Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy

Hengran Zhang, Keping Bi, Jiafeng Guo, Xueqi Cheng·June 17, 2024

Summary

The paper introduces the Iterative Utility Judgment Framework (ITEM) for Information Retrieval, drawing from Schutz's relevance concept. ITEM enhances Retrieval-Augmented Generation (RAG) by focusing on utility, a higher standard of relevance, alongside topical relevance. It addresses the need for utility judgments in guiding large language models (LLMs) with valuable retrieval results. Experiments on TREC DL, WebAP, and NQ datasets show significant improvements in utility, ranking, and answer generation compared to baseline methods. The study highlights the effectiveness of iterative and dynamic approaches in improving LLM performance in tasks like answer generation and passage ranking, with ITEM-ARr being less effective than other variants. The paper also evaluates different LLMs, retrievers, and methods, emphasizing the importance of utility judgments and the potential for future fine-tuning.

Key findings

5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper proposes an Iterative Utility Judgment Framework (ITEM) inspired by relevance in philosophy to enhance utility judgments and question-answering (QA) performance of Large Language Models (LLMs) . This framework aims to address the challenge of incorporating utility judgments into tasks like Retrieval-Augmented Generation (RAG) by promoting each step of the RAG cycle through dynamic iterations of topical relevance, utility, and answering . The paper introduces a novel approach that involves making iterative utility judgments on the results obtained from a single retrieval, which differs from previous methods that rely on multi-round retrieval based on feedback from LLMs . The research focuses on improving utility judgments, ranking of topical relevance, and answer generation tasks, showcasing significant advancements over existing baselines . This problem of enhancing utility judgments in LLMs is a new and innovative approach that contributes to the field of information retrieval and natural language processing .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate an Iterative Utility Judgment Framework inspired by relevance in philosophy to enhance the utility judgment and QA performance of Large Language Models (LLMs) by incorporating utility judgments into Retrieval-Augmented Generation (RAG) . The framework emphasizes the importance of utility and topical relevance in information retrieval, focusing on the interaction between topical relevance, utility, and answering in RAG, which are related to the three types of relevance discussed by Schutz: topical relevance, interpretational relevance, and motivational relevance . The study explores the dynamic iterations of these relevance types to promote each step of the RAG cycle, leading to significant improvements in utility judgments, ranking of topical relevance, and answer generation compared to baseline methods .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes an Iterative Utility Judgment Framework inspired by relevance in philosophy, focusing on utility judgments via Large Language Models (LLMs) . The framework aims to enhance utility judgments for LLMs by identifying passages that are useful in answering a given question . It introduces the concept of making iterative utility judgments on the results obtained from a single retrieval, emphasizing the importance of reducing operational costs associated with conducting multiple retrievals for a single query .

The paper suggests two typical input approaches for LLMs in utility judgments: Listwise and Pointwise. In the Listwise approach, the utility judgments function is based on the whole candidate list of passages, while in the Pointwise approach, the function focuses on individual passages that meet specific criteria . This distinction allows for a more nuanced evaluation of the retrieved passages to determine their utility in answering the question effectively.

Furthermore, the paper acknowledges three primary limitations of the proposed framework. Firstly, the methods are applied in zero-shot scenarios without any training, which can lead to sensitivity to prompts and unstable performance. Future research is suggested to explore more effective training methods to genuinely enhance LLMs' ability in utility judgments through training . Secondly, the number of candidate passages assumed in the search scenario is limited, indicating the need for further study in large-scale scenarios. Lastly, the iterative framework's effectiveness raises concerns about the increased cost of calling large models, prompting the exploration of methods to reduce iteration costs in the future . The Iterative Utility Judgment Framework proposed in the paper introduces several key characteristics and advantages compared to previous methods. Firstly, the framework emphasizes the significance of iterative interactions in enhancing utility judgments for Large Language Models (LLMs) by leveraging multiple iterations, leading to improved performance in utility judgments compared to single iterations . This iterative approach is demonstrated to enhance the F1 scores of LLMs like Mistral, Llama 3, and ChatGPT on datasets such as WebAP, showcasing performance improvements ranging from 6.4% to 7.3% after multiple iterations .

Moreover, the paper highlights the distinction between explicit and implicit answers in utility judgments based on the Listwise and Pointwise approaches. It suggests that explicit answers generally outperform implicit answers in utility judgments when using the Listwise approach, while the opposite is true for the Pointwise approach. This distinction is crucial as it influences the effectiveness of utility judgments in addressing the information needs of a given question. The proposed framework, through its iterative nature, shows greater improvement with explicit answers compared to implicit answers in both input approaches in most cases .

Additionally, the comparison between different LLMs within the framework reveals that ChatGPT outperforms other LLMs on various datasets using both Listwise and Pointwise input approaches. For instance, on the TREC dataset, ChatGPT achieves significant F1 improvements after multiple iterations, showcasing its superiority over Mistral and Llama 3. This comparative analysis underscores the effectiveness of ChatGPT within the proposed Iterative Utility Judgment Framework .

Furthermore, the paper provides detailed performance metrics, including Precision, Recall, and F1 scores, to offer a comprehensive analysis of the framework's effectiveness compared to previous methods. For example, Mistral demonstrates a 6.0% F1 improvement with the Listwise approach over the Pointwise approach on the TREC dataset after multiple iterations, highlighting the framework's superiority in utility judgments . These detailed performance evaluations contribute to a thorough understanding of the framework's advantages over existing methods in enhancing utility judgments for LLMs.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of utility judgments and relevance in information retrieval. Noteworthy researchers in this area include Hengran Zhang, Keping Bi, Jiafeng Guo, Xueqi Cheng, Ruqing Zhang, Maarten de Rijke, Yixing Fan, Xinran Zhao, Tong Chen, and many others . The key solution proposed in the paper is the Iterative Utility Judgment Framework (ITEM), which aims to enhance utility judgments, topical relevance ranking, and answer generation in Retrieval-Augmented Generation (RAG) tasks through dynamic iterations inspired by different types of relevance .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate utility judgments using Precision, Recall, and F1 for the utility judgments task, and normalized discounted cumulative gain (NDCG) for the ranking task . The experiments utilized several representative Language Model Models (LLMs) including ChatGPT, Mistral, and Llama 3 . Two retrievers, RocketQAv2 and BM25, were employed to gather candidate passages for utility judgments . The experiments also compared the performance of different LLMs in terms of answer generation using metrics like exact match (EM) and F1 . Additionally, the experiments explored the impact of different iteration stop conditions on utility judgments using Mistral on retrieval datasets . The study also compared the performance of LLMs in terms of utility judgments with multiple iterations versus single iterations, highlighting the significance of iterative interaction in improving performance .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the TREC dataset and the WebAP dataset . The code used in the study is open source, as it mentions the use of open-source large language models for listwise document reranking .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The study utilizes several representative Language Models (LLMs) such as ChatGPT, Mistral, and Llama 3 to conduct experiments . These LLMs are evaluated based on Precision, Recall, F1, and normalized discounted cumulative gain (NDCG) metrics . The experiments demonstrate the effectiveness of large language models as text rankers with pairwise ranking prompting, showcasing their utility in various tasks .

Furthermore, the paper discusses the use of different stop conditions for the Iterative Utility Judgment Framework, such as utility judgments and answer generation . The performance of Mistral under different stop conditions is analyzed, providing insights into the quality of utility judgments . This detailed analysis enhances the understanding of how these models perform in judging the utility of passages in answering questions .

Moreover, the study compares the performance of Mistral, Llama 3, and ChatGPT on different datasets, highlighting the effectiveness of Mistral in outperforming baselines and other models in terms of topical relevance ranking and utility judgments . The results indicate that Mistral, particularly with single-iteration ITEM-ARs, shows superior performance, emphasizing the importance of topical relevance ranking in achieving better utility judgments .

In conclusion, the experiments and results presented in the paper provide robust evidence supporting the scientific hypotheses under investigation. The detailed evaluation of different LLMs, stop conditions, and performance metrics contributes to a comprehensive analysis of the utility and effectiveness of these models in various tasks, reinforcing the validity of the scientific hypotheses being tested .


What are the contributions of this paper?

The paper "Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy" makes several contributions:

  • It introduces an Iterative Utility Judgment Framework (ITEM) that incorporates utility judgments into Retrieval-Augmented Generation (RAG) to enhance downstream tasks .
  • The framework is inspired by the three types of relevance discussed by Schutz: topical relevance, interpretational relevance, and motivational relevance, promoting each step of the RAG cycle .
  • Extensive experiments conducted on multi-grade passage retrieval and factoid question-answering datasets show significant improvements in utility judgments, topical relevance ranking, and answer generation compared to baseline approaches .

What work can be continued in depth?

To further advance the research in this area, several avenues can be explored:

  • Developing better fine-tuning strategies for utility judgments to enhance the performance of Large Language Models (LLMs) .
  • Creating end-to-end solutions that integrate retrieval and utility judgments to optimize information retrieval processes .
  • Exploring the impact of dynamic interactions in achieving high performance and stability in utility judgments and question-answering tasks .
  • Investigating the effectiveness of different LLMs, such as ChatGPT, Mistral, and Llama 3, in utility judgments and answer generation tasks .
  • Further studying the iterative relevance feedback via LLMs to understand how multiple rounds of utility judgments can enhance retrieval and answer generation .

Tables

2

Introduction
Background
Relevance concept in Information Retrieval
Schutz's relevance theory
Objective
Addressing the need for utility in LLM guidance
Enhancing Retrieval-Augmented Generation (RAG)
Method
Data Collection
Datasets used
TREC DL
WebAP
NQ
Dataset preparation for utility judgments
Data Preprocessing
Iterative approach for utility-focused data preprocessing
Dynamic adaptation of relevance criteria
ITEM Framework
ITEM-Base: Baseline RAG with utility consideration
ITEM-AR: Iterative retrieval and augmentation
ITEM-ARr: Iterative retrieval, augmentation, and ranking
Experiments and Evaluation
Performance metrics
Utility
Ranking
Answer generation
Comparative analysis with baseline methods
Sensitivity to LLMs, retrievers, and fine-tuning
Results and Discussion
Significance of utility improvements
Advantages of iterative and dynamic approaches
Limitations and challenges (e.g., ITEM-ARr effectiveness)
Future Directions
Potential for utility-focused fine-tuning of LLMs
Applications in real-world IR scenarios
Open research questions and directions
Conclusion
Summary of ITEM's contributions
Implications for Information Retrieval and large language models
Call for further utility-oriented research in the field.
Basic info
papers
computation and language
information retrieval
machine learning
artificial intelligence
Advanced features
Insights
Which variant of ITEM is found to be less effective in the study?
How does ITEM enhance Retrieval-Augmented Generation (RAG)?
What framework does the paper present for Information Retrieval?
What datasets are used for experiments in the paper to evaluate the effectiveness of ITEM?

Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy

Hengran Zhang, Keping Bi, Jiafeng Guo, Xueqi Cheng·June 17, 2024

Summary

The paper introduces the Iterative Utility Judgment Framework (ITEM) for Information Retrieval, drawing from Schutz's relevance concept. ITEM enhances Retrieval-Augmented Generation (RAG) by focusing on utility, a higher standard of relevance, alongside topical relevance. It addresses the need for utility judgments in guiding large language models (LLMs) with valuable retrieval results. Experiments on TREC DL, WebAP, and NQ datasets show significant improvements in utility, ranking, and answer generation compared to baseline methods. The study highlights the effectiveness of iterative and dynamic approaches in improving LLM performance in tasks like answer generation and passage ranking, with ITEM-ARr being less effective than other variants. The paper also evaluates different LLMs, retrievers, and methods, emphasizing the importance of utility judgments and the potential for future fine-tuning.
Mind map
Sensitivity to LLMs, retrievers, and fine-tuning
Comparative analysis with baseline methods
Answer generation
Ranking
Utility
ITEM-ARr: Iterative retrieval, augmentation, and ranking
ITEM-AR: Iterative retrieval and augmentation
ITEM-Base: Baseline RAG with utility consideration
NQ
WebAP
TREC DL
Performance metrics
ITEM Framework
Dataset preparation for utility judgments
Datasets used
Enhancing Retrieval-Augmented Generation (RAG)
Addressing the need for utility in LLM guidance
Schutz's relevance theory
Relevance concept in Information Retrieval
Call for further utility-oriented research in the field.
Implications for Information Retrieval and large language models
Summary of ITEM's contributions
Open research questions and directions
Applications in real-world IR scenarios
Potential for utility-focused fine-tuning of LLMs
Limitations and challenges (e.g., ITEM-ARr effectiveness)
Advantages of iterative and dynamic approaches
Significance of utility improvements
Experiments and Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Future Directions
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Relevance concept in Information Retrieval
Schutz's relevance theory
Objective
Addressing the need for utility in LLM guidance
Enhancing Retrieval-Augmented Generation (RAG)
Method
Data Collection
Datasets used
TREC DL
WebAP
NQ
Dataset preparation for utility judgments
Data Preprocessing
Iterative approach for utility-focused data preprocessing
Dynamic adaptation of relevance criteria
ITEM Framework
ITEM-Base: Baseline RAG with utility consideration
ITEM-AR: Iterative retrieval and augmentation
ITEM-ARr: Iterative retrieval, augmentation, and ranking
Experiments and Evaluation
Performance metrics
Utility
Ranking
Answer generation
Comparative analysis with baseline methods
Sensitivity to LLMs, retrievers, and fine-tuning
Results and Discussion
Significance of utility improvements
Advantages of iterative and dynamic approaches
Limitations and challenges (e.g., ITEM-ARr effectiveness)
Future Directions
Potential for utility-focused fine-tuning of LLMs
Applications in real-world IR scenarios
Open research questions and directions
Conclusion
Summary of ITEM's contributions
Implications for Information Retrieval and large language models
Call for further utility-oriented research in the field.
Key findings
5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper proposes an Iterative Utility Judgment Framework (ITEM) inspired by relevance in philosophy to enhance utility judgments and question-answering (QA) performance of Large Language Models (LLMs) . This framework aims to address the challenge of incorporating utility judgments into tasks like Retrieval-Augmented Generation (RAG) by promoting each step of the RAG cycle through dynamic iterations of topical relevance, utility, and answering . The paper introduces a novel approach that involves making iterative utility judgments on the results obtained from a single retrieval, which differs from previous methods that rely on multi-round retrieval based on feedback from LLMs . The research focuses on improving utility judgments, ranking of topical relevance, and answer generation tasks, showcasing significant advancements over existing baselines . This problem of enhancing utility judgments in LLMs is a new and innovative approach that contributes to the field of information retrieval and natural language processing .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate an Iterative Utility Judgment Framework inspired by relevance in philosophy to enhance the utility judgment and QA performance of Large Language Models (LLMs) by incorporating utility judgments into Retrieval-Augmented Generation (RAG) . The framework emphasizes the importance of utility and topical relevance in information retrieval, focusing on the interaction between topical relevance, utility, and answering in RAG, which are related to the three types of relevance discussed by Schutz: topical relevance, interpretational relevance, and motivational relevance . The study explores the dynamic iterations of these relevance types to promote each step of the RAG cycle, leading to significant improvements in utility judgments, ranking of topical relevance, and answer generation compared to baseline methods .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes an Iterative Utility Judgment Framework inspired by relevance in philosophy, focusing on utility judgments via Large Language Models (LLMs) . The framework aims to enhance utility judgments for LLMs by identifying passages that are useful in answering a given question . It introduces the concept of making iterative utility judgments on the results obtained from a single retrieval, emphasizing the importance of reducing operational costs associated with conducting multiple retrievals for a single query .

The paper suggests two typical input approaches for LLMs in utility judgments: Listwise and Pointwise. In the Listwise approach, the utility judgments function is based on the whole candidate list of passages, while in the Pointwise approach, the function focuses on individual passages that meet specific criteria . This distinction allows for a more nuanced evaluation of the retrieved passages to determine their utility in answering the question effectively.

Furthermore, the paper acknowledges three primary limitations of the proposed framework. Firstly, the methods are applied in zero-shot scenarios without any training, which can lead to sensitivity to prompts and unstable performance. Future research is suggested to explore more effective training methods to genuinely enhance LLMs' ability in utility judgments through training . Secondly, the number of candidate passages assumed in the search scenario is limited, indicating the need for further study in large-scale scenarios. Lastly, the iterative framework's effectiveness raises concerns about the increased cost of calling large models, prompting the exploration of methods to reduce iteration costs in the future . The Iterative Utility Judgment Framework proposed in the paper introduces several key characteristics and advantages compared to previous methods. Firstly, the framework emphasizes the significance of iterative interactions in enhancing utility judgments for Large Language Models (LLMs) by leveraging multiple iterations, leading to improved performance in utility judgments compared to single iterations . This iterative approach is demonstrated to enhance the F1 scores of LLMs like Mistral, Llama 3, and ChatGPT on datasets such as WebAP, showcasing performance improvements ranging from 6.4% to 7.3% after multiple iterations .

Moreover, the paper highlights the distinction between explicit and implicit answers in utility judgments based on the Listwise and Pointwise approaches. It suggests that explicit answers generally outperform implicit answers in utility judgments when using the Listwise approach, while the opposite is true for the Pointwise approach. This distinction is crucial as it influences the effectiveness of utility judgments in addressing the information needs of a given question. The proposed framework, through its iterative nature, shows greater improvement with explicit answers compared to implicit answers in both input approaches in most cases .

Additionally, the comparison between different LLMs within the framework reveals that ChatGPT outperforms other LLMs on various datasets using both Listwise and Pointwise input approaches. For instance, on the TREC dataset, ChatGPT achieves significant F1 improvements after multiple iterations, showcasing its superiority over Mistral and Llama 3. This comparative analysis underscores the effectiveness of ChatGPT within the proposed Iterative Utility Judgment Framework .

Furthermore, the paper provides detailed performance metrics, including Precision, Recall, and F1 scores, to offer a comprehensive analysis of the framework's effectiveness compared to previous methods. For example, Mistral demonstrates a 6.0% F1 improvement with the Listwise approach over the Pointwise approach on the TREC dataset after multiple iterations, highlighting the framework's superiority in utility judgments . These detailed performance evaluations contribute to a thorough understanding of the framework's advantages over existing methods in enhancing utility judgments for LLMs.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of utility judgments and relevance in information retrieval. Noteworthy researchers in this area include Hengran Zhang, Keping Bi, Jiafeng Guo, Xueqi Cheng, Ruqing Zhang, Maarten de Rijke, Yixing Fan, Xinran Zhao, Tong Chen, and many others . The key solution proposed in the paper is the Iterative Utility Judgment Framework (ITEM), which aims to enhance utility judgments, topical relevance ranking, and answer generation in Retrieval-Augmented Generation (RAG) tasks through dynamic iterations inspired by different types of relevance .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate utility judgments using Precision, Recall, and F1 for the utility judgments task, and normalized discounted cumulative gain (NDCG) for the ranking task . The experiments utilized several representative Language Model Models (LLMs) including ChatGPT, Mistral, and Llama 3 . Two retrievers, RocketQAv2 and BM25, were employed to gather candidate passages for utility judgments . The experiments also compared the performance of different LLMs in terms of answer generation using metrics like exact match (EM) and F1 . Additionally, the experiments explored the impact of different iteration stop conditions on utility judgments using Mistral on retrieval datasets . The study also compared the performance of LLMs in terms of utility judgments with multiple iterations versus single iterations, highlighting the significance of iterative interaction in improving performance .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the TREC dataset and the WebAP dataset . The code used in the study is open source, as it mentions the use of open-source large language models for listwise document reranking .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The study utilizes several representative Language Models (LLMs) such as ChatGPT, Mistral, and Llama 3 to conduct experiments . These LLMs are evaluated based on Precision, Recall, F1, and normalized discounted cumulative gain (NDCG) metrics . The experiments demonstrate the effectiveness of large language models as text rankers with pairwise ranking prompting, showcasing their utility in various tasks .

Furthermore, the paper discusses the use of different stop conditions for the Iterative Utility Judgment Framework, such as utility judgments and answer generation . The performance of Mistral under different stop conditions is analyzed, providing insights into the quality of utility judgments . This detailed analysis enhances the understanding of how these models perform in judging the utility of passages in answering questions .

Moreover, the study compares the performance of Mistral, Llama 3, and ChatGPT on different datasets, highlighting the effectiveness of Mistral in outperforming baselines and other models in terms of topical relevance ranking and utility judgments . The results indicate that Mistral, particularly with single-iteration ITEM-ARs, shows superior performance, emphasizing the importance of topical relevance ranking in achieving better utility judgments .

In conclusion, the experiments and results presented in the paper provide robust evidence supporting the scientific hypotheses under investigation. The detailed evaluation of different LLMs, stop conditions, and performance metrics contributes to a comprehensive analysis of the utility and effectiveness of these models in various tasks, reinforcing the validity of the scientific hypotheses being tested .


What are the contributions of this paper?

The paper "Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy" makes several contributions:

  • It introduces an Iterative Utility Judgment Framework (ITEM) that incorporates utility judgments into Retrieval-Augmented Generation (RAG) to enhance downstream tasks .
  • The framework is inspired by the three types of relevance discussed by Schutz: topical relevance, interpretational relevance, and motivational relevance, promoting each step of the RAG cycle .
  • Extensive experiments conducted on multi-grade passage retrieval and factoid question-answering datasets show significant improvements in utility judgments, topical relevance ranking, and answer generation compared to baseline approaches .

What work can be continued in depth?

To further advance the research in this area, several avenues can be explored:

  • Developing better fine-tuning strategies for utility judgments to enhance the performance of Large Language Models (LLMs) .
  • Creating end-to-end solutions that integrate retrieval and utility judgments to optimize information retrieval processes .
  • Exploring the impact of dynamic interactions in achieving high performance and stability in utility judgments and question-answering tasks .
  • Investigating the effectiveness of different LLMs, such as ChatGPT, Mistral, and Llama 3, in utility judgments and answer generation tasks .
  • Further studying the iterative relevance feedback via LLMs to understand how multiple rounds of utility judgments can enhance retrieval and answer generation .
Tables
2
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