages
are perceived by different personas or social segments, simplifying the
challenge of assessing text generation in personalized conversation with
lengthy user contexts, which can be challenging for both LLM and Human
Evaluation.

###  3.2 Experimental Setup

The evaluation uses Spearman and Pearson correlation coefficients, denoted as
$r_{s}$ and $r$, respectively, to assess sentiment intensity predictions. The
sentiment polarity classification is evaluated using Micro-F1 and Macro-F1
scores (denoted MiF1 and MaF1), which measure accuracy in multi-class
scenarios. To test the efficacy of the Persona-DB components, we compare our
framework against different variants that use the original user historical
data as the database, including H-Retrieval, where the most relevant entries
are used, and H-Recency, which uses the most recent entries. Additionally, we
evaluate the performance of Persona-DB without collaborative refinement in a
variant named Persona-DB w/o JOIN (abbreviated P-DB w/o J). For the
experiments in 3.3 and 3.4, we use 40 as the retrieval capacity. We also
include naive strategies including Random and Majority, where the former makes
predictions randomly, and the latter follows the majority label. A reference
to using full history, History (Full), offers insight into the performance of
complete history utilization. To accommodate the context window limitation
caused by an excessively large number of interactions, we limit the number of
items to within the range that LLM can accept. The retrieval process utilizes
an instruction-tuned LLM encoder, text-embedding-
ada-002111https://platform.openai.com/docs/models/embeddings, to facilitate
both the collaborative user retrieval and the downstream retrieval
augmentation. Inference is performed using
gpt-3.5-turbo-0613222https://platform.openai.com/docs/models/gpt-3-5-turbo via
the official OpenAPI, with a fixed seed across runs. The same model is also
utilized in the first stage of our framework to process user histories.

Our experiments were conducted using the PyTorch framework Paszke et al.
(2019) and the Huggingface Transformers library Wolf et al. (2020). The
sentiment intensity labels adhere to the definition in SemEval-2018 Task 1
Mohammad et al. (2018), incorporating both magnitude and direction in the
evaluation of sentiment intensity. More details can be found in the Appendix.

###  3.3 General Case Results Discussion

We evaluate the effectiveness of the Persona-DB framework and the baselines,
setting the top-k as 40 for both our variants and the baselines in the
retrieval-augmentation step. From the table, it is evident that H-Recency and
H-Retrieval underperform compared to History (Full), as expected, due to their
less comprehensive view of the user’s information. On the other hand, Persona-
DB consistently outperforms these retrieval-based methods across all
evaluation metrics with a large margin in correlation, demonstrating that our
framework can achieve superior performance under the same retrieval capacity,
compared to using historical posts only. The table also indicates that while
leveraging a user’s complete history offers a broad view of user data during
inference, our approach using only the top-k personas, in fact, exceeds the
performance of History (Full) consistently. This suggests that our strategy of
self-improving and composing interrelated personas achieves better
personalization efficiency as it uses much less data while achieving better
results.

To underscore the significance of the collaborative stage within our framework
further, we examine their absence’s effect on performance. Omitting JOIN
operations results in a marked decrease in correlation metrics and F1 scores,
affirming its essential role in addressing knowledge gaps among users.
Interestingly, even without implementing JOIN, the hierarchical database
exhibits greater predictive capability than that of the baselines using a log-
only database. This observation reiterates the value of high-level insights in
improving generalizability in the downstream models. While the hierarchical
construction is not perfect due to the use of LLM, it still allows both
components of our framework to work. This indicates that the method is robust
against error propagation at some level, and further enhancements to the LLM
to generate higher-quality insights may yield improved performance for
Persona-DB.

In short, the results reveal that the Persona-DB framework, with its
hierarchical persona database and collaborative refinement process, offers a
more efficient and accurate solution for personalized LLM interactions. The
critical role of the JOIN operation also shows the importance of developing
methods that consider user relations for LLM personalization.

|  $\phi_{int}$ (%) |  $\phi_{p}$ (%)
---|---|---
Method | $r_{s}$ | $r$ | MiF1 | MaF1
Case Study: Lurkers
History (Full) | 40.66 | 41.62 | 67.96 | 46.02
P-DB w/o J | 47.07 | 53.34 | 73.79 | 49.79
Persona-DB | 55.05 | 56.75 | 77.67 | 57.29
Case Study: Frequent Users
H-Retrieval | 30.63 | 33.34 | 56.08 | 42.02
H-Recency | 38.74 | 40.95 | 58.75 | 46.21
History (Full) | 43.29 | 45.07 | 61.72 | 48.63
P-DB w/o J | 41.41 | 44.44 | 63.5 | 49.67
Persona-DB | 49.03 | 49.71 | 65.58 | 52.72
Table 2: The two case studies, focusing on users with extremely scarce
(Lurkers) and long (Frequent Users) data histories, demonstrate that our
framework markedly improves performance for both ends of the spectrum. These
results underscore the effectiveness of our methods in cold-start scenarios,
and the ability to process and personalize content for users with extensive
histories.

###  3.4 Results on Lurkers and Frequent Users

In our evaluation, we delve into the evaluation of Persona-DB across two types
of common yet challenging cases: Lurkers and Frequent Users. This segmentation
allows us to understand the framework’s robustness and performance in
scenarios representing the extremes of user interaction volumes.

Lurkers The term "Lurker" refers to users with minimal interaction history, a
common scenario in recommendation systems known as the cold-start problem.
Being capable of personalizing responses for lurkers indicates a framework’s
ability to offer tailored interactions to new users without the prerequisite
of extensive historical accumulation. We select 100 users with the sparsest
(nonempty) interaction records, averaging only 13.81 interactions per user,
for this study. The results, as illustrated in Table 2, highlight Persona-DB’s
superior performance across all metrics compared to the baselines.
Specifically, our framework shows a 15% improvement in Pearson correlation
over the baseline method, showcasing its exceptional ability to maintain
accuracy with minimal user data. This efficiency can be attributed to the
framework’s capability to discern user characteristics from limited
interactions, leveraging reliable, frequent collaborators’ insights to enhance
personalization accuracy. Remarkably, even before applying the JOIN operation,
the transformed database exhibits noticeably improved performance,
underscoring the predictive power of LLMs in identifying generalizable
personas.

Frequent Users Furthermore, we assess the framework’s performance with
frequent users, characterized by voluminous interaction histories, potentially
introducing noise to the retrieval process. For this analysis, we focused on
the top 300 users with the longest histories. The evaluation is detailed in
Table 2. Interestingly, the performance of H-Retrieval is notably lower than
that of the recency-based method. One hypothesis for this observation is that
when the user history becomes frequent, the retrieval of relevant information
in a small capacity becomes harder as there are more semantically similar yet
non-relevant items. The table also demonstrates Persona-DB’s consistent
outperformance over alternative methods. This finding emphasizes the
framework’s adeptness at tailoring content for users with extensive
interaction histories. The success can be largely attributable to the
hierarchical representation’s ability to synthesize and distill relevant
information from a broad dataset, effectively mitigating noise and enhancing
personalization accuracy for both frequent users and their collaborators.

The conducted analyses on both lurkers and frequent users highlight the
versatility and robustness of the Persona-DB framework in delivering
personalized LLM interactions. By handling the challenges posed by both sparse
and extensive user interaction histories, our approach demonstrates a clear
improvement in LLM personalization over the baselines. The effectiveness of
collaborative persona databases across different scenarios shows the potential
for practical applicability in real-world contexts.

Figure 3: Both variants of our framework are capable of maintaining
performance above that of full history, even when the retrieval size is
significantly reduced.

###  3.5 Personalization Efficiency Analysis

To minimize resource consumption in the inference stage, it is crucial to
improve data efficiency in retrieval (i.e., the retrieved set remains
generalizable while being reduced in size). This section delves deeper into
this perspective by illustrating how our framework’s performance varies with
different retrieval sizes.

Figure 3 demonstrates the robustness of our method, illustrating that accuracy
is maintained even as the retrieval cap is significantly reduced. This
underscores the method’s resilience to variations in context window size for
LLMs, highlighting its capability to deliver precise predictions with minimal
data retrieval. The compelling performance, even with a constrained database,
supports our hypothesis that the personas derived from our hierarchical
construction possess enhanced predictive power.

While larger retrieval capacity generally corresponds to improved performance,
likely owing to the richer information context, certain instances reveal a
slight decrease in metrics. This phenomenon aligns with existing researchs
Wang et al. (2023) suggesting that overly extensive contexts can, at times,
introduce more noise than they mitigate. This finding is intriguing as it
shows there may exist an optimal retrieval threshold depending on the query,
beyond which the additional data may become counterproductive. Moreover, we
can see that as the retrieval size decreases, the advantage of using
collaborative entries shrinks. This trend likely stems from the increased
significance of a user’s own data in situations where retrieval capacity is
limited.

###  3.6 The Impact of Collaborative Composition

We further analyze how the proportion of collaborative items influences
Pearson correlation performance across different retrieval capacities. The
heatmap depicted in Figure 4 reveals an interesting trend: for larger
retrieval sizes, incorporating a higher percentage of data from collaborative
databases results in improved performance. Conversely, for smaller retrieval
sizes, an increased reliance on collaborative data sometimes detrimentally
affects performance. This pattern can be attributed to the reason that
typically, a user’s database contains only a limited number of entries
directly relevant to addressing a query effectively. Once the retrieval size
surpasses this small pool of pertinent data, the inclusion of novel, domain-
relevant insights from collaborative databases becomes increasingly
beneficial. However, in situations with extremely limited retrieval capacity,
the critical, predictive nature of the user’s own data takes precedence. In
these instances, over-reliance on external data sources can, therefore,
undermine performance. The pattern underscores the importance of maintaining a
strategic balance in the composition of the retrieval set to optimize LLM
personalization.

Figure 4: The figure illustrates the shift of correlation performance metric
as capacity and proportion of collaborative content changes. The trends show
that collaborative retrieval becomes more important as the retrieval size
grows.

##  4 Related Work

Personalization plays a crucial role in the effectiveness of large language
models (LLMs), particularly in ensuring their alignment with individual
preferences and interests Hwang et al. (2023); Kirk et al. (2023); Mao et al.
(2023); Han et al. (2023). With the advent of LLMs, the landscape of
personalization has also been significantly transformed, enabling enhanced
user engagement and the delivery of services tailored to individual needs Chen
et al. (2023b) and cultural preferences Li et al. (2023); Fung et al. (2023,
2024). Traditional personalization approaches for language models often use
auxiliary encoders to learn user representation from user profile data King
and Cook (2020); Qiu et al. (2021); Lian et al. (2022). For instance, to
personalize pre-trained language models, UserIdenitifer Mireshghallah et al.
(2022) prepends a user identifier prefix at the beginning of the input text
during the fine-tuning process. Works have also been done to utilize tensor
factorization during training to tackle data sparsity problems Wang et al.
(2021). Yet such works would require training resources and can be challenging
in situations of extensive user data and large models. Recently, there have
been works focusing on utilizing and improving retrieval augmentation due to
its capability to efficiently borrow external knowledge lacking in the pre-
trained data Wang and McAllester (2020); Lewis et al. (2020) without the cost
of fine-tuning downstream LLMs. In Zhang et al. (2023) and Asai et al. (2022),
the authors introduced new embedding models to better support the diverse
retrieval augmentation needs. Furthermore, Srinivasan et al. (2022) introduced
a practical approach for distilling retrieval-augmented LLMs, and Guu et al.
(2020) demonstrated the effectiveness of retrieval-augmented language model
pre-training for open-domain question answering. Yet limited exploration has
been done on optimizing the data to be retrieved to improve personalization.
In our work, we are the first to introduce a collaborative-style strategy to
enhance data efficiency in personalized retrieval for LLMs.

##  5 Conclusions and Future Work

In conclusion, we introduce Persona-DB, a framework designed to enhance the
efficiency and accuracy of retrieval-based LLM personalization through
collaborative data refinement. The framework leverages a hierarchical approach
to user data representation, enabling the construction of insights that are
generalizable across task contexts, and introduces a collaborative mechanism
to effectively bridge communal knowledge for improved query response when the
user lacks sufficient relevant data. Our experimental results indicate a
marked improvement in performance, particularly in scenarios characterized by
sparse user data, thus addressing a critical challenge in LLM personalization.
Furthermore, we perform analysis to study the impact of data composition on
retrieval effectiveness. Future work will focus on developing methods that
refine the framework’s capability to dynamically adapt the matching process
based on user feedback and interaction patterns.

## Limitations

Our user retrieval process currently fetches cached personas from the highest
and the most concise level of the hierarchy. However, conciseness implies a
trade-off between importance and information richness. Hence, the process can
be enhanced by selecting personas from different granularity levels depending
on the task domains. For instance, we can leverage both users’ values and
social roles, and slightly lengthier but more informative induced personas
such as patterns analysis and opinions. Furthermore, we employ LLMs for
abstracting personas in our work. However, just as data annotators may make
errors, LLMs inevitably may overlook critical information or hallucinate
incorrect viewpoints. We believe that this shortfall can be addressed through
continuous improvement of the LLM’s capabilities.

## Ethics

There are no ethics-related concerns in our paper. The dataset and resources
come from publicly available repositories. Our framework is designed to
enhance personalization efficiency, assisting humans in finding the most
suitable content and information. Moreover, the effectiveness of our method on
the news response prediction task shows its potential for improving
personalized content moderation and contributing to the creation of a safer
online environment.

## Acknowledgement

This research is based upon work supported in part by U.S. DARPA INCAS Program
No. HR001121C0165. The views and conclusions contained herein are those of the
authors and should not be interpreted as necessarily representing the official
policies, either expressed or implied, of DARPA, or the U.S. Government. The
U.S. Government is authorized to reproduce and distribute reprints for
governmental purposes notwithstanding any copyright annotation therein. We
would also like to thank Kevin Small for his helpful suggestions.

## References

  * Asai et al. (2022) Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, and Wen-tau Yih. 2022.  Task-aware retrieval with instructions.  _arXiv preprint arXiv:2211.09260_.
  * Baek et al. (2023) Jinheon Baek, Nirupama Chandrasekaran, Silviu Cucerzan, Sujay Kumar Jauhar, et al. 2023.  Knowledge-augmented large language models for personalized contextual query suggestion.  _arXiv preprint arXiv:2311.06318_.
  * Chen et al. (2023a) Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu, Qi Liu, Gangwei Jiang, Yuanhao Pu, Yuxuan Lei, Xiaolong Chen, Xingmei Wang, et al. 2023a.  When large language models meet personalization: Perspectives of challenges and opportunities.  _arXiv preprint arXiv:2307.16376_.
  * Chen et al. (2023b) Zheng Chen, Ziyan Jiang, Fan Yang, Zhankui He, Yupeng Hou, Eunah Cho, Julian McAuley, Aram Galstyan, Xiaohua Hu, and Jie Yang. 2023b.  The first workshop on personalized generative ai@ cikm 2023: Personalization meets large language models.  In _Proceedings of the 32nd ACM International Conference on Information and Knowledge Management_ , pages 5267–5270.
  * Chung et al. (2022) Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022.  Scaling instruction-finetuned language models.  _arXiv preprint arXiv:2210.11416_.
  * Fung et al. (2023) Yi Fung, Tuhin Chakrabarty, Hao Guo, Owen Rambow, Smaranda Muresan, and Heng Ji. 2023.  NORMSAGE: Multi-lingual multi-cultural norm discovery from conversations on-the-fly.  In _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing_ , pages 15217–15230, Singapore. Association for Computational Linguistics.
  * Fung et al. (2024) Yi Fung, Ruining Zhao, Jae Doo, Chenkai Sun, and Heng Ji. 2024.  Massively multi-cultural knowledge acquisition & lm benchmarking.
  * Guu et al. (2020) Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020.  Retrieval augmented language model pre-training.  In _International conference on machine learning_ , pages 3929–3938. PMLR.
  * Han et al. (2023) Chi Han, Jialiang Xu, Manling Li, Yi Fung, Chenkai Sun, Nan Jiang, Tarek Abdelzaher, and Heng Ji. 2023.  Lm-switch: Lightweight language model conditioning in word embedding space.  _arXiv preprint arXiv:2305.12798_.
  * Hwang et al. (2023) EunJeong Hwang, Bodhisattwa Prasad Majumder, and Niket Tandon. 2023.  Aligning language models to user opinions.  _CoRR_ , abs/2305.14929.
  * King and Cook (2020) Milton King and Paul Cook. 2020.  Evaluating approaches to personalizing language models.  In _Proceedings of the Twelfth Language Resources and Evaluation Conference_ , pages 2461–2469.
  * Kirk et al. (2023) Hannah Rose Kirk, Bertie Vidgen, Paul Röttger, and Scott A Hale. 2023.  Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback.  _arXiv preprint arXiv:2303.05453_.
  * Lewis et al. (2020) Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020.  Retrieval-augmented generation for knowledge-intensive nlp tasks.  _Advances in Neural Information Processing Systems_ , 33:9459–9474.
  * Li et al. (2023) Sha Li, Chi Han, Pengfei Yu, Carl Edwards, Manling Li, Xingyao Wang, Yi Fung, Charles Yu, Joel Tetreault, Eduard Hovy, and Heng Ji. 2023.  Defining a new NLP playground.  In _Findings of the Association for Computational Linguistics: EMNLP 2023_ , pages 11932–11951, Singapore. Association for Computational Linguistics.
  * Lian et al. (2022) Ruixue Lian, Che-Wei Huang, Yuqing Tang, Qilong Gu, Chengyuan Ma, and Chenlei Guo. 2022.  Incremental user embedding modeling for personalized text classification.  In _ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_ , pages 7832–7836. IEEE.
  * Mao et al. (2023) Shengyu Mao, Ningyu Zhang, Xiaohan Wang, Mengru Wang, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang, and Huajun Chen. 2023.  Editing personality for llms.  _arXiv preprint arXiv:2310.02168_.
  * McClain et al. (2021) Colleen McClain, Regina Widjaya, Gonzalo Rivero, and Aaron Smith. 2021.  The behaviors and attitudes of us adults on twitter.
  * Mireshghallah et al. (2022) Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, and Dimitrios Dimitriadis. 2022.  Useridentifier: Implicit user representations for simple and effective personalized sentiment analysis.  In _Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022_ , pages 3449–3456. Association for Computational Linguistics.
  * Mohammad et al. (2018) Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko. 2018.  Semeval-2018 task 1: Affect in tweets.  In _Proceedings of the 12th international workshop on semantic evaluation_ , pages 1–17.
  * Paszke et al. (2019) Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019\.  Pytorch: An imperative style, high-performance deep learning library.  _arXiv preprint arXiv:1912.01703_.
  * Qiu et al. (2021) Zhaopeng Qiu, Xian Wu, Jingyue Gao, and Wei Fan. 2021.  U-bert: Pre-training user representations for improved recommendation.  In _Proceedings of the AAAI Conference on Artificial Intelligence_ , volume 35, pages 4320–4327.
  * Reimer et al. (2022) Nils Karl Reimer, Mohammad Atari, Farzan Karimi-Malekabadi, Jackson Trager, Brendan Kennedy, Jesse Graham, and Morteza Dehghani. 2022.  Moral values predict county-level covid-19 vaccination rates in the united states.  _American Psychologist_ , 77(6):743.
  * Srinivasan et al. (2022) Krishna Srinivasan, Karthik Raman, Anupam Samanta, Lingrui Liao, Luca Bertelli, and Mike Bendersky. 2022.  Quill: Query intent with large language models using retrieval augmentation and multi-stage distillation.  _arXiv preprint arXiv:2210.15718_.
  * Su et al. (2022) Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A Smith, Luke Zettlemoyer, and Tao Yu. 2022.  One embedder, any task: Instruction-finetuned text embeddings.  _arXiv preprint arXiv:2212.09741_.
  * Sun et al. (2023) Chenkai Sun, Jinning Li, Hou Pong Chan, ChengXiang Zhai, and Heng Ji. 2023.  Measuring the effect of influential messages on varying personas.  _arXiv preprint arXiv:2305.16470_.
  * Torelli and Kaikati (2009) Carlos J Torelli and Andrew M Kaikati. 2009.  Values as predictors of judgments and behaviors: The role of abstract and concrete mindsets.  _Journal of personality and social psychology_ , 96(1):231.
  * Wang and McAllester (2020) Hai Wang and David McAllester. 2020.  On-the-fly information retrieval augmentation for language models.  _arXiv preprint arXiv:2007.01528_.
  * Wang et al. (2021) Zhenghui Wang, Lingxiao Luo, and Diyi Yang. 2021.  Personalized response generation with tensor factorization.  In _Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)_ , pages 47–57.
  * Wang et al. (2023) Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, and Graham Neubig. 2023\.  Learning to filter context for retrieval-augmented generation.  _arXiv preprint arXiv:2311.08377_.
  * Wei et al. (2021) Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021.  Finetuned language models are zero-shot learners.  _arXiv preprint arXiv:2109.01652_.
  * Wolf et al. (2020) Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020.  Transformers: State-of-the-art natural language processing.  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations_ , pages 38–45, Online. Association for Computational Linguistics.
  * Zhang et al. (2023) Peitian Zhang, Shitao Xiao, Zheng Liu, Zhicheng Dou, and Jian-Yun Nie. 2023.  Retrieve anything to augment large language models.  _arXiv preprint arXiv:2310.07554_.

##  Appendix A Appendix

###  A.1 Additional Details

The data statistics are shown in Table 3. In the main and case studies, we set
25% as the composition ratio. We show all prompts used in the work in the
following figures. Figure 5 and Figure 6 represent the prompts for
constructing the hierarchical database. Figure 7, Figure 8, and Figure 9
represent the inference prompt for baseline, Persona-DB w/o JOIN, and Persona-
DB.

Figure 5: Prompt template used for extracting hierarchical user database. Figure 6: Prompt template used for extracting hierarchical user database. Figure 7: Prompt template used for Baseline Inference. Figure 8: Prompt template used for Inference for Persona-DB without JOIN. Figure 9: Prompt template used for Inference for Persona-DB. Split | Train | Dev. | Test
---|---|---|---
# Samples | 10,977 | 1,341 | 1,039
# Headlines | 3,561 | 1,065 | 843
# Users | 7,243 | 1,206 | 961
Avg # Profile Tokens | 10.75 | 11.02 | 10.50
Avg # Response Tokens | 12.33 | 12.2 | 11.87
Avg # Headline Tokens | 19.79 | 19.82 | 19.72
Table 3: Summary statistics for the original dataset.