35 citations · 84 across the 10 of their papers we have counts for
10 papers · 1 filter
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
Jun Yan, Vikas Yadav, Shiyang Li +6
Instruction-tuned Large Language Models (LLMs) have become a ubiquitous platform for open-ended applications due to their ability to modulate responses based on human instructions.…
AlpaGasus: Training A Better Alpaca with Fewer Data
Lichang Chen, Shiyang Li, Jun Yan +8
Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT data…
Graph Reasoning for Question Answering with Triplet Retrieval
Shiyang Li, Yifan Gao, Haoming Jiang +5
Answering complex questions often requires reasoning over knowledge graphs (KGs). State-of-the-art methods often utilize entities in questions to retrieve local subgraphs, which ar…
Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting
Xinlu Zhang, Shiyang Li, Xianjun Yang +3
Large language models (LLMs) demonstrate remarkable medical expertise, but data privacy concerns impede their direct use in healthcare environments. Although offering improved data…
Explanations from Large Language Models Make Small Reasoners Better
Shiyang Li, Jianshu Chen, Yelong Shen +9
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In thi…
ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
Zhiyu Chen, Shiyang Li, Charese Smiley +3
With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The communi…