| Challenge: | Existing large language models have limited abilities to solve deductive reasoning problems . performance differences between conditions do not improve overall performance . |
| Approach: | They investigate whether several large language models can solve a deductive reasoning problem in their conventional form. |
| Outcome: | The proposed models can solve a classic type of deductive reasoning problem in their conventional form. |
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| Challenge: | Reasoning is a fundamental aspect of human intelligence that plays a crucial role in many intellectual activities. |
| Approach: | They propose to improve LLMs' ability to elicit reasoning by providing exemplars or prompts to model reasoning. |
| Outcome: | This paper provides a comprehensive overview of the state of knowledge on reasoning in large language models. |
Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoning (2024.acl-long)
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| Challenge: | Recent advances in the domain of large language models (LLMs) have showcased their capability in executing deductive reasoning tasks. |
| Approach: | They examine inferential strategies employed by large language models through a detailed evaluation of their responses to propositional logic problems. |
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Is Large Language Model Performance on Reasoning Tasks Impacted by Different Ways Questions Are Asked? (2025.findings-acl)
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| Challenge: | Existing studies on Large Language Models (LLMs) have not investigated the impact of question types on LLM performance. |
| Approach: | They evaluate the performance of five Large Language Models on reasoning tasks . they use quantitative reasoning tasks and deductive reasoning tasks to evaluate the models . |
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A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
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Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
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| Challenge: | Current Large Language Models (LLMs) are unparalleled in their ability to generate grammatically correct, fluent text. |
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Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated their logical reasoning abilities across various domains. |
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Large Language Models for Mathematical Reasoning: Progresses and Challenges (2024.eacl-srw)
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| Challenge: | a survey examines the landscape of mathematical problem-solving techniques . large language models have proven to be potent assets in unraveling nuances of mathematical reasoning . |
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Do Large Language Models excel in Complex Logical Reasoning with Formal Language? (2025.emnlp-main)
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| Challenge: | Existing studies on LLMs have focused on formal language, but evaluations of their performance are limited. |
| Approach: | They propose to use a formal language to evaluate LLMs across logical reasoning problems using formal languages. |
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