Summary of Chai For Llms: Improving Code-mixed Translation in Large Language Models Through Reinforcement Learning with Ai Feedback, by Wenbo Zhang et al.
CHAI for LLMs: Improving Code-Mixed Translation in Large Language Models through Reinforcement Learning with AI Feedback
by Wenbo Zhang, Aditya Majumdar, Amulya Yadav
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed CHAI framework improves the ability of multilingual Large Language Models (LLMs) to handle code-mixed languages by leveraging their capacity for accurate annotations and generating preference data through reinforcement learning from AI feedback (RLAIF). This medium-difficulty summary highlights the technical achievements, including novel contributions that enable LLMs to annotate code-mixed translation tasks, generate preference data at scale, and conduct rigorous experimental evaluations. The paper’s focus on improving multilingual LLMs’ capabilities in code-mixed language understanding is essential for developing more inclusive language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves how Large Language Models (LLMs) work with different languages mixed together. Right now, these models struggle to understand when people switch between languages while speaking or writing. The authors want to change this by creating a new way to train LLMs using their own abilities as annotators and feedback. This helps the models learn from their mistakes and get better at understanding code-mixed languages. |
Keywords
» Artificial intelligence » Language understanding » Reinforcement learning » Translation
