9 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.CL2024★ 1 cited
InFoBench: Evaluating Instruction Following Ability in Large Language Models
Yiwei Qin, Kaiqiang Song, Yebowen Hu +7
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions. Addressing a gap…
cs.LG2023★ 9 cited
A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges
Xuansheng Wu, Kaixiong Zhou, Mingchen Sun +2
The recent "pre-train, prompt, predict training" paradigm has gained popularity as a way to learn generalizable models with limited labeled data. The approach involves using a pre-…
cs.CL2023
NoPPA: Non-Parametric Pairwise Attention Random Walk Model for Sentence Representation
Xuansheng Wu, Zhiyi Zhao, Ninghao Liu
We propose a novel non-parametric/un-trainable language model, named Non-Parametric Pairwise Attention Random Walk Model (NoPPA), to generate sentence embedding only with pre-train…