Self-Correction in LLMs: Hype vs. Reality
Explore the complexities and potential applications of self-correction in LLMs while highlighting the need for further research in understanding its performance impact.
What is Self-Correction?
Self-correction in Large Language Models (LLMs) refers to the ability of these models to assess the accuracy of their outputs and refine their responses. It involves the process where an LLM can initially provide an incorrect answer or output but can correct itself after reviewing its own reasoning. This process is also known as “self-critique,” “self-refine,” or “self-improve” and has been observed in various tasks, although its effectiveness may vary depending on the nature of the task at hand (RadOncNotes).
Self-correction in language models involves utilizing feedback signals to refine the model. However, there is an ambiguity and uncertainty in the wider community, with even domain experts unsure about the intricacies of when and how self-correction operates. Some existing literature may inadvertently contribute to this confusion by not clearly stating whether their self-correction strategies include external feedback or not (Huang et al, Xu et al).
Different approaches to self-correction include:
- self-training during training-time correction,
