Assessing LLM Reasoning Steps via Principal Knowledge Grounding

• URL: http://arxiv.org/abs/2511.00879v1
• Date: Sun, 02 Nov 2025 10:25:43 GMT
• Title: Assessing LLM Reasoning Steps via Principal Knowledge Grounding
• Authors: Hyeon Hwang, Yewon Cho, Chanwoong Yoon, Yein Park, Minju Song, Kyungjae Lee, Gangwoo Kim, Jaewoo Kang,
• Abstract summary: Step-by-step reasoning has become a standard approach for large language models (LLMs) to tackle complex tasks.<n>We introduce a novel evaluation suite that systematically assesses the knowledge grounding of intermediate reasoning.
• Score: 22.194851964203128
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Step-by-step reasoning has become a standard approach for large language models (LLMs) to tackle complex tasks. While this paradigm has proven effective, it raises a fundamental question: How can we verify that an LLM's reasoning is accurately grounded in knowledge? To address this question, we introduce a novel evaluation suite that systematically assesses the knowledge grounding of intermediate reasoning. Our framework comprises three key components. (1) Principal Knowledge Collection, a large-scale repository of atomic knowledge essential for reasoning. Based on the collection, we propose (2) knowledge-grounded evaluation metrics designed to measure how well models recall and apply prerequisite knowledge in reasoning. These metrics are computed by our (3) evaluator LLM, a lightweight model optimized for cost-effective and reliable metric computation. Our evaluation suite demonstrates remarkable effectiveness in identifying missing or misapplied knowledge elements, providing crucial insights for uncovering fundamental reasoning deficiencies in LLMs. Beyond evaluation, we demonstrate how these metrics can be integrated into preference optimization, showcasing further applications of knowledge-grounded evaluation.

Related papers

• OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics [101.78963920333342]
  We introduce OpenUnlearning, a standardized framework for benchmarking large language models (LLMs) unlearning methods and metrics.<n>OpenUnlearning integrates 9 unlearning algorithms and 16 diverse evaluations across 3 leading benchmarks.<n>We also benchmark diverse unlearning methods and provide a comparative analysis against an extensive evaluation suite.
  arXiv  Detail & Related papers  (2025-06-14T20:16:37Z)
• Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness [46.653774740885275]
  Machine unlearning techniques aim to mitigate unintended memorization in large language models (LLMs)<n>We propose a knowledge unlearning evaluation framework that more accurately captures the implicit structure of real-world knowledge.<n>Our framework provides a more realistic and rigorous assessment of unlearning performance.
  arXiv  Detail & Related papers  (2025-06-06T04:35:19Z)
• How Reliable are LLMs as Knowledge Bases? Re-thinking Facutality and Consistency [60.25969380388974]
  Large Language Models (LLMs) are increasingly explored as knowledge bases (KBs)<n>Current evaluation methods focus too narrowly on knowledge retention, overlooking other crucial criteria for reliable performance.<n>We propose new criteria and metrics to quantify factuality and consistency, leading to a final reliability score.
  arXiv  Detail & Related papers  (2024-07-18T15:20:18Z)
• Evaluating Human Alignment and Model Faithfulness of LLM Rationale [66.75309523854476]
  We study how well large language models (LLMs) explain their generations through rationales. We show that prompting-based methods are less "faithful" than attribution-based explanations.
  arXiv  Detail & Related papers  (2024-06-28T20:06:30Z)
• Evaluating Mathematical Reasoning Beyond Accuracy [50.09931172314218]
  We introduce ReasonEval, a new methodology for evaluating the quality of reasoning steps.<n>We show that ReasonEval consistently outperforms baseline methods in the meta-evaluation datasets.<n>We observe that ReasonEval can play a significant role in data selection.
  arXiv  Detail & Related papers  (2024-04-08T17:18:04Z)
• EpiK-Eval: Evaluation for Language Models as Epistemic Models [16.485951373967502]
  We introduce EpiK-Eval, a novel question-answering benchmark tailored to evaluate LLMs' proficiency in formulating a coherent and consistent knowledge representation from segmented narratives. We argue that these shortcomings stem from the intrinsic nature of prevailing training objectives. The findings from this study offer insights for developing more robust and reliable LLMs.
  arXiv  Detail & Related papers  (2023-10-23T21:15:54Z)
• Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution [48.86322922826514]
  This paper defines a new task of Knowledge-aware Language Model Attribution (KaLMA) First, we extend attribution source from unstructured texts to Knowledge Graph (KG), whose rich structures benefit both the attribution performance and working scenarios. Second, we propose a new Conscious Incompetence" setting considering the incomplete knowledge repository. Third, we propose a comprehensive automatic evaluation metric encompassing text quality, citation quality, and text citation alignment.
  arXiv  Detail & Related papers  (2023-10-09T11:45:59Z)
• KoLA: Carefully Benchmarking World Knowledge of Large Language Models [87.96683299084788]
  We construct a Knowledge-oriented LLM Assessment benchmark (KoLA) We mimic human cognition to form a four-level taxonomy of knowledge-related abilities, covering $19$ tasks. We use both Wikipedia, a corpus prevalently pre-trained by LLMs, along with continuously collected emerging corpora, to evaluate the capacity to handle unseen data and evolving knowledge.
  arXiv  Detail & Related papers  (2023-06-15T17:20:46Z)

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.