Summary of Using Counterfactual Tasks to Evaluate the Generality Of Analogical Reasoning in Large Language Models, by Martha Lewis and Melanie Mitchell
Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models
by Martha Lewis, Melanie Mitchell
First submitted to arxiv on: 14 Feb 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 investigates the generality of large language models (LLMs) in performing abstract reasoning tasks, specifically analogy-making. Researchers created a set of “counterfactual” variants that test similar abstract reasoning abilities but are dissimilar to pre-training data. Comparing human and LLM performance on both original and counterfactual problems reveals that humans excel on all tasks while GPT models’ performance drops sharply on the counterfactual set, indicating a lack of robustness and generality in LLM analogy-making capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies if large language models can do abstract thinking like people. It makes special problems to test this called “counterfactual” versions. These new problems are different from what the computer saw before. The results show that humans can solve all kinds of problems, but the computer’s performance drops when it faces these new problems, which means it can’t really think abstractly. |
Keywords
* Artificial intelligence * Gpt
