Summary of Agr: Age Group Fairness Reward For Bias Mitigation in Llms, by Shuirong Cao et al.
AGR: Age Group fairness Reward for Bias Mitigation in LLMs
by Shuirong Cao, Ruoxi Cheng, Zhiqiang Wang
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 investigates age biases in language models, which can result in unequal treatment of individuals across different age groups. Despite much research on racial and gender biases, age bias remains understudied. The scarcity of datasets for age bias detection and measurement hinders its assessment, and existing fine-tuning methods rarely address age-related fairness. To mitigate this issue, the authors construct age bias preference datasets and instruction-tuning datasets for reinforcement learning with human feedback (RLHF). They also introduce an age fairness reward (ARG) to reduce differences in response quality across different age groups. The experiments demonstrate that ARG significantly improves response accuracy and reduces performance disparities across age groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models can be unfair by showing biases towards certain ages, which is a problem. Age bias has not been studied much, but the authors of this paper are working to change that. They made special datasets to help detect and measure age bias, and they created a new way to fine-tune language models so they’re fairer to people of different ages. The results show that their method works well and makes the language models better for everyone. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Reinforcement learning » Rlhf
