CHAI trains a reward model on GPT-4o preference labels and uses PPO to align Llama-3.1-8B for English-to-Hinglish translation, claiming a 25.66% human win-rate improvement over baselines.
HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
Text generation is a highly active area of research in the computational linguistic community. The evaluation of the generated text is a challenging task and multiple theories and metrics have been proposed over the years. Unfortunately, text generation and evaluation are relatively understudied due to the scarcity of high-quality resources in code-mixed languages where the words and phrases from multiple languages are mixed in a single utterance of text and speech. To address this challenge, we present a corpus (HinGE) for a widely popular code-mixed language Hinglish (code-mixing of Hindi and English languages). HinGE has Hinglish sentences generated by humans as well as two rule-based algorithms corresponding to the parallel Hindi-English sentences. In addition, we demonstrate the inefficacy of widely-used evaluation metrics on the code-mixed data. The HinGE dataset will facilitate the progress of natural language generation research in code-mixed languages.
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CHAI for LLMs: Improving Code-Mixed Translation in Large Language Models through Reinforcement Learning with AI Feedback
CHAI trains a reward model on GPT-4o preference labels and uses PPO to align Llama-3.1-8B for English-to-Hinglish translation, claiming a 25.66% human win-rate improvement over baselines.