Summary of One Language, Many Gaps: Evaluating Dialect Fairness and Robustness Of Large Language Models in Reasoning Tasks, by Fangru Lin et al.
One Language, Many Gaps: Evaluating Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks
by Fangru Lin, Shaoguang Mao, Emanuele La Malfa, Valentin Hofmann, Adrian de Wynter, Xun Wang, Si-Qing Chen, Michael Wooldridge, Janet B. Pierrehumbert, Furu Wei
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This study addresses the issue of Large Language Models (LLMs) being biased against speakers of non-standard dialects, particularly African American Vernacular English (AAVE). The researchers present ReDial, a benchmark containing 1.2K+ parallel query pairs in Standardized English and AAVE, to evaluate the fairness and robustness of LLMs in canonical reasoning tasks. They hire AAVE speakers with computer science backgrounds to rewrite seven popular benchmarks, including HumanEval and GSM8K. The study finds that widely used LLMs, such as GPT, Claude, Llama, Mistral, and Phi models, show significant brittleness and unfairness to queries in AAVE. This work establishes a systematic framework for analyzing LLM bias and highlights the need for more inclusive language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is important because it shows that widely used language models are biased against speakers of non-standard dialects. The researchers created a new benchmark, ReDial, that tests how well these models can understand questions in African American Vernacular English (AAVE). They found that most models did not do well on this test and were unfair to people who speak AAVE. This is important because it means that language models are not treating everyone equally. |
Keywords
» Artificial intelligence » Claude » Gpt » Llama
