Summary of Bias in Large Language Models: Origin, Evaluation, and Mitigation, by Yufei Guo et al.
Bias in Large Language Models: Origin, Evaluation, and Mitigation
by Yufei Guo, Muzhe Guo, Juntao Su, Zhou Yang, Mengqiu Zhu, Hongfei Li, Mengyang Qiu, Shuo Shuo Liu
First submitted to arxiv on: 16 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 presents a comprehensive review of biases in Large Language Models (LLMs), discussing their origins, manifestations, and mitigation strategies. The authors categorize biases as intrinsic and extrinsic, analyzing their effects on various NLP tasks. They also evaluate different bias detection methods, including data-level, model-level, and output-level approaches, providing a toolkit for researchers to detect biases. Additionally, the paper explores pre-model, intra-model, and post-model mitigation techniques, highlighting their effectiveness and limitations. The authors discuss ethical and legal implications of biased LLMs in real-world applications such as healthcare and criminal justice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at big language models that can understand and generate human-like language. But these models have a problem – they can be biased. Biases can come from the data used to train the model or from the way the model is designed. The authors of this paper review what we know about biases in these models, how they affect different tasks like language translation and text summarization, and how we can detect and fix them. They also talk about why biased models are a problem, especially when it comes to using AI in things like healthcare and the justice system. |
Keywords
» Artificial intelligence » Nlp » Summarization » Translation
