WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the challenge of evaluating large language models (LLMs) on their ability to handle real-world knowledge conflicts, specifically focusing on inter-context conflicts extracted from Wikipedia . This problem is not entirely new but highlights the need to enhance LLMs' performance in managing contradictions within complex settings . The study introduces the WikiContradict benchmark, which contains human-annotated instances of contradictions to assess LLMs' behavior when faced with knowledge inconsistencies .
What scientific hypothesis does this paper seek to validate?
This paper seeks to validate the hypothesis that prompting Large Language Models (LLMs) to pay attention to contradictory context information improves their performance in accurately answering questions that involve conflicting passages, especially for instances with explicit conflicts that require reasoning . The study focuses on evaluating LLMs' abilities to handle real-world inter-context conflicts by introducing the WikiContradict benchmark, which consists of human-annotated instances showcasing different types of contradictions extracted from Wikipedia . The goal is to assess how well LLMs perform in dealing with knowledge inconsistencies arising from the same or different retrieved passages, emphasizing the complexity and nuance of real-world knowledge conflicts .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper "WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia" introduces several novel contributions and methodologies .
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WikiContradict Benchmark: The paper proposes the WikiContradict benchmark, which focuses on real-world inter-context conflicts extracted and annotated from Wikipedia. This benchmark consists of 253 high-quality, human-annotated instances covering various types of contradictions identified by Wikipedia editors. The instances include explicit and implicit conflicts, requiring reasoning to detect contradictions .
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Evaluation of LLMs: The study evaluates the performance of different Large Language Models (LLMs) on the WikiContradict benchmark using diverse prompt templates to assess their behavior under various question-answering scenarios. This evaluation includes scenarios like RAG with a single context passage and RAG with two contradictory passages. The paper conducts a rigorous human evaluation to assess the correctness of the models' responses, resulting in the WikiContradict_HumanEval dataset .
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Improving LLM Performance: The research highlights the importance of prompting LLMs to pay attention to contradictory context information to enhance their performance in accurately answering questions involving conflicting passages. For instance, the top-performing model, Llama-3-70b-instruct, showed a significant increase in performance when dealing with explicit conflicts. This improvement underscores the potential for enhancing LLMs' capabilities in handling real-world knowledge conflicts .
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Complexity of Knowledge Conflicts: The paper emphasizes the complexity and nuance of real-world knowledge conflicts, which go beyond explicit, surface-level contradictions. By focusing on "real-world inter-context conflicts," where inconsistencies arise from retrieved passages from a single trusted source like Wikipedia, the study aims to provide insights into how LLMs behave when confronted with such challenges .
In summary, the paper introduces the WikiContradict benchmark, evaluates LLMs' performance on real-world knowledge conflicts, and underscores the importance of addressing inter-context conflicts to enhance the accuracy of LLM responses in handling contradictory information . The WikiContradict benchmark proposed in the paper "WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia" introduces several key characteristics and advantages compared to previous methods .
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Focus on Real-World Inter-Context Conflicts: Unlike previous benchmarks that primarily concentrate on explicit, surface-level contradictions, WikiContradict delves into the complexity of real-world knowledge conflicts. It specifically targets "real-world inter-context conflicts," where inconsistencies arise from retrieved passages from a single trusted source like Wikipedia. This focus on genuine contradictions enhances the benchmark's relevance and applicability to real-world scenarios .
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High-Quality Human-Annotated Instances: The WikiContradict benchmark comprises 253 high-quality, human-annotated instances covering various types of contradictions identified by Wikipedia editors. These instances are meticulously validated to ensure accuracy and reliability, providing a robust dataset for evaluating LLMs' performance in handling conflicting information .
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Diverse Prompt Templates for Evaluation: The paper employs diverse prompt templates to assess LLMs' behavior under different question-answering scenarios, including scenarios like RAG with a single context passage and RAG with two contradictory passages. This comprehensive evaluation approach allows for a nuanced analysis of LLMs' responses to conflicting contexts, enhancing the benchmark's effectiveness in capturing the models' capabilities .
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Rigorous Human Evaluation: The study conducts a rigorous human evaluation to assess the correctness of the models' responses on the WikiContradict benchmark. This evaluation involves responses from five LLMs to five prompt templates, resulting in a total of 1,375 evaluation samples. The human evaluation dataset, WikiContradict_HumanEval, consists of 1,200 samples after resolving annotation disagreements among annotators. This meticulous evaluation process ensures the reliability and validity of the benchmark results .
In summary, the WikiContradict benchmark stands out for its focus on real-world inter-context conflicts, high-quality human annotation, diverse evaluation scenarios, and rigorous human evaluation, offering a comprehensive and robust framework for assessing LLMs' performance in handling knowledge conflicts .
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?
In the field of evaluating large language models (LLMs) on real-world knowledge conflicts, there are several related research works and notable researchers:
- Related Research: One significant work is the "WikiContradict" benchmark, which challenges current LLMs by presenting contradictions extracted from Wikipedia articles . This benchmark aims to assess how well LLMs handle real-world inter-context conflicts, focusing on contradictions from trusted sources like Wikipedia .
- Noteworthy Researchers: Notable researchers in this field include Q. Cheng, T. Sun, W. Zhang, S. Wang, X. Liu, M. Zhang, J. He, M. Huang, Z. Yin, K. Chen, and X. Qiu . Additionally, Z. Jin, P. Cao, Y. Chen, K. Liu, X. Jiang, J. Xu, L. Qiuxia, and J. Zhao have explored and resolved knowledge conflicts in retrieval-augmented language models .
- Key Solution: The key solution mentioned in the paper involves prompting LLMs to pay attention to contradictory context information, which significantly improves their performance in accurately answering questions that involve conflicts . By training LLMs to handle contradictions, particularly inter-context conflicts, researchers aim to enhance the models' ability to manage real-world knowledge conflicts effectively .
How were the experiments in the paper designed?
The experiments in the paper were designed to evaluate the performance of various LLMs on the WikiContradict benchmark. The experiments involved employing diverse prompt templates to assess the behavior of the models under different question answering scenarios, including RAG with a single context passage and RAG with two contradictory passages . Subsequently, a rigorous human evaluation was conducted to assess the correctness of the models' responses. The human evaluation dataset comprised responses from 5 LLMs to 5 prompt templates, applied to 55 instances from the WikiContradict dataset, resulting in a total of 1,375 evaluation samples. Each sample was annotated by 2 authors of the paper, resulting in 2,750 human judgments .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is called WikiContradict. It consists of 253 high-quality, human-annotated instances covering different types of contradictions identified by Wikipedia editors and validated by the researchers . The dataset was collected on February 26, 2024 .
Regarding the code, the study does not explicitly mention whether the code is open source or publicly available. The focus of the study is on evaluating LLMs on real-world knowledge conflicts using the WikiContradict benchmark . For specific details on the availability of the code, it would be advisable to refer to the original source or contact the authors of the study.
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 needed verification. The study focused on investigating the behaviors of Large Language Models (LLMs) when faced with "real-world inter-context conflicts" sourced from Wikipedia, where inconsistencies arise from the same or different retrieved passages considered equally credible . The WikiContradict benchmark, consisting of 253 high-quality, human-annotated instances, covered various types of contradictions identified by Wikipedia editors and validated by the researchers .
The study evaluated the performance of different LLMs on the WikiContradict dataset using diverse prompt templates to assess their behavior under various question-answering scenarios, including RAG with a single context passage and RAG with two contradictory passages . A rigorous human evaluation was conducted to assess the correctness of the models' responses, resulting in a dataset with 1,200 samples from 5 LLMs' responses to 48 WikiContradict instances based on 5 prompt templates . The inter-annotator agreement ranged from moderate to substantial, indicating a reliable evaluation process .
Overall, the study's methodology, which included diverse prompt templates, human evaluation, and a focus on real-world inter-context conflicts, provided a robust framework for testing and verifying the hypotheses related to LLMs' performance in handling knowledge conflicts sourced from Wikipedia .
What are the contributions of this paper?
The contributions of this paper include the development of the WikiContradict benchmark, which challenges current Large Language Models (LLMs) by presenting real-world inter-context conflicts extracted from Wikipedia . This benchmark aims to assess LLMs' performance in handling contradictions and understanding complex scenarios, particularly focusing on inter-context conflicts . The paper also introduces a human evaluation dataset, WikiContradict_HumanEval, comprising responses from 5 LLMs to 5 prompt templates applied to 55 instances from the WikiContradict dataset, resulting in a total of 1,375 evaluation samples . Additionally, the study evaluates the performance of various LLMs using diverse prompt templates to assess their behavior under different question answering scenarios, including RAG with a single context passage and RAG with two contradictory passages .
What work can be continued in depth?
Further research can be conducted to delve deeper into the evaluation of large language models (LLMs) in handling real-world inter-context conflicts, specifically focusing on the capability of models to understand and manage knowledge conflicts . This includes exploring the nuances of different types of contradictions, such as intra-memory, context-memory, and inter-context conflicts, to ensure the trustworthiness of LLM responses . Additionally, there is an opportunity to investigate how LLMs perform in dealing with real-world scenarios, particularly in managing inter-context conflicts extracted from sources like Wikipedia . This research can shed light on the complexities of real-world conflicts and enhance our understanding of LLM behavior and capability in handling contradictory information .