Papers with generated reasoning
Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models (2025.naacl-long)
Copied to clipboard
Tongxuan Liu, Wenjiang Xu, Weizhe Huang, Yuting Zeng, Jiaxing Wang, Xingyu Wang, Hailong Yang, Jing Li
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory. |
| Approach: | They propose a propositional logic prompting method which generates expanded logical information descriptions and utilizes them as an additional augmentation to original contexts. |
| Outcome: | Extensive experiments show that Logic-of-Thought boosts the performance of various prompting methods with a striking margin across five logical reasoning tasks. |
Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter? (2024.emnlp-main)
Copied to clipboard
Nemika Tyagi, Mihir Parmar, Mohith Kulkarni, Aswin Rrv, Nisarg Patel, Mutsumi Nakamura, Arindam Mitra, Chitta Baral
| Challenge: | Existing studies evaluate only the final predicted answer of a puzzle, without providing any finer metrics to evaluate them. |
| Approach: | They propose to use a grid-based evaluation dataset to evaluate LLMs' reasoning abilities and a new error taxonomy to evaluate their reasoning chains. |
| Outcome: | The proposed model outperforms existing prompting methods on a wide range of natural language understanding tasks previously thought to be exclusive to humans. |
AG-GRPO: Answer-Guided GRPO for Masked Diffusion Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Recent work on large language models (LLMs) has emphasized not only final-answer accuracy but also reliability of reasoning on challenging tasks. |
| Approach: | They propose an answer-guided group-relative policy optimization for masked diffusion language models which generates text through iterative mangled token restoration. |
| Outcome: | The proposed approach improves over pretrained dLLMs and prior RL methods across mathematics, puzzle-solving, and code-generation benchmarks. |