Summary of Cobias: Contextual Reliability in Bias Assessment, by Priyanshul Govil et al.
COBIAS: Contextual Reliability in Bias Assessment
by Priyanshul Govil, Hemang Jain, Vamshi Krishna Bonagiri, Aman Chadha, Ponnurangam Kumaraguru, Manas Gaur, Sanorita Dey
First submitted to arxiv on: 22 Feb 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 This paper introduces a novel framework for evaluating the robustness of Large Language Models (LLMs) against biased statements, considering contextual considerations that existing methods lack. The proposed Context-Oriented Bias Indicator and Assessment Score (COBIAS) measures the reliability of biased statements in detecting bias based on the variance in model behavior across different contexts. To evaluate COBIAS, the authors augment stereotyped statements from two benchmark datasets with contextual information, showing alignment with human judgment on contextual reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can pick up biases from the data they’re trained on. Right now, we don’t have good ways to test these models for bias or fix them when they are biased. This paper introduces a new way to do this by considering the situations where biased statements might appear. It also creates a score called COBIAS that measures how well a model does at detecting bias in different contexts. The authors tested COBIAS and found that it agrees with human judgment about when a statement is reliable. |
Keywords
» Artificial intelligence » Alignment
