Summary of P-folio: Evaluating and Improving Logical Reasoning with Abundant Human-written Reasoning Chains, by Simeng Han et al.
P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains
by Simeng Han, Aaron Yu, Rui Shen, Zhenting Qi, Martin Riddell, Wenfei Zhou, Yujie Qiao, Yilun Zhao, Semih Yavuz, Ye Liu, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Dragomir Radev, Rex Ying, Arman Cohan
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a new dataset, P-FOLIO, for evaluating large-language-model (LLM) reasoning capabilities in first-order logic problems. The dataset consists of diverse and complex reasoning chains written by humans, with an annotation protocol that facilitates well-structured natural language proofs. The authors evaluate LLMs at a fine granularity via single-step inference rule classification, showing that human-written reasoning chains significantly boost the logical reasoning capabilities of LLMs through many-shot prompting and fine-tuning. They also find that fine-tuning on P-FOLIO improves model performance by 10% or more on other out-of-domain datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a new way to test how well artificial intelligence models can understand logical reasoning problems. Right now, we don’t have a good way to do this because the current methods aren’t very effective. The authors created a special dataset called P-FOLIO that has lots of different and complex examples of logical reasoning problems, along with their solutions. They also made a special way for humans to annotate these problems so they can be used to test AI models. The paper shows that using human-written problem solutions makes the AI models much better at solving similar problems. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Inference » Large language model » Prompting
