Investigating LLM's Problem Solving Capability -- a Study on Statics Questions

• URL: http://arxiv.org/abs/2606.26103v1
• Date: Thu, 30 Apr 2026 20:17:09 GMT
• Title: Investigating LLM's Problem Solving Capability -- a Study on Statics Questions
• Abstract summary: Large Language Models (LLMs) have rapidly influenced many aspects of society, particularly education.<n>This study adopts a model distillation process to evaluate LLM capabilities in solving statics problems.<n> Experimental results show that while LLMs perform well on text-only statics problems, their accuracy decreases when diagrams are introduced.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have rapidly influenced many aspects of society, particularly education, due to their demonstrated ability to complete assignments and examinations across a wide range of subjects. Although prior studies have examined the educational impact of LLMs, much of the existing work relies on public or open problem datasets and lacks topic-specific analysis. In engineering education, especially within mechanical engineering, systematic investigations of LLM performance on specific problem types remain limited. Instead of using traditional methods that directly ask textbook questions to an LLM tool, our study adopts a model distillation process to evaluate LLM capabilities in solving statics problems. By distilling ChatGPT, we extracted 25 text-only statics questions and further constructed two additional datasets by adding diagrams and modifying their numerical values. Experimental results show that while LLMs perform well on text-only statics problems, their accuracy decreases when diagrams are introduced and the problems require multi-step reasoning. Further analysis suggests that this performance drop is not primarily caused by limitations in image recognition, but rather by difficulties in multi-step reasoning and in consistently applying extracted visual information across successive solution stages.

Related papers

• ReliableMath: Benchmark of Reliable Mathematical Reasoning on Large Language Models [70.33764118171463]
  Large Language Models (LLMs) tend to fabricate unreliable responses when confronted with problems that are unsolvable or beyond their capability.<n>We develop a ReliableMath dataset which incorporates open-source solvable problems and high-quality unsolvable problems.<n>LLMs fail to directly identify unsolvable problems and always generate fabricated responses.
  arXiv  Detail & Related papers  (2025-07-03T19:19:44Z)
• Evaluating Large Language Models for Real-World Engineering Tasks [75.97299249823972]
  This paper introduces a curated database comprising over 100 questions derived from authentic, production-oriented engineering scenarios.<n>Using this dataset, we evaluate four state-of-the-art Large Language Models (LLMs)<n>Our results show that LLMs demonstrate strengths in basic temporal and structural reasoning but struggle significantly with abstract reasoning, formal modeling, and context-sensitive engineering logic.
  arXiv  Detail & Related papers  (2025-05-12T14:05:23Z)
• A Knapsack by Any Other Name: Presentation impacts LLM performance on NP-hard problems [64.05451567422342]
  We introduce the dataset of Everyday Hard Optimization Problems (EHOP), a collection of NP-hard problems expressed in natural language.<n>EHOP includes problem formulations that could be found in computer science textbooks (e.g., graph coloring), versions that are dressed up as problems that could arise in real life.<n>We find that state-of-the-art LLMs, across multiple prompting strategies, solve textbook problems more accurately than their real-life and inverted counterparts.
  arXiv  Detail & Related papers  (2025-02-19T14:39:59Z)
• Feasibility Study for Supporting Static Malware Analysis Using LLM [0.8057006406834466]
  Large language models (LLMs) are becoming more advanced and widespread. This study focuses on whether an LLM can be used to support static analysis.
  arXiv  Detail & Related papers  (2024-11-22T13:03:07Z)
• The LLM Effect: Are Humans Truly Using LLMs, or Are They Being Influenced By Them Instead? [60.01746782465275]
  Large Language Models (LLMs) have shown capabilities close to human performance in various analytical tasks. This paper investigates the efficiency and accuracy of LLMs in specialized tasks through a structured user study focusing on Human-LLM partnership.
  arXiv  Detail & Related papers  (2024-10-07T02:30:18Z)
• Adversarial Math Word Problem Generation [6.92510069380188]
  We propose a new paradigm for ensuring fair evaluation of large language models (LLMs) We generate adversarial examples which preserve the structure and difficulty of the original questions aimed for assessment, but are unsolvable by LLMs. We conduct experiments on various open- and closed-source LLMs, quantitatively and qualitatively demonstrating that our method significantly degrades their math problem-solving ability.
  arXiv  Detail & Related papers  (2024-02-27T22:07:52Z)
• Factuality of Large Language Models: A Survey [29.557596701431827]
  We critically analyze existing work with the aim to identify the major challenges and their associated causes. We analyze the obstacles to automated factuality evaluation for open-ended text generation.
  arXiv  Detail & Related papers  (2024-02-04T09:36:31Z)
• SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models [70.5763210869525]
  We introduce an expansive benchmark suite SciBench for Large Language Model (LLM) SciBench contains a dataset featuring a range of collegiate-level scientific problems from mathematics, chemistry, and physics domains. The results reveal that the current LLMs fall short of delivering satisfactory performance, with the best overall score of merely 43.22%.
  arXiv  Detail & Related papers  (2023-07-20T07:01:57Z)
• Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
  This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks. We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
  arXiv  Detail & Related papers  (2023-05-24T10:45:25Z)

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.