Summary of A Comprehensive Survey Of Bias in Llms: Current Landscape and Future Directions, by Rajesh Ranjan et al.
A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions
by Rajesh Ranjan, Shailja Gupta, Surya Narayan Singh
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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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 Large Language Models (LLMs) have transformed various natural language processing applications by providing exceptional text generation, translation, and comprehension capabilities. However, their widespread deployment has highlighted significant concerns regarding biases embedded within these models. This comprehensive survey categorizes biases into several dimensions, synthesizing current research findings and discussing implications for real-world applications. The survey also critically assesses existing bias mitigation techniques and proposes future research directions to enhance fairness and equity in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are powerful tools that can generate text, translate languages, and understand what we say. But some people are worried because these models can be biased against certain groups of people or ideas. This survey looks at all the different types of bias, where they come from, how they affect us, and how to fix them. It’s a big review of everything that’s been learned about bias in these models so far. |
Keywords
» Artificial intelligence » Natural language processing » Text generation » Translation
