Bias in Large Language Models: Origin, Evaluation, and Mitigation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915971225092096 |
|---|---|
| author | Guo, Yufei Guo, Muzhe Su, Juntao Yang, Zhou Zhu, Mengqiu Li, Hongfei Qiu, Mengyang Liu, Shuo Shuo |
| author_facet | Guo, Yufei Guo, Muzhe Su, Juntao Yang, Zhou Zhu, Mengqiu Li, Hongfei Qiu, Mengyang Liu, Shuo Shuo |
| contents | Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. We categorize biases as intrinsic and extrinsic, analyzing their manifestations in various NLP tasks. The review critically assesses a range of bias evaluation methods, including data-level, model-level, and output-level approaches, providing researchers with a robust toolkit for bias detection. We further explore mitigation strategies, categorizing them into pre-model, intra-model, and post-model techniques, highlighting their effectiveness and limitations. Ethical and legal implications of biased LLMs are discussed, emphasizing potential harms in real-world applications such as healthcare and criminal justice. By synthesizing current knowledge on bias in LLMs, this review contributes to the ongoing effort to develop fair and responsible AI systems. Our work serves as a comprehensive resource for researchers and practitioners working towards understanding, evaluating, and mitigating bias in LLMs, fostering the development of more equitable AI technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10915 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bias in Large Language Models: Origin, Evaluation, and Mitigation Guo, Yufei Guo, Muzhe Su, Juntao Yang, Zhou Zhu, Mengqiu Li, Hongfei Qiu, Mengyang Liu, Shuo Shuo Computation and Language Machine Learning Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. We categorize biases as intrinsic and extrinsic, analyzing their manifestations in various NLP tasks. The review critically assesses a range of bias evaluation methods, including data-level, model-level, and output-level approaches, providing researchers with a robust toolkit for bias detection. We further explore mitigation strategies, categorizing them into pre-model, intra-model, and post-model techniques, highlighting their effectiveness and limitations. Ethical and legal implications of biased LLMs are discussed, emphasizing potential harms in real-world applications such as healthcare and criminal justice. By synthesizing current knowledge on bias in LLMs, this review contributes to the ongoing effort to develop fair and responsible AI systems. Our work serves as a comprehensive resource for researchers and practitioners working towards understanding, evaluating, and mitigating bias in LLMs, fostering the development of more equitable AI technologies. |
| title | Bias in Large Language Models: Origin, Evaluation, and Mitigation |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2411.10915 |