Dialectic Preference Bias in Large Language Models
Bibliographic record
Abstract
Dialectic preference is an often overlooked language model's (LLM) bias against marginalized groups. It can be observed When LLMs output reflects or promotes unfair preferences or prejudices towards particular dialects or linguistic variations. Such bias may lead the model to favor certain ways of speaking or writing, which can disadvantage speakers of marginalized dialects. Such bias can perpetuate social biases and inequalities, affecting how people interact with and are supported by AI technologies. In this preliminary study, we analyze dialectic preference bias for Standard American English (SAE) compared to African American English (AAE) using the sentiment classification task on Claude 3 Haiku, Phi-3-medium, and LLaMa 3.1 8b. We find a greater tendency to classify AAE sentiments as negative, especially in LLaMa 3.1 8b, compared to other models, demonstrating the presence of dialectic preference bias. This work highlights the importance of addressing dialectic language-based biases in LLMs to reach inclusive and equitable LLMs. We plan to extend this study to more dialects and larger language models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".