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# How does prompt engineering affect ChatGPT performance on unsupervised
entity resolution?

Khanin Sisaengsuwanchai<EMAIL_ADDRESS>University of Southern CaliforniaLos
AngelesCaliforniaUSA90292 ,  Navapat Nananukul<EMAIL_ADDRESS>University of
Southern CaliforniaLos AngelesCaliforniaUSA90292 and  Mayank Kejriwal
<EMAIL_ADDRESS>University of Southern CaliforniaLos
AngelesCaliforniaUSA90292

(2023; 20 February 2007; 12 March 2009; 5 June 2009)

###### Abstract.

Entity Resolution (ER) is the problem of semi-automatically determining when
two entities refer to the same underlying entity, with applications ranging
from healthcare to e-commerce. Traditional ER solutions required considerable
manual expertise, including feature engineering, as well as identification and
curation of training data. In many instances, such techniques are highly
dependent on the domain. With recent advent in large language models (LLMs),
there is an opportunity to make ER much more seamless and domain-independent.
However, it is also well known that LLMs can pose risks, and that the quality
of their outputs can depend on so-called prompt engineering. Unfortunately, a
systematic experimental study on the effects of different prompting methods
for addressing ER, using LLMs like ChatGPT, has been lacking thus far. This
paper aims to address this gap by conducting such a study. Although
preliminary in nature, our results show that prompting can significantly
affect the quality of ER, although it affects some metrics more than others,
and can also be dataset dependent.

large language models, prompt engineering, experimental study, entity
resolution, generative models

††copyright: acmcopyright††journalyear: 2023††doi:
XXXXXXX.XXXXXXX††conference: Make sure to enter the correct conference title
from your rights confirmation email; June 03–05, 2018; Woodstock, NY††price:
15.00††isbn: 978-1-4503-XXXX-X/18/06

##  1\. Background

Entity Resolution (ER) is at least a 50 year-old problem that has been studied
in multiple Artificial Intelligence contexts (Kejriwal and Kejriwal, 2019;
Getoor and Machanavajjhala, 2012; Kejriwal, 2023; Christophides et al., 2020;
Getoor and Machanavajjhala, 2013; Christophides et al., 2015; Kejriwal, 2014).
ER can be defined quite simply as the algorithmic problem of determining when
two or more entities refer to the same underlying entity. Ironically, ER
itself has been studied under many names, including entity matching, instance
matching, deduplication, and record linkage, to just name a few (Getoor and
Machanavajjhala, 2012; Kejriwal et al., 2021; Elmagarmid et al., 2006;
Kejriwal, 2022).

Many promising solutions to ER have been proposed over the decades, and
significant progress has been achieved. Nevertheless, we are far from solving
the problem at human performance levels, and errors can be costly. While
initially (and in some contexts still), rule-based and manually engineered
solutions were prevalent (Papadakis et al., 2021; Volz et al., 2009), machine
learning methods became increasingly popular as a solution for ER, as with
many other problems amenable to learning from data, starting from the early-
mid 2000s (Talburt, 2011; Kejriwal, 2016). Over the last decade, deep learning
solutions have also been applied to ER (Ebraheem et al., 2017; Di Cicco et
al., 2019; Nafa et al., 2022; Kasai et al., 2019; Kooli et al., 2018), and
even more recently, transformer-based models such as BERT (Li et al., 2020a).
Performance has steadily improved, but on challenging datasets, remains
significantly below human performance.

At the same time, because of the domain-specific nature of ER (e.g., ER in
e-commerce (Gheini and Kejriwal, 2019) can be significantly different than in
a healthcare setting (Gupta and Deshpande, 2020), where precision requirements
might be stronger), manual engineering has still played an important role,
requiring considerable effort in setting up and executing an ER architecture.
One issue is that a typical ER workflow comprises two steps: blocking and
similarity (Li et al., 2020b; Balaji et al., 2016; Kejriwal, 2021). Blocking
is necessary to mitigate the quadratic complexity111Given $n$ entities or
‘records’, and a matching function $f$ that indicates whether a pair of
records matches or not, a naive ER solution would still need $O(n^{2})$
comparisons. of ER (Papadakis et al., 2020; Kejriwal and Miranker, 2013). A
blocking function clusters records together in time that, in practice, is sub-
quadratic. Only records within these clusters, called blocks, are then
compared with one another using a similarity function that is trained using
machine learning. In recent years, there has been enormous progress on
learning-based and even unsupervised blocking (O’Hare et al., 2019; Kejriwal
and Miranker, 2015b, 2014, a) but it is less clear how to apply LLMs to
blocking. In this paper, we focus solely on similarity, as our intent is to
study whether a large language model (LLM) can be used as a good similarity
method.

Recent advent of LLMs suggest that they could indeed be a promising and
unsupervised way of telling when two records are the same or not, at least
across most ordinary domains. However, clear evidence of this has been lacking
in the literature due to the recency of the LLMs. Additionally, through a new
line of research called _prompt engineering_ (Zhou et al., 2022; White et al.,
2023), there is now a considerable body of evidence showing that LLMs can be
sensitive to the manner in which they are prompted.

This paper is modeled as an experimental study that considers this question as
its primary focus. Specifically, how does prompt engineering affect the
performance of an LLM like ChatGPT on ‘unsupervised’ ER (where no explicit
training or fine-tuning is conducted), and even more broadly, what is the
general performance of ChatGPT on ER? To evaluate this question, we consider
five viable prompt engineering methods, in addition to a reasonable default
prompt, and use two e-commerce benchmark datasets that been extensively used
in the literature. At least one of these (based on matching products across
Google and Amazon) has been found to be challenging even for state-of-the-art
ER systems.

##  2\. Prompting Methods

Figure 1. Structure of an ER prompt presented to the LLM for a ‘candidate’
pair that needs to be classified as duplicate or non-duplicate.

In this study, we engineered 1 default prompt and 4 alternative prompts to
assess GPT-3.5’s capabilities in ER. Each alternative was derived by modifying
a single component from the default prompt. The default prompt (that we denote
as _Binary Reasoning with Concatenated Attributes_) integrates multiple
attributes for its input. The response structure is such that GPT-3.5 delivers
binary answers: “yes” or “no”, each accompanied by a rationale. Additionally,
a confidence level, ranging from 0 to 1, is provided for deeper insight. We
ensured that GPT-3.5 processes each product pair individually, guaranteeing
undivided attention to each set. To further enhance the quality of feedback,
we consistently applied a persona, refining its interpretability and accuracy
(Figure 1). Below, we briefly describe the five prompting methods (other than
the default) that were evaluated in this study:

  1. (1)

Single attribute (Title or Product name): The intuition behind this is giving
the simplest information such as the product name may lead to high matching
performance. The first alternative is changing from prompting a long
concatenation of multiple attributes to using one attribute in this case, each
dataset’s product name/title.

  2. (2)

Representing attributes using JSON format: We observed that GPT-3.5 can
process structured data efficiently. GPT-3.5 can separate attribute names or
keys and the information or values of JSON format data. Therefore, we embedded
all the pairs with JSON format type before sending them to GPT-3.5 for entity
matching.

  3. (3)

Few-shot prompting: This approach presents the model with three examples with
correct answers for each pair. We then request binary (yes or no) responses
from GPT-3.5 as we prompt in the default setup.

  4. (4)

Using similarity score instead of Y/N response: Rather than offering a simple
yes or no response, we aim to evaluate GPT-3.5’s performance through its
provision of similarity scores. We then aim to determine an optimal threshold
that maximizes the model’s F1 score.

  5. (5)

No persona: A persona provides context for ChatGPT to understand the user’s
input better. By “thinking” from the perspective of a particular persona, the
model can interpret and respond to user queries more accurately. To validate
these assertions in the context of ER, we exclude the persona and assess
GPT-3.5’s performance against the default configuration.

##  3\. Experimental Study

We present experiments on two established ER benchmarks that are publicly
available, and have been previously used extensively in the community for
evaluating ER:

  1. (1)

WDC Dataset  contains 26 million products and descriptions from e-commerce
websites. The products are categorized based on computers, cameras, shoes, and
watches. We selected computer type as our primary dataset in this experiment.
This smaller curated dataset consists of 7 attributes and comprises 1100
pairs, of which 300 are duplicates and the rest are non-duplicates.

  2. (2)

Amazon-Google Product contains product data from Amazon and Google. This
dataset contains mainly technology products such as software, and computer
hardware from both sites. This dataset is larger and more challenging,
consisting of 11,460 pairs, of which 1,166 are positives. There are only three
attributes, one of which is a detailed ‘description’ attribute.

###  3.1. Results

Table 1. Evaluation results for different prompt engineering methods on both benchmarks. Prompt Engineering Method/Technique | WDC Dataset | A.-Google
---|---|---
Precision | Recall | F1-score | Precision | Recall | F1-score
Binary Reasoning with Concatenated Attributes | 0.92 | 0.90 | 0.91 | 0.97 | 0.79 | 0.87
Single attribute: Title or Product Name | 0.91 | 0.94 | 0.93 | 0.96 | 0.70 | 0.81
Representing attributes using JSON format | 0.96 | 0.70 | 0.81 | 0.98 | 0.53 | 0.69
Few-shot prompting | 0.94 | 0.87 | 0.90 | 0.97 | 0.79 | 0.87
Using similarity score instead of Yes or No response | 0.97 | 0.85 | 0.91 | 0.93 | 0.97 | 0.95
No Persona | 0.97 | 0.85 | 0.91 | 0.97 | 0.56 | 0.71
Table 2. Cost Summary for different prompt engineering methods. Prompt Engineering Method / Technique | WDC Cost | Amazon-Google Cost
---|---|---
Binary Reasoning with Concatenated Attributes | $0.93 | $3.04
Single attribute: Title or Product Name | $0.59 | $2.19
Representing attributes using JSON format | $0.99 | $3.23
Few-shot prompting | $1.36 | $3.75
Using similarity score instead of Yes or No response | $0.95 | $3.11
No Persona | $0.68 | $2.01

Table 1 shows the result of each prompt method we designed across the two
datasets. Overall, the engineered prompts perform well on ER across both
datasets. Table 2 further illustrates the cost. Considering the _Single
Attribute_ prompting method, we find that there is no substantial difference
(compared to the default) across the metrics; nevertheless, the cost when
using a single attribute is considerably reduced (by about 37%). However, the
method presumes that we know a priori which attribute will contain the
information ‘density’ to enable the model to make a good matching decision
using only that attribute.

On the other hand, the use of JSON format increases the cost of GPT-3.5
because it increases the output token size. Furthermore, the F1-score
experiences a slight decline of 0.1 compared to the default method when
GPT-3.5 was tasked with entity matching over JSON-formatted data. This
indicates that a structured format like JSON does not particularly enhance
performance when prompting GPT-3.5 for ER.

Few-shot prompting was somewhat more effective than representing attributes
using JSON format, but it had the highest cost, as it required giving the
model examples of matching duplicates. A similar outcome can be achieved by
the default or by the single attribute method, but at significantly lower
cost.

When using the similarity score instead of binary response, the performance
shows that similarity score can create uncertainty in GPT-3.5 results. Even
when selecting the optimal threshold values for the F1 score, with the WDC
dataset, the performance dropped from 0.91 to 0.71. However, the F1-score
increased from the original method of 0.87 to 0.95 for the Amazon-Google
Dataset. Therefore, this method shows volatility but may be valuable in some
contexts. Its cost was similar to that of the default method (albeit the
slight cost difference could magnify if the experiments are conducted at large
scale).

Finally, the _no persona_ prompting method has similar performance (in terms
of F1-score) to the default method, but lower recall and higher precision. Its
cost is lower, but not as low as the single-attribute method. Considering its
lower cost (by more than 20%), it may be a useful cost-saving mechanism
compared to the default (assuming the single-attribute method cannot be used)
on larger-scale ER problems.

##  4\. Conclusion and Future Work

Entity Resolution continues to be an important problem in the database,
Semantic Web and knowledge capture communities. It has many applications, and
due to its highly domain-specific nature, is unlikely to be completely solved
by the advent of LLMs. Nevertheless, LLMs do offer an excellent alternative to
ER methods that require training and feature engineering. Even deep learning
methods had not completely eliminated this problem. Given the many issues that
have been noted for LLMs, including sensitivity to prompting, there was an
open question of how sensitive ER is to prompting, and what the baseline
quality of ER is when applied to models like GPT-3.5, on which the base
ChatGPT model is based.

Our results show that, while the prompting method does matter, the results are
generally remarkably stable. The real difference is in the cost. Although the
dollar amounts indicated in our results are small, it bears noting that the
benchmarks are not the size typically encountered in industrial-level ER,
where there can be many millions of records that may need to be deduplicated
and merged. Even differences of 5-10% in cost can be significant in such
situations (as can similar differences in performances). Therefore, the
seemingly small differences noted in our results suggest that prompting needs
to be taken carefully into account, perhaps through pilot studies, before any
large-scale ER process is undertaken using LLMs.

In future work, we plan to expand the prompting methods considered in this
paper, as well as consider ‘cross-tabulations’ of where one prompting method
is retrieving a duplicate correctly, but another is not. This may allow us to
better understand the relative merits of when to use one prompting method over
another. We will also consider more diverse datasets beyond e-commerce. This
is important because it would allow us to understand whether the performance
we observed in this paper is domain-independent or highly dependent on either
domain or dataset. While the results suggest some dependency, a larger, more
cross-domain study would help to yield insights that the preliminary study
here cannot.

Another agenda that we hope to pursue in future research is to compare how
these prompting methods can vary in performance across different LLMs,
including not just different foundation models within ChatGPT itself (e.g.,
GPT-3.5 versus GPT-4), but also LLMs that are publicly available (such as
Bloom) and released by other companies (such as Google’s Bard and Meta’s
Llama).

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