LLMThinkBench: Towards Basic Math Reasoning and Overthinking in Large Language Models

• URL: http://arxiv.org/abs/2507.04023v1
• Date: Sat, 05 Jul 2025 12:31:17 GMT
• Title: LLMThinkBench: Towards Basic Math Reasoning and Overthinking in Large Language Models
• Authors: Gaurav Srivastava, Aafiya Hussain, Sriram Srinivasan, Xuan Wang,
• Abstract summary: Large Language Models (LLMs) have achieved remarkable performance on complex mathematical benchmarks.<n>LLMs often struggle with simple arithmetic tasks and exhibit a tendency toward over-explaining or "overthinking" answers.<n>The framework provides 14 math tasks with randomized test data generation and robust parsing strategies.<n>Users can extend the tool with custom tasks, reproduce experiments with seeding, and generate detailed efficiency reports.
• Score: 4.370731670976415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have achieved remarkable performance on complex mathematical benchmarks, yet often struggle with simple arithmetic tasks and exhibit a tendency toward over-explaining or "overthinking" answers. To systematically assess this phenomenon, we introduce LLMThinkBench, a modular benchmarking framework that enables researchers to evaluate basic math reasoning and overthinking in LLMs. The framework provides 14 configurable math tasks with randomized test data generation and robust parsing strategies. Researchers can quantify overthinking using our Overthinking Score metric, which captures accuracy-verbosity tradeoffs through harmonic mean formulation. The tool offers flexible evaluation with a scalable vLLM/Transformers backend, multi-GPU support, and full configurability. Users can extend the tool with custom tasks, reproduce experiments with seeding, and generate detailed efficiency reports. Distributed as a pip-installable package with CLI and API access, LLMThinkBench provides researchers and practitioners an accessible, cost-effective alternative to expensive LLM-as-a-judge methods for diagnosing basic reasoning capabilities and efficiency analysis. Package can be installed as: pip install llmthinkbench

Related papers

• Computational Thinking Reasoning in Large Language Models [69.28428524878885]
  Computational Thinking Model (CTM) is a novel framework that incorporates computational thinking paradigms into large language models (LLMs)<n>Live code execution is seamlessly integrated into the reasoning process, allowing CTM to think by computing.<n>CTM outperforms conventional reasoning models and tool-augmented baselines in terms of accuracy, interpretability, and generalizability.
  arXiv  Detail & Related papers  (2025-06-03T09:11:15Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• SIMCOPILOT: Evaluating Large Language Models for Copilot-Style Code Generation [5.880496520248658]
  SIMCOPILOT is a benchmark that simulates the role of large language models (LLMs) as interactive, "copilot"-style coding assistants.<n>The benchmark comprises dedicated sub-benchmarks for Java (SIMCOPILOTJ) and Python.
  arXiv  Detail & Related papers  (2025-05-21T04:59:44Z)
• Computational Reasoning of Large Language Models [51.629694188014064]
  We introduce textbfTuring Machine Bench, a benchmark to assess the ability of Large Language Models (LLMs) to execute reasoning processes.<n> TMBench incorporates four key features: self-contained and knowledge-agnostic reasoning, a minimalistic multi-step structure, controllable difficulty, and a theoretical foundation based on Turing machine.
  arXiv  Detail & Related papers  (2025-04-29T13:52:47Z)
• Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving [55.895917967408586]
  Existing approaches to mathematical reasoning with large language models rely on Chain-of-Thought (CoT) for generalizability or Tool-Integrated Reasoning (TIR) for precise computation.<n>We propose TATA (Teaching LLMs According to Their Aptitude), an adaptive framework that enables LLMs to personalize their reasoning strategy spontaneously.
  arXiv  Detail & Related papers  (2025-02-17T16:56:23Z)
• Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox [46.39670209441478]
  Large language models (LLMs) have exhibited exciting progress in multiple scenarios. As an effective means to reduce memory footprint and inference cost, quantization also faces challenges in performance degradation at low bit-widths. This work provides a comprehensive benchmark suite for this research topic, including an evaluation system, detailed analyses, and a general toolbox.
  arXiv  Detail & Related papers  (2024-06-15T12:02:14Z)
• Efficient Tool Use with Chain-of-Abstraction Reasoning [63.08202389132155]
  Large language models (LLMs) need to ground their reasoning to real-world knowledge.<n>There remains challenges for fine-tuning LLM agents to invoke tools in multi-step reasoning problems.<n>We propose a new method for LLMs to better leverage tools in multi-step reasoning.
  arXiv  Detail & Related papers  (2024-01-30T21:53:30Z)
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets [75.64181719386497]
  We present CRAFT, a tool creation and retrieval framework for large language models (LLMs) It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks. Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
  arXiv  Detail & Related papers  (2023-09-29T17:40:26Z)
• ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models [32.95155349925248]
  We propose a modular paradigm ReWOO that detaches the reasoning process from external observations, thus significantly reducing token consumption. We show that ReWOO achieves 5x token efficiency and 4% accuracy improvement on HotpotQA, a multi-step reasoning benchmark. Our illustrative work offloads reasoning ability from 175B GPT3.5 into 7B LLaMA, demonstrating the significant potential for truly efficient and scalable ALM systems.
  arXiv  Detail & Related papers  (2023-05-23T00:16:48Z)

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.