15 citations · 40 across the 24 of their papers we have counts for
17 papers · 1 filter
Are We on the Right Way to Assessing LLM-as-a-Judge?
Yuanning Feng, Sinan Wang, Zhengxiang Cheng +2
LLM-as-a-Judge has been widely adopted as an evaluation method and served as supervised rewards in model training. However, existing benchmarks for LLM-as-a-Judge are mainly relyin…
Paper2Web: Let's Make Your Paper Alive!
Yuhang Chen, Tianpeng Lv, Siyi Zhang +4
Academic project websites can more effectively disseminate research when they clearly present core content and enable intuitive navigation and interaction. However, current approac…
Are We on the Right Way for Assessing Document Retrieval-Augmented Generation?
Wenxuan Shen, Mingjia Wang, Yaochen Wang +4
Retrieval-Augmented Generation (RAG) systems using Multimodal Large Language Models (MLLMs) show great promise for complex document understanding, yet their development is critical…
code_transformed: The Influence of Large Language Models on Code
Yuliang Xu, Siming Huang, Mingmeng Geng +3
Coding remains one of the most fundamental modes of interaction between humans and machines. With the rapid advancement of Large Language Models (LLMs), code generation capabilitie…
Judge Anything: MLLM as a Judge Across Any Modality
Shu Pu, Yaochen Wang, Dongping Chen +10
Evaluating generative foundation models on open-ended multimodal understanding (MMU) and generation (MMG) tasks across diverse modalities (e.g., images, audio, video) poses signifi…
CrowdSelect: Synthetic Instruction Data Selection with Multi-LLM Wisdom
Yisen Li, Lingfeng Yang, Wenxuan Shen +4
Distilling advanced Large Language Models' instruction-following capabilities into smaller models using a selected subset has become a mainstream approach in model training. While…
Wikipedia in the Era of LLMs: Evolution and Risks
Siming Huang, Yuliang Xu, Mingmeng Geng +2
In this paper, we present a comprehensive analysis and monitoring framework for the impact of Large Language Models (LLMs) on Wikipedia, examining the evolution of Wikipedia throug…
nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow
Geliang Ouyang, Jingyao Chen, Zhihe Nie +4
Natural Language to Visualization (NL2Vis) seeks to convert natural-language descriptions into visual representations of given tables, empowering users to derive insights from larg…
CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale
Chenlong Wang, Zhaoyang Chu, Zhengxiang Cheng +6
Large Language Models (LLMs) have exhibited exceptional performance in software engineering yet face challenges in adapting to continually evolving code knowledge, particularly reg…
The Impact of Large Language Models in Academia: from Writing to Speaking
Mingmeng Geng, Caixi Chen, Yanru Wu +3
Large language models (LLMs) are increasingly impacting human society, particularly in textual information. Based on more than 30,000 papers and 1,000 presentations from machine le…
Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge
Jiayi Ye, Yanbo Wang, Yue Huang +9
LLM-as-a-Judge has been widely utilized as an evaluation method in various benchmarks and served as supervised rewards in model training. However, despite their excellence in many…
Self-Cognition in Large Language Models: An Exploratory Study
Dongping Chen, Jiawen Shi, Yao Wan +3
While Large Language Models (LLMs) have achieved remarkable success across various applications, they also raise concerns regarding self-cognition. In this paper, we perform a pion…
DataGen: Unified Synthetic Dataset Generation via Large Language Models
Yue Huang, Siyuan Wu, Chujie Gao +8
Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation and reducing dependence on expen…
Jailbreaking Large Language Models Through Alignment Vulnerabilities in Out-of-Distribution Settings
Yue Huang, Jingyu Tang, Dongping Chen +5
Recently, Large Language Models (LLMs) have garnered significant attention for their exceptional natural language processing capabilities. However, concerns about their trustworthi…
HonestLLM: Toward an Honest and Helpful Large Language Model
Chujie Gao, Siyuan Wu, Yue Huang +6
Large Language Models (LLMs) have achieved remarkable success across various industries due to their exceptional generative capabilities. However, for safe and effective real-world…
MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark
Dongping Chen, Ruoxi Chen, Shilin Zhang +7
Multimodal Large Language Models (MLLMs) have gained significant attention recently, showing remarkable potential in artificial general intelligence. However, assessing the utility…
LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?
Qihui Zhang, Chujie Gao, Dongping Chen +8
With the rapid development and widespread application of Large Language Models (LLMs), the use of Machine-Generated Text (MGT) has become increasingly common, bringing with it pote…