59 citations · 375 across the 88 of their papers we have counts for
20 papers · 1 filter
Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation
Zhuohao Yu, Weizheng Gu, Yidong Wang +5
Large Language Models excel at code generation yet struggle with complex programming tasks that demand sophisticated reasoning. To bridge this gap, traditional process supervision…
SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization
Hongrui Jia, Chaoya Jiang, Haiyang Xu +6
As language models continue to scale, Large Language Models (LLMs) have exhibited emerging capabilities in In-Context Learning (ICL), enabling them to solve language tasks by prefi…
SG-Bench: Evaluating LLM Safety Generalization Across Diverse Tasks and Prompt Types
Yutao Mou, Shikun Zhang, Wei Ye
Ensuring the safety of large language model (LLM) applications is essential for developing trustworthy artificial intelligence. Current LLM safety benchmarks have two limitations.…
ISC4DGF: Enhancing Directed Grey-box Fuzzing with LLM-Driven Initial Seed Corpus Generation
Yijiang Xu, Hongrui Jia, Liguo Chen +8
Fuzz testing is crucial for identifying software vulnerabilities, with coverage-guided grey-box fuzzers like AFL and Angora excelling in broad detection. However, as the need for t…
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
Xuanwang Zhang, Yunze Song, Yidong Wang +10
Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hall…
MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model
Chaoya Jiang, Jia Hongrui, Haiyang Xu +6
This paper presents MaVEn, an innovative Multi-granularity Visual Encoding framework designed to enhance the capabilities of Multimodal Large Language Models (MLLMs) in multi-image…
Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey
Haixin Wang, Yadi Cao, Zijie Huang +13
This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques. We begin by introducing fundamental conc…
A Survey on Evaluating Large Language Models in Code Generation Tasks
Liguo Chen, Qi Guo, Hongrui Jia +9
This paper provides a comprehensive review of the current methods and metrics used to evaluate the performance of Large Language Models (LLMs) in code generation tasks. With the ra…
Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction
Dingyao Yu, Yang An, Wei Ye +4
Chinese Spelling Correction (CSC) commonly lacks large-scale high-quality corpora, due to the labor-intensive labeling of spelling errors in real-life human writing or typing scena…
Enhancing In-Context Learning via Implicit Demonstration Augmentation
Xiaoling Zhou, Wei Ye, Yidong Wang +4
The emergence of in-context learning (ICL) enables large pre-trained language models (PLMs) to make predictions for unseen inputs without updating parameters. Despite its potential…
AutoSurvey: Large Language Models Can Automatically Write Surveys
Yidong Wang, Qi Guo, Wenjin Yao +10
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial…
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling
Zile Qiao, Wei Ye, Yong Jiang +5
Retrieval-augmented language models (RALMs) have recently shown great potential in mitigating the limitations of implicit knowledge in LLMs, such as untimely updating of the latest…
Boosting Model Resilience via Implicit Adversarial Data Augmentation
Xiaoling Zhou, Wei Ye, Zhemg Lee +2
Data augmentation plays a pivotal role in enhancing and diversifying training data. Nonetheless, consistently improving model performance in varied learning scenarios, especially t…
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models
Zeqian Ju, Yuancheng Wang, Kai Shen +16
While recent large-scale text-to-speech (TTS) models have achieved significant progress, they still fall short in speech quality, similarity, and prosody. Considering speech intric…
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models
Zhuohao Yu, Chang Gao, Wenjin Yao +6
The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-e…
CoderUJB: An Executable and Unified Java Benchmark for Practical Programming Scenarios
Zhengran Zeng, Yidong Wang, Rui Xie +2
In the evolving landscape of large language models (LLMs) tailored for software engineering, the need for benchmarks that accurately reflect real-world development scenarios is par…
CodeShell Technical Report
Rui Xie, Zhengran Zeng, Zhuohao Yu +3
Code large language models mark a pivotal breakthrough in artificial intelligence. They are specifically crafted to understand and generate programming languages, significantly boo…
Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
Wei Ye, Chaoya Jiang, Haiyang Xu +6
Vision Transformers (ViTs) have become increasingly popular in large-scale Vision and Language Pre-training (VLP) models. Although previous VLP research has demonstrated the effica…
Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
Chaoya Jiang, Hongrui Jia, Wei Ye +5
Large Vision Language Models exhibit remarkable capabilities but struggle with hallucinations inconsistencies between images and their descriptions. Previous hallucination evaluati…
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
Zhuohao Yu, Chang Gao, Wenjin Yao +6
Automatic evaluation methods for large language models (LLMs) are hindered by data contamination, leading to inflated assessments of their effectiveness. Existing strategies, which…