Introducing SelFee—a reinvented and powerful language model that uses self-feedback and self-revision to generate high-quality responses backed by a team of researchers from KAIST. Unlike previous models, SelFee doesn't rely on external, large-scale language or task-specific models, tipping the scales in the AI world.
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Why it matters?
- SelFee, built on the base of LLaMA-based instruction-following model and fine-tuned, offers a fresh approach - generating an initial solution and self-feedback sequences and then revising its answers until a high-quality response is achieved.
- Data used for its training and model evaluation was collected from varied sources and fine-tuned with OpenAI API calls, beating the 13B SelFee model with a minimal 7B SelFee model that generated at least three revisions.
- SelFee proves the potential of iterative revision in enhancing language model responses, indicating that an increase in inference computation of a model may be superior to merely magnifying its size.
Features and Limitations:
- SelFee's effective use of self-feedback significantly improves response quality, avoiding the requirement of external, large-scale language or task-specific models, translating into faster, cost-effective LLM solutions.
- However, lacking in certain areas compared to ChatGPT, such as math, reasoning, factuality, and coding, SelFee has room for further improvement and growth.
The revolution in the AI language model landscape is promising but still an evolving journey, with SelFee being the latest participant driving this change.
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(source) (github)
