One Language, Many Gaps: Evaluating Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks (2410.11005v1)

Abstract: Language is not monolithic. While many benchmarks are used as proxies to systematically estimate LLMs' (LLM) performance in real-life tasks, they tend to ignore the nuances of within-language variation and thus fail to model the experience of speakers of minority dialects. Focusing on African American Vernacular English (AAVE), we present the first study on LLMs' fairness and robustness to a dialect in canonical reasoning tasks (algorithm, math, logic, and comprehensive reasoning). We hire AAVE speakers, including experts with computer science backgrounds, to rewrite seven popular benchmarks, such as HumanEval and GSM8K. The result of this effort is ReDial, a dialectal benchmark comprising $1.2K+$ parallel query pairs in Standardized English and AAVE. We use ReDial to evaluate state-of-the-art LLMs, including GPT-4o/4/3.5-turbo, LLaMA-3.1/3, Mistral, and Phi-3. We find that, compared to Standardized English, almost all of these widely used models show significant brittleness and unfairness to queries in AAVE. Furthermore, AAVE queries can degrade performance more substantially than misspelled texts in Standardized English, even when LLMs are more familiar with the AAVE queries. Finally, asking models to rephrase questions in Standardized English does not close the performance gap but generally introduces higher costs. Overall, our findings indicate that LLMs provide unfair service to dialect users in complex reasoning tasks. Code can be found at https://github.com/fangru-lin/redial_dialect_robustness_fairness.git.

• The paper demonstrates significant performance drops in LLMs when processing AAVE queries compared to Standardized English benchmarks.
• It introduces ReDial, a novel dataset of over 1,200 parallel query pairs across four reasoning tasks, ensuring semantic equivalence in dialectal translations.
• Findings highlight the need for training innovations to address fairness and robustness challenges in AI language models handling dialectal variations.

Evaluating Dialect Fairness and Robustness of LLMs in Reasoning Tasks

The paper, "One Language, Many Gaps: Evaluating Dialect Fairness and Robustness of LLMs in Reasoning Tasks," addresses an essential gap in the evaluation of LLMs: their performance when encountering dialects, specifically African American Vernacular English (AAVE), in reasoning tasks. Current benchmarks typically use Standardized English, overlooking dialectal variations that represent real-world language use. This oversight can lead to biases and reduced performance when LLMs interact with queries in non-standard dialects, which this paper seeks to investigate through the creation of a novel benchmark dataset named ReDial.

Methodology and Dataset Creation

To explore the issue of dialect fairness, the authors assembled a team of AAVE speakers, including those with computer science expertise, to rewrite instances from seven well-known benchmarks like HumanEval and GSM8K into AAVE. This translation process was meticulously validated to ensure the rewrite preserved the semantic intent while sounding natural in AAVE. The product of these efforts, ReDial, consists of over 1,200 parallel query pairs in both Standardized English and AAVE, covering four primary reasoning tasks: algorithmic, mathematical, logical, and comprehensive reasoning.

Key Findings and Numerical Results

The evaluation of state-of-the-art LLMs using ReDial revealed significant findings:

• Performance Discrepancies: Most LLMs, including GPT-4 and even large-scale models like LLaMA-3.1-70B-Instruct, showed a marked reduction in performance when processing AAVE queries compared to their Standardized English counterparts. For instance, performance drops of approximately 0.072 in pass rates were observed. The models had particular difficulty handling algorithmic and comprehensive tasks when phrased in AAVE.
• Robustness to Variants: Comparison with misspelled Standardized English texts demonstrated higher brittleness towards dialectal input. Despite having equivalent semantic content, LLMs struggled more with AAVE, indicating that simple data augmentation strategies may not be an effective solution.
• Standardization Attempts: Attempts to standardize AAVE inputs within models did not eliminate the performance gaps and often incurred increased computational costs, further highlighting unfair service discrepancies.

Implications and Future Developments

The implications of this paper are significant. It underscores the necessity for developing models that can handle the linguistic diversity of actual users. The findings advocate for a reevaluation of training data, potentially involving more comprehensive dialectal input without relying solely on quantity increases. Furthermore, addressing inherent model biases requires architectural innovations and novel training methodologies that prioritize fairness across dialects.

In future AI developments, research could explore the underlying causes of this brittleness more deeply, potentially incorporating linguistic insights from dialectology and sociolinguistics. It also calls for broader benchmarking practices that include a wider spectrum of dialects and languages, ensuring that LLMs provide equitable service to all linguistic demographics.

Overall, this paper critically demonstrates the inadequacy of current LLMs in dealing with non-standard dialects, providing a robust and systematic dataset for future evaluation and improvement of language technology to ensure fairness and robustness in AI systems.