Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models

Understand

One of the primary driving forces contributing to the superior performance of Large Language Models (LLMs) is the extensive availability of human-annotated natural language data, which is used for alignment fine-tuning.

• This inspired researchers to investigate self-training methods to mitigate the extensive reliance on human annotations.
• However, the current success of self-training has been primarily observed in natural language scenarios, rather than in the increasingly important neural-symbolic scenarios.
• To this end, we propose an environment-guided neural-symbolic self-training framework named ENVISIONS.