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

• URL: http://arxiv.org/abs/2508.18444v1
• Date: Mon, 25 Aug 2025 19:48:39 GMT
• Title: How Reliable are LLMs for Reasoning on the Re-ranking task?
• Authors: Nafis Tanveer Islam, Zhiming Zhao,
• Abstract summary: We analyze how different training methods affect the semantic understanding of the re-ranking task in Large Language Models (LLMs)<n>In newly developed systems with limited user engagement and insufficient ranking data, accurately re-ranking content remains a significant challenge.
• Score: 3.282961543904818
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Reliable Response Generation in the Wild [11.058848731627233]
  Large language models (LLMs) have advanced information retrieval systems.<n>LLMs often face knowledge conflicts between internal memory and retrievaled external information.<n>We propose Swin-VIB, a novel framework that integrates a pipeline of variational information bottleneck models into adaptive augmentation of retrieved information.
  arXiv  Detail & Related papers  (2025-04-17T14:40:31Z)
• MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model [54.14155564592936]
  We propose a Mixture of Rule Experts guided by a Large Language Model (MoRE-LLM)<n>MoRE-LLM steers the discovery of local rule-based surrogates during training and their utilization for the classification task.<n>LLM is responsible for enhancing the domain knowledge alignment of the rules by correcting and contextualizing them.
  arXiv  Detail & Related papers  (2025-03-26T11:09:21Z)
• Enhancing LLM Knowledge Learning through Generalization [73.16975077770765]
  We show that an LLM's ability to continually predict the same factual knowledge tokens given diverse paraphrased contexts is positively correlated with its capacity to extract that knowledge via question-answering.<n>We propose two strategies to enhance LLMs' ability to predict the same knowledge tokens given varied contexts, thereby enhancing knowledge acquisition.
  arXiv  Detail & Related papers  (2025-03-05T17:56:20Z)
• Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering [66.5524727179286]
  NOVA is a framework designed to identify high-quality data that aligns well with the learned knowledge to reduce hallucinations.<n>It includes Internal Consistency Probing (ICP) and Semantic Equivalence Identification (SEI) to measure how familiar the LLM is with instruction data.<n>To ensure the quality of selected samples, we introduce an expert-aligned reward model, considering characteristics beyond just familiarity.
  arXiv  Detail & Related papers  (2025-02-11T08:05:56Z)
• UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models [41.67393607081513]
  Large Language Models (LLMs) often struggle to accurately express the factual knowledge they possess.<n>We propose the UAlign framework, which leverages Uncertainty estimations to represent knowledge boundaries.<n>We show that the proposed UAlign can significantly enhance the LLMs' capacities to confidently answer known questions.
  arXiv  Detail & Related papers  (2024-12-16T14:14:27Z)
• Dynamic Uncertainty Ranking: Enhancing Retrieval-Augmented In-Context Learning for Long-Tail Knowledge in LLMs [50.29035873837]
  Large language models (LLMs) can learn vast amounts of knowledge from diverse domains during pre-training.<n>Long-tail knowledge from specialized domains is often scarce and underrepresented, rarely appearing in the models' memorization.<n>We propose a reinforcement learning-based dynamic uncertainty ranking method for ICL that accounts for the varying impact of each retrieved sample on LLM predictions.
  arXiv  Detail & Related papers  (2024-10-31T03:42:17Z)
• A Reality check of the benefits of LLM in business [1.9181612035055007]
  Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks. This paper thoroughly examines the usefulness and readiness of LLMs for business processes.
  arXiv  Detail & Related papers  (2024-06-09T02:36:00Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs [0.5461938536945721]
  Large language models (LLMs) encapsulate a vast amount of factual information within their pre-trained weights. This knowledge is inherently limited, relying heavily on the characteristics of the training data. We compare two common approaches: unsupervised fine-tuning and retrieval-augmented generation.
  arXiv  Detail & Related papers  (2023-12-10T16:52:00Z)
• RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge [69.79676144482792]
  This study aims to evaluate the ability of LLMs to distinguish reliable information from external knowledge. Our benchmark consists of two tasks, Question Answering and Text Generation, and for each task, we provide models with a context containing counterfactual information.
  arXiv  Detail & Related papers  (2023-11-14T13:24:19Z)
• TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models [52.734140807634624]
  Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs. We introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
  arXiv  Detail & Related papers  (2023-10-10T16:38:49Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
  We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.