Papers with LSAT
LTRAG: Enhancing Autoformalization and Self-refinement for Logical Reasoning with Thought-Guided RAG (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown promise in natural language reasoning, especially with techniques like chain-of-thought prompting. |
| Approach: | They propose a framework to enhance autoformalization and self-refinement for logical reasoning with Retrieval-Augmented Generation (RAG) by building knowledge bases of thought-guided examples. |
| Outcome: | The proposed framework outperforms Logic-LM and LINC on FOLIO and AR-LSAT, and achieves an accuracy gain of 13% over Logic LM and the proposed methods on GPT-4 and AR LSAT. |
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models (2024.findings-naacl)
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Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, Nan Duan
| Challenge: | Traditional benchmarks for evaluating foundation models often fail to accurately represent their general abilities for human-centric tasks. |
| Approach: | They propose a bilingual benchmark to assess foundation models in the context of human-centric standardized exams such as college entrance exams, law school admission tests, and math competitions. |
| Outcome: | The proposed benchmark exceeds the average human performance on SAT, LSAT, and math competitions with 95% accuracy and 92.5% on the Chinese college entrance English exam. |