15 citations · 25 across the 6 of their papers we have counts for
6 papers
Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors
Vikas Yadav, Zheng Tang, Vijay Srinivasan
Large language models (LLM) have achieved remarkable success in natural language generation but lesser focus has been given to their applicability in decision making tasks such as…
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.…
Instruction-following Evaluation through Verbalizer Manipulation
Shiyang Li, Jun Yan, Hai Wang +4
While instruction-tuned models have shown remarkable success in various natural language processing tasks, accurately evaluating their ability to follow instructions remains challe…
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…
It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers
Zheng Tang, Mihai Surdeanu
We propose an explainable approach for relation extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. Our approach…
How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks
Hoang Van, Zheng Tang, Mihai Surdeanu
The general goal of text simplification (TS) is to reduce text complexity for human consumption. This paper investigates another potential use of neural TS: assisting machines perf…