Summary of A Critical Review Of Causal Reasoning Benchmarks For Large Language Models, by Linying Yang et al.
A Critical Review of Causal Reasoning Benchmarks for Large Language Models
by Linying Yang, Vik Shirvaikar, Oscar Clivio, Fabian Falck
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 comprehensive review of Large Language Model (LLM) benchmarks for causality, highlighting how recent benchmarks have moved towards a more thorough definition of causal reasoning by incorporating interventional or counterfactual reasoning. The authors derive a set of criteria that a useful benchmark or set of benchmarks should aim to satisfy, aiming to pave the way towards a general framework for assessing causal understanding in LLMs and designing novel benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well Large Language Models can figure out cause-and-effect relationships. It shows that many tests for this ability are too easy because they just require the model to look up information, rather than actually reasoning about causes and effects. The authors give a summary of what’s been tried so far in testing LLMs for causality, and then suggest some criteria that a good test should meet. |
Keywords
» Artificial intelligence » Large language model
