4 citations · 14 across the 19 of their papers we have counts for
4 papers · 1 filter
LM-Cocktail: Resilient Tuning of Language Models via Model Merging
Shitao Xiao, Zheng Liu, Peitian Zhang +1
The pre-trained language models are continually fine-tuned to better support downstream applications. However, this operation may result in significant performance degeneration on…
BiPFT: Binary Pre-trained Foundation Transformer with Low-rank Estimation of Binarization Residual Polynomials
Xingrun Xing, Li Du, Xinyuan Wang +4
Pretrained foundation models offer substantial benefits for a wide range of downstream tasks, which can be one of the most potential techniques to access artificial general intelli…
Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis
Li Du, Yequan Wang, Xingrun Xing +4
Although demonstrating superb performance on various NLP tasks, large language models (LLMs) still suffer from the hallucination problem, which threatens the reliability of LLMs. T…
KLoB: a Benchmark for Assessing Knowledge Locating Methods in Language Models
Yiming Ju, Xingrun Xing, Zhixiong Zeng
Recently, Locate-Then-Edit paradigm has emerged as one of the main approaches in changing factual knowledge stored in the Language models. However, there is a lack of research on w…