Summary of Causal Evaluation Of Language Models, by Sirui Chen et al.
Causal Evaluation of Language Models
by Sirui Chen, Bo Peng, Meiqi Chen, Ruiqi Wang, Mengying Xu, Xingyu Zeng, Rui Zhao, Shengjie Zhao, Yu Qiao, Chaochao Lu
First submitted to arxiv on: 1 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A comprehensive benchmark for evaluating the causal reasoning capabilities of language models, called CaLM, is introduced in this work. The CaLM framework defines a taxonomy consisting of four modules: causal target, adaptation, metric, and error, providing a broad evaluation design space. A dataset comprising 126,334 data samples is composed to provide curated sets of causal targets, adaptations, metrics, and errors. Extensive evaluations are conducted on 28 leading language models across various dimensions, yielding 50 high-level empirical findings that guide future language model development. The CaLM platform includes a website, leaderboards, datasets, and toolkits for supporting scalable assessments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causal reasoning in machines is important for achieving human-like intelligence. Language models have made great progress recently, but we don’t know if they can do causal reasoning yet. This paper introduces a new way to test language models’ ability to reason causally, called CaLM. It’s like a framework that helps us see how well the models are doing. The authors also created a big dataset of examples and tested 28 different models on it. They found out some interesting things about which models are good at causal reasoning and which aren’t. |
Keywords
» Artificial intelligence » Language model
