Summary of Beads: Bias Evaluation Across Domains, by Shaina Raza and Mizanur Rahman and Michael R. Zhang
BEADs: Bias Evaluation Across Domains
by Shaina Raza, Mizanur Rahman, Michael R. Zhang
First submitted to arxiv on: 6 Jun 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 The paper introduces the Bias Evaluations Across Domains (BEADs) dataset, designed to support a wide range of natural language processing (NLP) tasks. The BEADs dataset is annotated by GPT4 for scalability and verified by experts to ensure high reliability. It provides data for both fine-tuning, including classification and language generation tasks, and for evaluating large language models (LLMs). The paper shows that BEADs effectively identifies biases when fine-tuned on the dataset, reduces biases while preserving language quality in language generation tasks, and reveals prevalent demographic biases in LLMs during evaluation. The results demonstrate the potential of BEADs to detect biases across various domains, supporting responsible AI development and application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent advancements in large language models (LLMs) have greatly enhanced natural language processing (NLP) applications. However, these models often inherit biases from their training data. To address this gap, a new dataset called BEADs is introduced. This dataset is designed to support various NLP tasks like text classification, token classification, bias quantification, and benign language generation. The findings indicate that BEADs effectively identifies biases when fine-tuned on the dataset and reduces biases while preserving language quality in language generation tasks. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Natural language processing » Nlp » Text classification » Token
