Towards Effective Extraction and Evaluation of Factual Claims (2502.10855v2)

Abstract: A common strategy for fact-checking long-form content generated by LLMs is extracting simple claims that can be verified independently. Since inaccurate or incomplete claims compromise fact-checking results, ensuring claim quality is critical. However, the lack of a standardized evaluation framework impedes assessment and comparison of claim extraction methods. To address this gap, we propose a framework for evaluating claim extraction in the context of fact-checking along with automated, scalable, and replicable methods for applying this framework, including novel approaches for measuring coverage and decontextualization. We also introduce Claimify, an LLM-based claim extraction method, and demonstrate that it outperforms existing methods under our evaluation framework. A key feature of Claimify is its ability to handle ambiguity and extract claims only when there is high confidence in the correct interpretation of the source text.

• The paper introduces Claimify, a novel method for systematically extracting and evaluating factual claims from LLM outputs.
• It employs a multi-stage process—sentence splitting, selection, disambiguation, and decomposition—to ensure precise and self-contained claims.
• Experimental results on the BingCheck dataset demonstrate Claimify's superior performance in entailment, coverage, and decontextualization compared to existing methods.

Effective Extraction and Evaluation of Factual Claims

This essay provides a comprehensive exploration of the methods and techniques for extracting and evaluating factual claims from long-form content generated by LLMs. The paper focuses on addressing the gap in standardized methods for assessing claim extraction techniques, proposing new frameworks, and introducing a novel claim extraction method called Claimify. This essay details the various components and methodologies presented in the paper.

Introduction to Claim Extraction

The paper begins with the premise that LLMs often produce content that may not be grounded in external sources, necessitating reliable fact-checking systems. A common strategy is to decompose complex outputs into simpler claims, verify these individually, and base conclusions on their collective assessment. However, the efficacy of such systems depends on the quality of these extracted claims.

The paper identifies the absence of a standardized framework for evaluating claim extraction methods and proposes new methodologies for robust evaluation. This includes novel approaches to measuring factors like the coverage of claims and their decontextualization—a crucial step given the context-sensitive nature of much factual data.

Key Concepts for Claim Evaluation

The paper posits that claim extraction should be evaluated based on three metrics:

1. Entailment: Ensures that if the original text is true, then the extracted claims must also be true. This is foundational to the faithfulness of the extraction process.
2. Coverage: Involves extracting all verifiable information while avoiding unverifiable content. This dual requirement ensures both completeness and precision in representing the source material.
3. Decontextualization: Claims should be self-contained and retain their original meaning even when isolated. This ensures claims can stand independently for verification purposes.

Furthermore, the paper challenges the utility of atomicity in claims, which refers to breaking down claims into their simplest truths, as it does not consistently enhance verification performance.

Claimify: A Novel Method

Claimify is introduced as a new method for extracting claims from text, utilizing LLMs to address the nuances of claim extraction uniquely. The paper highlights several stages in Claimify’s process:

• Sentence Splitting: Utilizes Natural Language Toolkit (NLTK) to break text into sentences for more precise processing.
• Selection: Use of LLMs to identify sentences with verifiable content and filter out those without, ensuring the system focuses on relevant data.
• Disambiguation: Unlike other systems, Claimify identifies ambiguity in text—both referential and structural—and addresses whether these can be resolved based on context and prior information.
• Decomposition: The final stage, where selected and disambiguated sentences are broken down into factual claims, allowing for detailed analysis and verification.

Claimify’s robust handling of ambiguity is noted as a significant advancement over existing claim extraction methods, which often overlook such nuances.

Experimental Evaluation and Results

The performance of Claimify was assessed against five other methods using the BingCheck dataset—a comprehensive benchmark for testing long-form answer generation in a real-world setting.

Entailment: Claimify achieved a near-perfect rate of entailed claims, indicating its strong reliability compared to other methods.

Coverage: Both sentence-level and element-level coverages were evaluated, with Claimify outperforming its peers, demonstrating its effective balance between precision and comprehensiveness.

Decontextualization: Claimify also led in ensuring that claims were sufficiently decontextualized, minimizing misinterpretations during factual verification.

Related Work and Conclusion

The paper situates its contributions within existing literature on claim detection, decomposition, and ambiguity handling, highlighting where Claimify offers novel enhancements, particularly in the domain of ambiguity management in complex LLMs.

In conclusion, the paper advances the understanding and methodologies available for factual claim extraction and evaluation. By introducing Claimify, it sets the stage for more accurate and context-sensitive analysis in fact-checking systems, mitigating risks associated with ambiguous or incomplete information extraction from LLM outputs. The proposed framework and Claimify's unique handling of ambiguity and context omissions offer valuable improvements for ensuring the factual integrity of automatically generated content. Future work may explore further generalization and adaptation of these approaches across different datasets and AI models.