Summary of Llms For Generating and Evaluating Counterfactuals: a Comprehensive Study, by Van Bach Nguyen et al.
LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study
by Van Bach Nguyen, Paul Youssef, Christin Seifert, Jörg Schlötterer
First submitted to arxiv on: 26 Apr 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 Large Language Models (LLMs) have demonstrated impressive performance in various Natural Language Understanding (NLU) tasks, but their ability to generate high-quality counterfactuals (CFs), which flip a model’s prediction with minimal input changes, remains uncertain. This study investigates the efficacy of several common LLMs in generating CFs for two NLU tasks: Sentiment Analysis and Natural Language Inference. We conduct a comprehensive comparison of these LLMs, evaluating their CFs using both intrinsic metrics and the impact on data augmentation. Our results show that LLMs can generate fluent CFs but struggle to keep the induced changes minimal. Additionally, we analyze differences between human-generated and LLM-generated CFs, providing insights for future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that understand language really well. But they need help explaining why they make certain decisions. One way to do this is by generating counterfactuals (CFs), which show how a small change in the input can flip their prediction. This study looked at how well LLMs can generate CFs for two important tasks: judging if a sentence means the same thing as another, and figuring out if a statement is true or false. They compared several different LLMs to see which ones were best at generating CFs. The results show that while LLMs can make good CFs, they sometimes struggle to keep the changes small. This study also looked at how human-made CFs compare to those made by LLMs and found some interesting differences. |
Keywords
» Artificial intelligence » Data augmentation » Inference » Language understanding
