Value Induction in LLMs

This post investigates the unintended consequences of value induction in conversational LLMs. It fine-tunes models using curated value subsets of preference datasets and measures the impact on the expression of other values, model safety, anthropomorphic language, and QA benchmarks. Findings indicate that inducing values leads to the expression of related and sometimes contrasting values, increases safety, and all values increase anthropomorphic language use, making models more validating and sycophantic.