Artificial intelligence is evolving at a rapid pace, and the latest research from the Korea Advanced Institute of Science and Technology (KAIST) brings forth groundbreaking advancements in the realm of AI. In a new study, KAIST researchers have demonstrated how natural language self-feedback can significantly improve language model performance. This innovation introduced the SelFee model, specifically designed for self-feedback and self-revision generation.
The SelFee model is based on the fine-tuned LLaMA instruction-following structure. At its core, the model generates initial solutions and self-feedback sequences to accomplish tasks. Notably, the model can determine whether an answer requires revision or not. If deemed necessary, it generates a revised response based on the given self-feedback.
To provide a strong foundation for the SelFee model’s self-feedback generation, researchers collected data from diverse sources, such as ShareGPT, Alpaca, Math, Code, and the Flan Collection. To address the scarcity of feedback and revision data, a distillation process was employed using the ChatGPT teacher model.
Data augmentation techniques were applied using OpenAI API calls. An iterative process of generating answers, obtaining feedback, and revising answers with ChatGPT was engaged to fine-tune the SelFee model. Furthermore, the FastChat framework was utilized, providing a solid, refined foundation for the model’s development.
The Vicuna evaluation setting, involving 80 diverse queries, was used to assess the SelFee model’s performance. A pilot evaluation was conducted using GPT-4 as the evaluator, comparing the SelFee model with ChatGPT. The results revealed that it took a minimum of three revisions for the SelFee model to surpass ChatGPT’s answer quality.
While the SelFee model showed a significant improvement in language performance, certain areas were identified where it didn’t quite match up to ChatGPT. In mathematical, reasoning, factuality, and coding tasks, SelFee was found to lack specific knowledge compared to its competitor.
The SelFee model has the potential to revolutionize the field of AI by introducing a novel approach to self-feedback and self-revision. Its innovative features elevate language model performance to new heights, offering improvements in critical reasoning and language generation.
As researchers fine-tune the SelFee model further, it is anticipated that language model performance will continue to reach its full potential, delivering even more accurate and insightful responses. The implications of this exciting new development are profound and will continue to shape the future of AI technology and its applications.
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