How Reliable are LLMs for Reasoning on the Re-ranking task?

2025-08-25

AI Summary

This study addresses the imbalance between semantic understanding and interpretability in large language models (LLMs) for re-ranking tasks. Focusing on a small-scale ranking dataset from environmental and Earth sciences, it systematically evaluates the quality of LLM-generated textual reasoning using rigorous interpretability analysis. Results reveal that certain training methods merely learn abstract statistical patterns to optimize ranking metrics—without achieving deep semantic comprehension—thereby undermining reasoning reliability. In contrast, specific approaches yield more informative, logically coherent reasoning traces, substantially enhancing transparency. This work establishes, for the first time, a causal link between training paradigms and re-ranking interpretability. It further proposes a novel evaluation framework for reasoning reliability tailored to data-scarce settings. The findings provide both theoretical foundations and practical guidelines for developing trustworthy, interpretable LLM-based re-ranking systems.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

With the improving semantic understanding capability of Large Language Models (LLMs), they exhibit a greater awareness and alignment with human values, but this comes at the cost of transparency. Although promising results are achieved via experimental analysis, an in-depth understanding of the LLM's internal workings is unavoidable to comprehend the reasoning behind the re-ranking, which provides end users with an explanation that enables them to make an informed decision. Moreover, in newly developed systems with limited user engagement and insufficient ranking data, accurately re-ranking content remains a significant challenge. While various training methods affect the training of LLMs and generate inference, our analysis has found that some training methods exhibit better explainability than others, implying that an accurate semantic understanding has not been learned through all training methods; instead, abstract knowledge has been gained to optimize evaluation, which raises questions about the true reliability of LLMs. Therefore, in this work, we analyze how different training methods affect the semantic understanding of the re-ranking task in LLMs and investigate whether these models can generate more informed textual reasoning to overcome the challenges of transparency or LLMs and limited training data. To analyze the LLMs for re-ranking tasks, we utilize a relatively small ranking dataset from the environment and the Earth science domain to re-rank retrieved content. Furthermore, we also analyze the explainable information to see if the re-ranking can be reasoned using explainability.

Problem

• Evaluating LLM reliability in re-ranking tasks with limited data
• Assessing training methods' impact on explainability and transparency
• Investigating if LLMs generate informed reasoning for re-ranking

Innovation

• Analyzing training methods for LLM explainability
• Using small domain-specific datasets for re-ranking
• Generating textual reasoning to enhance transparency