AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models

• URL: http://arxiv.org/abs/2502.16906v1
• Date: Mon, 24 Feb 2025 07:02:31 GMT
• Title: AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models
• Authors: Qin Zhu, Fei Huang, Runyu Peng, Keming Lu, Bowen Yu, Qinyuan Cheng, Xipeng Qiu, Xuanjing Huang, Junyang Lin,
• Abstract summary: We propose an automated method for synthesizing open-ended logic puzzles, and use it to develop a bilingual benchmark, AutoLogi.<n>Our approach features program-based verification and controllable difficulty levels, enabling more reliable evaluation that better distinguishes models' reasoning abilities.
• Score: 86.83875864328984
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While logical reasoning evaluation of Large Language Models (LLMs) has attracted significant attention, existing benchmarks predominantly rely on multiple-choice formats that are vulnerable to random guessing, leading to overestimated performance and substantial performance fluctuations. To obtain more accurate assessments of models' reasoning capabilities, we propose an automated method for synthesizing open-ended logic puzzles, and use it to develop a bilingual benchmark, AutoLogi. Our approach features program-based verification and controllable difficulty levels, enabling more reliable evaluation that better distinguishes models' reasoning abilities. Extensive evaluation of eight modern LLMs shows that AutoLogi can better reflect true model capabilities, with performance scores spanning from 35% to 73% compared to the narrower range of 21% to 37% on the source multiple-choice dataset. Beyond benchmark creation, this synthesis method can generate high-quality training data by incorporating program verifiers into the rejection sampling process, enabling systematic enhancement of LLMs' reasoning capabilities across diverse datasets.

Related papers

• Can Large Language Models Unveil the Mysteries? An Exploration of Their Ability to Unlock Information in Complex Scenarios [9.761316172913016]
  We explore the ability of advanced models to integrate multiple inputs for reasoning in complex scenarios. We present three plug-and-play approaches: utilizing model input for reasoning, enhancing reasoning through minimum margin decoding, and retrieving semantically relevant data. Our approach improves the performance of models on reasoning, with a 22.17% boost on CVQA over the SOTA closed-source model.
  arXiv  Detail & Related papers  (2025-02-27T10:58:27Z)
• Scalable Best-of-N Selection for Large Language Models via Self-Certainty [65.31658824274894]
  Best-of-N selection is a key technique for improving the reasoning performance of Large Language Models. We propose self-certainty, a novel and efficient metric to estimate response quality without requiring external reward models. Our findings establish self-certainty as a practical and efficient way for improving LLM reasoning capabilities.
  arXiv  Detail & Related papers  (2025-02-25T19:08:07Z)
• Scoring Verifiers: Evaluating Synthetic Verification for Code and Reasoning [59.25951947621526]
  We propose an approach which can transform existing coding benchmarks into scoring and ranking datasets to evaluate the effectiveness of synthetic verifiers. We release four new benchmarks (HE-R, HE-R+, MBPP-R, and MBPP-R+), and analyzed synthetic verification methods with standard, reasoning-based, and reward-based LLMs. Our experiments show that reasoning can significantly improve test case generation and that scaling the number of test cases enhances the verification accuracy.
  arXiv  Detail & Related papers  (2025-02-19T15:32:11Z)
• EquiBench: Benchmarking Code Reasoning Capabilities of Large Language Models via Equivalence Checking [54.354203142828084]
  We present the task of equivalence checking as a new way to evaluate the code reasoning abilities of large language models.<n>We introduce EquiBench, a dataset of 2400 program pairs spanning four programming languages and six equivalence categories.<n>Our evaluation of 17 state-of-the-art LLMs shows that OpenAI o3-mini achieves the highest overall accuracy of 78.0%.
  arXiv  Detail & Related papers  (2025-02-18T02:54:25Z)
• BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning [78.63421517563056]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks. We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model. We introduce the Bootstrapping Reinforced Thinking Process (BRiTE) algorithm, which works in two steps.
  arXiv  Detail & Related papers  (2025-01-31T02:39:07Z)
• On the Adversarial Robustness of Instruction-Tuned Large Language Models for Code [4.286327408435937]
  We assess the impact of diverse input challenges on the functionality and correctness of generated code using rigorous metrics and established benchmarks.<n>Open-source models demonstrate an increased susceptibility to input perturbations, resulting in declines in functional correctness ranging from 12% to 34%.<n>In contrast, commercial models demonstrate relatively greater resilience, with performance degradation ranging from 3% to 24%.
  arXiv  Detail & Related papers  (2024-11-29T07:00:47Z)
• Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks [20.072783454089098]
  This paper presents AutoEval, a novel benchmark for scaling Large Language Model (LLM) assessment in formal tasks with clear notions of correctness. AutoEval is the first benchmarking paradigm that offers several key advantages necessary for scaling objective evaluation of LLMs without human labeling.
  arXiv  Detail & Related papers  (2024-10-11T00:56:37Z)
• Leveraging LLMs for Dialogue Quality Measurement [27.046917937460798]
  Large language models (LLMs) show robust zeroshot and few-shot capabilities across NLP tasks. Manipulating factors such as model size, in-context examples, and selection techniques, we examine "chain-of-thought" (CoT) reasoning and label extraction procedures. Our results indicate that LLMs that are suitably fine-tuned and have sufficient reasoning capabilities can be leveraged for automated dialogue evaluation.
  arXiv  Detail & Related papers  (2024-06-25T06:19:47Z)
• 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)
• Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning [25.496627355906966]
  We develop three new logical reasoning datasets named "ReClor-plus", "LogiQA-plus" and "LogiQAv2-plus"<n>Experiments show that these simple augmentations greatly hinder the models' performance.<n>Applying logic-driven data augmentation for fine-tuning and prompting can enhance generalisation in both discriminative and generative models.
  arXiv  Detail & Related papers  (2023-10-13T22:29:15Z)
• Discover, Explanation, Improvement: An Automatic Slice Detection Framework for Natural Language Processing [72.14557106085284]
  slice detection models (SDM) automatically identify underperforming groups of datapoints. This paper proposes a benchmark named "Discover, Explain, improve (DEIM)" for classification NLP tasks. Our evaluation shows that Edisa can accurately select error-prone datapoints with informative semantic features.
  arXiv  Detail & Related papers  (2022-11-08T19:00:00Z)

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.