4 citations · 9 across the 17 of their papers we have counts for
4 papers · 1 filter
Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning
Yujian Liu, Shiyu Chang, Tommi Jaakkola +1
Recent studies have identified one aggravating factor of LLM hallucinations as the knowledge inconsistency between pre-training and fine-tuning, where unfamiliar fine-tuning data m…
Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective
Yujian Liu, Yang Zhang, Tommi Jaakkola +1
This paper investigates Who's Harry Potter (WHP), a pioneering yet insufficiently understood method for LLM unlearning. We explore it in two steps. First, we introduce a new task o…
Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference
Jiabao Ji, Yujian Liu, Yang Zhang +4
As Large Language Models (LLMs) demonstrate extensive capability in learning from documents, LLM unlearning becomes an increasingly important research area to address concerns of L…
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion
Yujian Liu, Jiabao Ji, Tong Yu +6
Table question answering is a popular task that assesses a model's ability to understand and interact with structured data. However, the given table often does not contain sufficie…