AAVENUE: Detecting LLM Biases on NLU Tasks in AAVE via a Novel Benchmark
This addresses the problem of bias detection in NLP systems for AAVE speakers, though it appears incremental as it builds upon existing benchmarks with a more flexible translation methodology.
The authors tackled the problem of dialect-induced performance discrepancies in natural language understanding by introducing AAVENUE, a benchmark for evaluating large language model performance on African American Vernacular English and Standard American English tasks, revealing that LLMs consistently perform better on SAE tasks than AAVE-translated versions.
Detecting biases in natural language understanding (NLU) for African American Vernacular English (AAVE) is crucial to developing inclusive natural language processing (NLP) systems. To address dialect-induced performance discrepancies, we introduce AAVENUE ({AAVE} {N}atural Language {U}nderstanding {E}valuation), a benchmark for evaluating large language model (LLM) performance on NLU tasks in AAVE and Standard American English (SAE). AAVENUE builds upon and extends existing benchmarks like VALUE, replacing deterministic syntactic and morphological transformations with a more flexible methodology leveraging LLM-based translation with few-shot prompting, improving performance across our evaluation metrics when translating key tasks from the GLUE and SuperGLUE benchmarks. We compare AAVENUE and VALUE translations using five popular LLMs and a comprehensive set of metrics including fluency, BARTScore, quality, coherence, and understandability. Additionally, we recruit fluent AAVE speakers to validate our translations for authenticity. Our evaluations reveal that LLMs consistently perform better on SAE tasks than AAVE-translated versions, underscoring inherent biases and highlighting the need for more inclusive NLP models. We have open-sourced our source code on GitHub and created a website to showcase our work at https://aavenuee.github.io.