ConQRet: Benchmarking Fine-Grained Evaluation of Retrieval Augmented Argumentation with LLM Judges

Abstract: Computational argumentation, which involves generating answers or summaries for controversial topics like abortion bans and vaccination, has become increasingly important in today's polarized environment. Sophisticated LLM capabilities offer the potential to provide nuanced, evidence-based answers to such questions through Retrieval-Augmented Argumentation (RAArg), leveraging real-world evidence for high-quality, grounded arguments. However, evaluating RAArg remains challenging, as human evaluation is costly and difficult for complex, lengthy answers on complicated topics. At the same time, re-using existing argumentation datasets is no longer sufficient, as they lack long, complex arguments and realistic evidence from potentially misleading sources, limiting holistic evaluation of retrieval effectiveness and argument quality. To address these gaps, we investigate automated evaluation methods using multiple fine-grained LLM judges, providing better and more interpretable assessments than traditional single-score metrics and even previously reported human crowdsourcing. To validate the proposed techniques, we introduce ConQRet, a new benchmark featuring long and complex human-authored arguments on debated topics, grounded in real-world websites, allowing an exhaustive evaluation across retrieval effectiveness, argument quality, and groundedness. We validate our LLM Judges on a prior dataset and the new ConQRet benchmark. Our proposed LLM Judges and the ConQRet benchmark can enable rapid progress in computational argumentation and can be naturally extended to other complex retrieval-augmented generation tasks.

• The paper introduces the ConQRet benchmark, enabling fine-grained evaluation of argument quality by integrating web-sourced evidence.
• It proposes novel LLM judge methods that deliver detailed, multidimensional assessments of both retrieval effectiveness and argument cogency.
• Empirical evaluations validate the approach on legacy and new datasets, offering actionable insights for improving retrieval-augmented argumentation systems.

Fine-Grained Evaluation of Retrieval-Augmented Argumentation using LLM Judges: An Overview of ConQRet

The paper “ConQRet: Benchmarking Fine-Grained Evaluation of Retrieval Augmented Argumentation with LLM Judges” addresses the significant challenges in computational argumentation, particularly how to assess the quality and retrieval effectiveness of arguments generated using retrieval-augmented methods in a realistic and nuanced manner. As controversial topics, such as those revolving around public health measures and socio-political issues, become increasingly essential in today's discussions, the need for robust evaluation methods becomes evident.

Key Contributions

The authors identify several limitations in existing argumentation datasets and propose innovative evaluation methods to address the needs of realistic, nuanced discussions. Their contributions are outlined as follows:

1. Introduction of ConQRet Benchmark: The paper introduces ConQRet, a benchmark dataset featuring long and complex arguments sourced from contentious topics. This dataset enables comprehensive evaluations across multiple metrics, focusing on the integration of web-sourced evidence into arguments.
2. Development of LLM Judges: The authors propose novel fine-grained evaluation methods, employing LLMs as judges. These methods offer detailed evaluations across varying dimensions of argument and retrieval effectiveness, providing insights beyond simple aggregate scores.
3. Validation on Existing and New Datasets: The effectiveness of the proposed LLM Judges is validated using a prior dataset alongside the newly introduced ConQRet benchmark. This dual validation highlights the robustness of the judges in assessing both argument quality and retrieval accuracy.
4. Empirical Evaluation of Retrieval-Augmented Argumentation (RAArg): The paper thoroughly investigates RAArg systems, addressing the intrinsic complexities posed by retrieval and generation of argumentation, and examines various automated LLM-based evaluative measures.

Evaluation Approach and Findings

Evaluation Metrics: The work evaluates traditional metrics of context relevance, argument quality, and groundedness, emphasizing fine granularity and interpretability. These metrics are critical in analyzing the impact of evidence retrieval on argument cogency and effectiveness.

Context Relevance and Argument Groundedness: The study effectively demonstrates the deployment of different LLM-based methods to offer nuanced and detailed evaluations. Notably, it finds that fine-grained metrics provide more consistent and reliable assessments, especially in variable contexts with irrelevant content.

Implications: By enabling a more comprehensive evaluation procedure, the findings from the paper have significant implications for enhancing computational argumentation tasks. The ability to delineate specific elements affecting argument quality allows for more targeted improvements to argument retrieval and generation approaches.

Future Directions

The paper paves the way for several future research avenues:

• Extending Benchmarking Across Domains: Future work can explore extending benchmarks like ConQRet to other domains, enhancing the generalizability and applicability of retrieval-augmented argument generation (RAArg) systems.
• Refining LLM Judge Methodologies: There is room to further refine LLM-based evaluation techniques to improve sensitivity and reliability, particularly in contextually rich or ambiguous scenarios.
• Integration with Real-World Applications: Investigating the application of these methods in real-world discourse generation systems, particularly in socio-politically sensitive contexts, may yield valuable insights into improving public discourse.

Conclusion

This paper makes significant strides in bridging the gap between retrieval methodologies and computational argumentation. By introducing fine-grained evaluation metrics and a comprehensive benchmark, it sets a precedent for more nuanced automated evaluation systems. The insights derived from this research represent an essential step toward developing more capable and contextually aware argumentation systems, a crucial need in our increasingly information-driven society.