257 citations · 733 across the 67 of their papers we have counts for
67 papers
Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones?
Zhe Yang, Yichang Zhang, Tianyu Liu +4
Large language models (LLMs) have demonstrated impressive capabilities, but still suffer from inconsistency issues (e.g. LLMs can react differently to disturbances like rephrasing…
Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens
Weiyao Luo, Suncong Zheng, Heming Xia +5
Large language models (LLMs) have shown promising efficacy across various tasks, becoming powerful tools in numerous aspects of human life. However, Transformer-based LLMs suffer a…
Reducing Hallucinations in Entity Abstract Summarization with Facts-Template Decomposition
Fangwei Zhu, Peiyi Wang, Zhifang Sui
Entity abstract summarization aims to generate a coherent description of a given entity based on a set of relevant Internet documents. Pretrained language models (PLMs) have achiev…
PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization
Xiangdi Meng, Damai Dai, Weiyao Luo +7
Supervised fine-tuning is the most common method to adapt large language models (LLMs) to downstream tasks, but full fine-tuning LLMs requires massive computational resources. Rece…
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Haoran Li, Qingxiu Dong, Zhengyang Tang +17
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on se…
Can Large Multimodal Models Uncover Deep Semantics Behind Images?
Yixin Yang, Zheng Li, Qingxiu Dong +2
Understanding the deep semantics of images is essential in the era dominated by social media. However, current research works primarily on the superficial description of images, re…
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
Damai Dai, Chengqi Deng, Chenggang Zhao +14
In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. However, conventional M…
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding
Heming Xia, Zhe Yang, Qingxiu Dong +6
To mitigate the high inference latency stemming from autoregressive decoding in Large Language Models (LLMs), Speculative Decoding has emerged as a novel decoding paradigm for LLM…
Language Models Encode the Value of Numbers Linearly
Fangwei Zhu, Damai Dai, Zhifang Sui
Large language models (LLMs) have exhibited impressive competence in various tasks, but their internal mechanisms on mathematical problems are still under-explored. In this paper,…
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…