Large Language Models Can Accurately Predict Searcher Preferences and Outperform Human Labelers

Core Concepts

Large language models can generate relevance labels for search results that are as accurate as or more accurate than labels provided by human assessors, and can be used to efficiently scale relevance labeling for information retrieval system evaluation.

Abstract

The paper discusses an alternative approach to obtaining high-quality relevance labels for evaluating information retrieval systems. Traditionally, relevance labels are obtained from human assessors, which can be costly, time-consuming, and prone to biases and errors. The authors propose using large language models (LLMs) to generate relevance labels that match real searcher preferences.
The key highlights and insights are:

The authors conducted experiments using the TREC-Robust dataset, comparing relevance labels generated by LLMs to those provided by human TREC assessors. They found that with careful prompt engineering, LLMs can achieve label quality comparable to or better than human assessors, as measured by agreement with the human labels.

The authors also evaluated the impact of the LLM-generated labels on query and system ranking, and found that the rankings derived from LLM labels were highly consistent with those derived from human labels.

The authors argue that LLMs offer several advantages over human labelers, including higher accuracy, faster throughput, lower cost, and better scalability. They report successful deployment of LLM-based relevance labeling at Bing, where the LLM labels outperformed both crowd workers and in-house experts.

The authors note that LLM performance is highly sensitive to prompt wording, and emphasize the importance of carefully selecting and validating the prompt against a high-quality ground truth dataset of real searcher preferences.

Stats

"LLMs are as accurate as human labellers and as useful for finding the best systems and hardest queries."
"LLM performance varies with prompt features, but also varies unpredictably with simple paraphrases."

Quotes

"LLMs can do better on this metric than any population of human labellers that we study."
"Our experiments show LLMs are as accurate as human labellers and as useful for finding the best systems and hardest queries."

Key Insights Distilled From

Large language models can accurately predict searcher preferences

by Paul Thomas,... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2309.10621.pdf

Deeper Inquiries

How can the prompt engineering process be further automated or optimized to reduce the manual effort required

To further automate and optimize the prompt engineering process for LLMs, several strategies can be implemented:

Automated Prompt Generation: Develop algorithms that can generate diverse and effective prompts based on input parameters such as query type, document characteristics, and desired label granularity. This can reduce the manual effort required to create prompts for each task.

Prompt Optimization Algorithms: Implement machine learning models that can analyze the performance of different prompts and iteratively optimize them based on feedback. This can help in identifying the most effective prompt structures for different types of tasks.

Natural Language Processing (NLP) Techniques: Utilize NLP techniques to analyze the effectiveness of different prompt variations and suggest improvements. This can include sentiment analysis, language modeling, and semantic similarity calculations.

Human-in-the-Loop Systems: Develop systems where human annotators provide feedback on generated prompts, which is then used to refine and improve the prompt generation algorithms. This iterative process can lead to more effective prompts with minimal manual effort.

Prompt Library: Create a library of pre-designed prompts for common tasks and domains, allowing users to select and customize prompts based on their specific requirements. This can streamline the prompt engineering process and reduce manual effort.

What are the potential biases or limitations of using LLM-generated labels, and how can they be mitigated

Using LLM-generated labels may introduce potential biases and limitations that need to be addressed to ensure the accuracy and reliability of the labeling process:

Bias in Training Data: LLMs can inadvertently learn biases present in the training data, leading to biased label generation. Regularly auditing and updating the training data to remove biases can help mitigate this issue.

Lack of Contextual Understanding: LLMs may struggle to understand the nuanced context of certain queries or documents, leading to inaccuracies in label generation. Providing additional context or using multi-step prompts can help improve understanding.

Overfitting: LLMs may overfit to specific patterns in the training data, resulting in labels that do not generalize well to new data. Regularly testing the LLM on unseen data and adjusting the training process can help prevent overfitting.

Ethical Considerations: LLMs may inadvertently generate labels that are unethical or harmful. Implementing ethical guidelines and oversight mechanisms can help ensure that the generated labels are appropriate and unbiased.

Quality Control: Establishing robust quality control measures, such as human validation of LLM-generated labels, can help identify and correct any inaccuracies or biases in the labeling process.

How might this LLM-based labeling approach be extended to other information retrieval tasks beyond relevance assessment

The LLM-based labeling approach can be extended to various information retrieval tasks beyond relevance assessment by adapting the prompt structure and training data to suit the specific task requirements. Some ways to extend this approach include:

Sentiment Analysis: Use LLMs to generate labels for sentiment analysis tasks, where the goal is to determine the sentiment (positive, negative, neutral) of a piece of text. Prompts can be designed to capture the sentiment of the content accurately.

Entity Recognition: Employ LLMs to label entities in text, such as names, locations, organizations, etc. Prompts can guide the model to identify and label specific entities within the text accurately.

Document Classification: Utilize LLMs for document classification tasks, where documents are categorized into predefined classes or categories. Prompts can be designed to guide the model in assigning the correct class labels to documents.

Question Answering: Extend the LLM-based approach to question-answering tasks, where the model generates labels indicating whether a given answer is correct or not. Prompts can be structured to elicit accurate responses from the model.

Information Extraction: Apply LLMs for information extraction tasks, where specific pieces of information need to be identified and labeled within text data. Prompts can guide the model in extracting and labeling relevant information accurately.