activity
20232025
most citedMMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

34 citations · 73 across the 20 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.SE2025

VisCoder2: Building Multi-Language Visualization Coding Agents

Yuansheng Ni, Songcheng Cai, Xiangchao Chen +8

Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code. However, existing models often fail in practical…

cs.SE2025

VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation

Yuansheng Ni, Ping Nie, Kai Zou +2

Large language models (LLMs) often struggle with visualization tasks like plotting diagrams, charts, where success depends on both code correctness and visual semantics. Existing i…

cs.CL2025★ 1 cited

ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations

Yubo Wang, Xueguang Ma, Ping Nie +7

Academic writing requires both coherent text generation and precise citation of relevant literature. Although recent Retrieval-Augmented Generation (RAG) systems have significantly…

cs.CL2025★ 4 cited

SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

P Team, Xinrun Du, Yifan Yao +94

Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledg…

cs.CV2025

VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search

Yiming Jia, Jiachen Li, Xiang Yue +4

Vision-Language Models have made significant progress on many perception-focused tasks. However, their progress on reasoning-focused tasks remains limited due to the lack of high-q…

cs.CL2025

Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate

Yubo Wang, Xiang Yue, Wenhu Chen

Supervised Fine-Tuning (SFT) is commonly used to train language models to imitate annotated responses for given instructions. In this paper, we propose Critique Fine-Tuning (CFT),…

cs.AI2025

Aligning Instruction Tuning with Pre-training

Yiming Liang, Tianyu Zheng, Xinrun Du +12

Instruction tuning enhances large language models (LLMs) to follow human instructions across diverse tasks, relying on high-quality datasets to guide behavior. However, these datas…