7 citations · 20 across the 40 of their papers we have counts for
7 papers · 2 filters
Large Language Model Evaluation via Matrix Nuclear-Norm
Yahan Li, Tingyu Xia, Yi Chang +1
As large language models (LLMs) continue to evolve, efficient evaluation metrics are vital for assessing their ability to compress information and reduce redundancy. While traditio…
Rethinking Data Selection at Scale: Random Selection is Almost All You Need
Tingyu Xia, Bowen Yu, Kai Dang +5
Supervised fine-tuning (SFT) is crucial for aligning Large Language Models (LLMs) with human instructions. The primary goal during SFT is to select a small yet representative subse…
CHBench: A Chinese Dataset for Evaluating Health in Large Language Models
Chenlu Guo, Nuo Xu, Yi Chang +1
With the rapid development of large language models (LLMs), assessing their performance on health-related inquiries has become increasingly essential. The use of these models in re…
XTRUST: On the Multilingual Trustworthiness of Large Language Models
Yahan Li, Yi Wang, Yi Chang +1
Large language models (LLMs) have demonstrated remarkable capabilities across a range of natural language processing (NLP) tasks, capturing the attention of both practitioners and…
BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models
Yupeng Chang, Yi Chang, Yuan Wu
Parameter-efficient fine-tuning (PEFT) has become a de facto standard for adapting large language models (LLMs). However, we identify a critical vulnerability within popular low-ra…
NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli
Xu Wang, Cheng Li, Yi Chang +2
Large Language Models (LLMs) have become integral to a wide spectrum of applications, ranging from traditional computing tasks to advanced artificial intelligence (AI) applications…
Language Models can Evaluate Themselves via Probability Discrepancy
Tingyu Xia, Bowen Yu, Yuan Wu +2
In this paper, we initiate our discussion by demonstrating how Large Language Models (LLMs), when tasked with responding to queries, display a more even probability distribution in…