Quantifying Logical Consistency in Transformers via Query-Key Alignment

• URL: http://arxiv.org/abs/2502.17017v1
• Date: Mon, 24 Feb 2025 10:02:50 GMT
• Title: Quantifying Logical Consistency in Transformers via Query-Key Alignment
• Authors: Eduard Tulchinskii, Anastasia Voznyuk, Laida Kushnareva, Andrei Andriiainen, Irina Piontkovskaya, Evgeny Burnaev, Serguei Barannikov,
• Abstract summary: We propose a novel, lightweight evaluation strategy for logical reasoning.<n>By computing a single forward pass and extracting a "QK-score" from carefully chosen heads, our method reveals latent representations that reliably separate valid from invalid inferences.
• Score: 20.636818928993684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated impressive performance in various natural language processing tasks, yet their ability to perform multi-step logical reasoning remains an open challenge. Although Chain-of-Thought prompting has improved logical reasoning by enabling models to generate intermediate steps, it lacks mechanisms to assess the coherence of these logical transitions. In this paper, we propose a novel, lightweight evaluation strategy for logical reasoning that uses query-key alignments inside transformer attention heads. By computing a single forward pass and extracting a "QK-score" from carefully chosen heads, our method reveals latent representations that reliably separate valid from invalid inferences, offering a scalable alternative to traditional ablation-based techniques. We also provide an empirical validation on multiple logical reasoning benchmarks, demonstrating improved robustness of our evaluation method against distractors and increased reasoning depth. The experiments were conducted on a diverse set of models, ranging from 1.5B to 70B parameters.

Related papers

• SEAL: Steerable Reasoning Calibration of Large Language Models for Free [58.190800043449336]
  Large Language Models (LLMs) have demonstrated compelling capabilities for complex reasoning tasks via the extended chain-of-thought (CoT) reasoning mechanism. Recent studies reveal substantial redundancy in the CoT reasoning traces, which negatively impacts model performance. We introduce SEAL, a training-free approach that seamlessly calibrates the CoT process, improving accuracy while demonstrating significant efficiency gains.
  arXiv  Detail & Related papers  (2025-04-07T02:42:07Z)
• Speculative Decoding for Multi-Sample Inference [21.64693536216534]
  We propose a novel speculative decoding method tailored for multi-sample reasoning scenarios. Our method exploits the intrinsic consensus of parallel generation paths to synthesize high-quality draft tokens.
  arXiv  Detail & Related papers  (2025-03-07T11:15:36Z)
• Efficient Reasoning with Hidden Thinking [48.96945580741641]
  Chain-of-Thought (CoT) reasoning has become a powerful framework for improving complex problem-solving capabilities.<n>We propose $textbfHeima$ (as hidden llama), an efficient reasoning framework that leverages reasoning CoTs at hidden latent space.<n>Heima model achieves higher generation efficiency while maintaining or even better zero-shot task accuracy.
  arXiv  Detail & Related papers  (2025-01-31T15:10:29Z)
• BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning [78.63421517563056]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks.<n>We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model.<n>We introduce the Bootstrapping Reinforced Thinking Process (BRiTE) algorithm, which works in two steps.
  arXiv  Detail & Related papers  (2025-01-31T02:39:07Z)
• Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models [9.689096888732642]
  We propose Logic-of-Thought (LoT) prompting which employs propositional logic to generate expanded logical information descriptions.<n>LoT boosts the performance of various prompting methods with a striking margin across five logical reasoning tasks.
  arXiv  Detail & Related papers  (2024-09-26T04:59:45Z)
• Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension [9.67774998354062]
  Previous research has primarily focused on enhancing logical reasoning capabilities through Chain-of-Thought (CoT) or data augmentation.<n>We propose a Premise-Oriented Data Augmentation (PODA) framework to generate CoT rationales including analyses for both correct and incorrect options.<n>We also introduce a novel thought-path contrastive learning method that compares reasoning paths between the original and counterfactual samples.
  arXiv  Detail & Related papers  (2024-09-22T15:44:43Z)
• Logic Agent: Enhancing Validity with Logic Rule Invocation [24.815341366820753]
  Chain-of-Thought prompting has emerged as a pivotal technique for augmenting the inferential capabilities of language models during reasoning tasks.<n>This paper introduces the Logic Agent (LA), an agent-based framework aimed at enhancing the validity of reasoning processes in Large Language Models.
  arXiv  Detail & Related papers  (2024-04-28T10:02:28Z)
• Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning [74.90592233107712]
  We propose a Direct-Indirect Reasoning (DIR) method, which considers Direct Reasoning (DR) and Indirect Reasoning (IR) as multiple parallel reasoning paths that are merged to derive the final answer.<n>Our DIR method is simple yet effective and can be straightforwardly integrated with existing variants of CoT methods.
  arXiv  Detail & Related papers  (2024-02-06T03:41:12Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models [74.40196814292426]
  We propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. GoT captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. We evaluate GoT's performance on a text-only reasoning task and a multimodal reasoning task.
  arXiv  Detail & Related papers  (2023-05-26T02:15:09Z)
• Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI [60.142926537264714]
  We introduce the methodology of Faithfulness-through-Counterfactuals. It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation. It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
  arXiv  Detail & Related papers  (2022-05-25T03:40:59Z)

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.