Work

Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models

Year: 2024

Type: conference-paper

Abstract: Ensuring the trustworthiness of large language models (LLMs) is crucial.Most studies concentrate on fully pre-trained LLMs to better understand and improve LLMs' trustworthiness.In this paper, to reveal the untapped potential of pre-training, we pioneer the exploration of LLMs' trustworthiness during this period, focusing on five key dimensions: reliability, privacy, toxicity, fairness, and robustness.To begin with, we apply linear probing to LLMs.The high probing accuracy suggests that LLMs in early pre-training can already distinguish concepts in each trustworthiness dimension.Therefore, to further uncover the hidden possibilities of pre-training, we extract steering vectors from a LLM's pre-training checkpoints to enhance the LLM's trustworthiness.Finally, inspired by Choi et al. (2023) that mutual information estimation is bounded by linear probing accuracy, we also probe LLMs with mutual information to investigate the dynamics of trustworthiness during pre-training.We are the first to observe a similar two-phase phenomenon: fitting and compression (Shwartz-Ziv and Tishby, 2017).This research provides an initial exploration of trustworthiness modeling during LLM pre-training, seeking to unveil new insights and spur further developments in the field.Our code is publicly accessible at https://github.com/ChnQ/TracingLLM.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2024.findings-acl.290