Summary of Eliciting Causal Abilities in Large Language Models For Reasoning Tasks, by Yajing Wang et al.
Eliciting Causal Abilities in Large Language Models for Reasoning Tasks
by Yajing Wang, Zongwei Luo, Jingzhe Wang, Zhanke Zhou, Yongqiang Chen, Bo Han
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed SCIE (Self-Causal Instruction Enhancement) method optimizes the prompting expressions used in large language models (LLMs) for downstream tasks. By eliciting the LLMs’ causal inference ability from prompting instructions to correct answers, SCIE enhances their reasoning performance while reducing training costs and increasing interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to use large language models (LLMs) better by helping them understand cause-and-effect relationships. The idea is to teach the LLMs to generate high-quality, low-quantity data that can be used to figure out why certain answers are correct. This helps improve their ability to reason and make connections between different pieces of information. By applying object-relational principles, this approach makes it possible to reuse these insights across different tasks without having to retrain the models. |
Keywords
» Artificial intelligence » Inference » Prompting
