257 citations · 733 across the 67 of their papers we have counts for
15 papers · 1 filter
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
Peiyi Wang, Lei Li, Zhihong Shao +6
In this paper, we present an innovative process-oriented math process reward model called \textbf{Math-Shepherd}, which assigns a reward score to each step of math problem solution…
Guiding AMR Parsing with Reverse Graph Linearization
Bofei Gao, Liang Chen, Peiyi Wang +2
Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence. The sequence-to-sequence approaches, which linearize the semantic gr…
Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning
Zhe Yang, Damai Dai, Peiyi Wang +1
Large Language Models (LLMs) have recently gained the In-Context Learning (ICL) ability with the models scaling up, allowing them to quickly adapt to downstream tasks with only a f…
InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective
Yifan Song, Peiyi Wang, Weimin Xiong +4
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks. We focus on continual text classification under the cl…
Large Language Model for Science: A Study on P vs. NP
Qingxiu Dong, Li Dong, Ke Xu +4
In this work, we use large language models (LLMs) to augment and accelerate research on the P versus NP problem, one of the most important open problems in theoretical computer sci…
Making Large Language Models Better Reasoners with Alignment
Peiyi Wang, Lei Li, Liang Chen +5
Reasoning is a cognitive process of using evidence to reach a sound conclusion. The reasoning capability is essential for large language models (LLMs) to serve as the brain of the…
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion
Shaoxiang Wu, Damai Dai, Ziwei Qin +4
Video multimodal fusion aims to integrate multimodal signals in videos, such as visual, audio and text, to make a complementary prediction with multiple modalities contents. Howeve…
Learn to Not Link: Exploring NIL Prediction in Entity Linking
Fangwei Zhu, Jifan Yu, Hailong Jin +3
Entity linking models have achieved significant success via utilizing pretrained language models to capture semantic features. However, the NIL prediction problem, which aims to id…
RepCL: Exploring Effective Representation for Continual Text Classification
Yifan Song, Peiyi Wang, Dawei Zhu +3
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks. In this work, we focus on continual text classificatio…
Enhancing Continual Relation Extraction via Classifier Decomposition
Heming Xia, Peiyi Wang, Tianyu Liu +3
Continual relation extraction (CRE) models aim at handling emerging new relations while avoiding catastrophically forgetting old ones in the streaming data. Though improvements hav…
Large Language Models are not Fair Evaluators
Peiyi Wang, Lei Li, Liang Chen +7
In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e.g., GPT-4, as a referee to score and compare the quality of respo…
ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories
Heming Xia, Qingxiu Dong, Lei Li +4
Recently, Large Language Models (LLMs) have been serving as general-purpose interfaces, posing a significant demand for comprehensive visual knowledge. However, it remains unclear…
Bi-Drop: Enhancing Fine-tuning Generalization via Synchronous sub-net Estimation and Optimization
Shoujie Tong, Heming Xia, Damai Dai +5
Pretrained language models have achieved remarkable success in natural language understanding. However, fine-tuning pretrained models on limited training data tends to overfit and…
Statistical Knowledge Assessment for Large Language Models
Qingxiu Dong, Jingjing Xu, Lingpeng Kong +2
Given varying prompts regarding a factoid question, can a large language model (LLM) reliably generate factually correct answers? Existing LLMs may generate distinct responses for…
A Survey on In-context Learning
Qingxiu Dong, Lei Li, Damai Dai +10
With the increasing capabilities of large language models (LLMs), in-context learning (ICL) has emerged as a new paradigm for natural language processing (NLP), where LLMs make pre…