Talk: Daniel Herrmann (Groningen)

Title:

Standards for Belief Representations in LLMs

Abstract:

As large language models (LLMs) continue to demonstrate remarkable abilities across various domains, computer scientists are developing methods to understand their cognitive processes, particularly concerning belief representation. However, the field currently lacks a unified theoretical foundation to underpin the study of belief in LLMs. I will present joint work with Ben Levinstein that begins to fill this gap by proposing adequacy conditions for a representation in an LLM to count as belief-like. Drawing from insights in philosophy and contemporary practices of machine learning, I'll establish criteria informed by theoretical considerations and practical constraints. These conditions help lay the groundwork for a comprehensive understanding of belief representation in LLMs.

Here is the Zoom link for remote participation:
https://lmu-munich.zoom.us/j/93279453658?pwd=MGRkZ1lOYTVtK2tleWVHbFZ4UFNzZz09