257 citations · 519 across the 28 of their papers we have counts for
28 papers
OpenCompass: A Universal Evaluation Platform for Large Language Models
Maosong Cao, Kai Chen, Haodong Duan +27
In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs). With the…
UniSRM: A Unified Speech Reward Model for Reasoning-Based Fine-grained Assessment
Yuanyuan Wang, Dongchao Yang, Yayue Deng +4
Evaluating speech generation still relies heavily on human judgments, such as Mean Opinion Score (MOS), which are expensive, subjective, and difficult to reproduce at scale. While…
Implicit Search via Discrete Diffusion: A Study on Chess
Jiacheng Ye, Zhenyu Wu, Jiahui Gao +4
In the post-AlphaGo era, there has been a renewed interest in search techniques such as Monte Carlo Tree Search (MCTS), particularly in their application to Large Language Models (…
SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents
Kanzhi Cheng, Qiushi Sun, Yougang Chu +4
Graphical User Interface (GUI) agents are designed to automate complex tasks on digital devices, such as smartphones and desktops. Most existing GUI agents interact with the enviro…
OS-Copilot: Towards Generalist Computer Agents with Self-Improvement
Zhiyong Wu, Chengcheng Han, Zichen Ding +5
Autonomous interaction with the computer has been a longstanding challenge with great potential, and the recent proliferation of large language models (LLMs) has markedly accelerat…
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
Fangzhi Xu, Zhiyong Wu, Qiushi Sun +6
Although Large Language Models (LLMs) demonstrate remarkable ability in processing and generating human-like text, they do have limitations when it comes to comprehending and expre…
How Vocabulary Sharing Facilitates Multilingualism in LLaMA?
Fei Yuan, Shuai Yuan, Zhiyong Wu +1
Large Language Models (LLMs), often show strong performance on English tasks, while exhibiting limitations on other languages. What is an LLM's multilingual capability when it is t…
DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models
Shansan Gong, Mukai Li, Jiangtao Feng +2
Diffusion models have gained prominence in generating high-quality sequences of text. Nevertheless, current approaches predominantly represent discrete text within a continuous dif…
EMO: Earth Mover Distance Optimization for Auto-Regressive Language Modeling
Siyu Ren, Zhiyong Wu, Kenny Q. Zhu
Neural language models are probabilistic models of human text. They are predominantly trained using maximum likelihood estimation (MLE), which is equivalent to minimizing the forwa…
Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration
Qiushi Sun, Zhangyue Yin, Xiang Li +3
Large Language Models (LLMs) are evolving at an unprecedented pace and have exhibited considerable capability in the realm of natural language processing (NLP) with world knowledge…
Can We Edit Factual Knowledge by In-Context Learning?
Ce Zheng, Lei Li, Qingxiu Dong +4
Previous studies have shown that large language models (LLMs) like GPTs store massive factual knowledge in their parameters. However, the stored knowledge could be false or out-dat…
OpenICL: An Open-Source Framework for In-context Learning
Zhenyu Wu, YaoXiang Wang, Jiacheng Ye +4
In recent years, In-context Learning (ICL) has gained increasing attention and emerged as the new paradigm for large language model (LLM) evaluation. Unlike traditional fine-tuning…
In-Context Learning with Many Demonstration Examples
Mukai Li, Shansan Gong, Jiangtao Feng +4
Large pre-training language models (PLMs) have shown promising in-context learning abilities. However, due to the backbone transformer architecture, existing PLMs are bottlenecked…
Compositional Exemplars for In-context Learning
Jiacheng Ye, Zhiyong Wu, Jiangtao Feng +2
Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability, where the model learns to do an unseen task via a prompt consisting of input-output…
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…
Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering
Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye +1
Despite the surprising few-shot performance of in-context learning (ICL), it is still a common practice to randomly sample examples to serve as context. This paper advocates a new…
Explanation Regeneration via Information Bottleneck
Qintong Li, Zhiyong Wu, Lingpeng Kong +1
Explaining the black-box predictions of NLP models naturally and accurately is an important open problem in natural language generation. These free-text explanations are expected t…
Unsupervised Explanation Generation via Correct Instantiations
Sijie Cheng, Zhiyong Wu, Jiangjie Chen +3
While large pre-trained language models (PLM) have shown their great skills at solving discriminative tasks, a significant gap remains when compared with humans for explanation-rel…
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
Jiacheng Ye, Jiahui Gao, Jiangtao Feng +3
Recently, dataset-generation-based zero-shot learning has shown promising results by training a task-specific model with a dataset synthesized from large pre-trained language model…
DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models
Shansan Gong, Mukai Li, Jiangtao Feng +2
Recently, diffusion models have emerged as a new paradigm for generative models. Despite the success in domains using continuous signals such as vision and audio, adapting diffusio…
COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization
Chenxin An, Ming Zhong, Zhiyong Wu +3
Traditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives. However, the output summary i…
Lexical Knowledge Internalization for Neural Dialog Generation
Zhiyong Wu, Wei Bi, Xiang Li +2
We propose knowledge internalization (KI), which aims to complement the lexical knowledge into neural dialog models. Instead of further conditioning the knowledge-grounded dialog (…
Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning
Jiahui Gao, Renjie Pi, Yong Lin +7
There is a rising interest in further exploring the zero-shot learning potential of large pre-trained language models (PLMs). A new paradigm called data-generation-based zero-shot…
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
Jiacheng Ye, Jiahui Gao, Qintong Li +5
There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible a…