Researchers from Tsinghua University, Shanghai Artificial Intelligence Laboratory, and 01.AI have developed a new framework called OpenChat to improve open-source language models with mixed data quality.
Open-source language models such as LLaMA and LLaMA2, which allow anyone to inspect and understand the program code, are often refined and optimized using special techniques such as supervised fine-tuning (SFT) and reinforcement learning fine-tuning (RLFT).
However, these techniques assume that all data used is of the same quality. In practice, however, a data set typically consists of a mixture of optimal and relatively poor data. This can hurt the performance of language models.
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