Toward Argumentative Large Language Models - Henning Wachsmuth (Leibniz University Hannover) скачать в хорошем качестве

Toward Argumentative Large Language Models - Henning Wachsmuth (Leibniz University Hannover)

Summary: Today's large language models (LLMs) are optimized toward giving helpful answers in response to prompts. In many situations, however, it may be preferable for an LLM to foster critical thinking rather than just following an instruction. While recent LLMs are said to 'reason', they barely build on established reasoning concepts known from argumentation theory. In this talk, I will give insights into recent efforts of my group in making LLMs more argumentative. Starting from basics of LLM training processes, I will present how to specialize LLMs for argumentation tasks via instruction fine-tuning as well as how to align the arguments they generate using reinforcement learning. From there, I will give an outlook on how to improve the actual reasoning capabilities of LLMs.