most citedSEvenLLM: Benchmarking, Eliciting, and Enhancing Abilities of Large Language Models in Cyber Threat Intelligence

5 citations · 8 across the 7 of their papers we have counts for

collaborators

11 papers

cs.CL2025

P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark

Tao Sun, Enhao Pan, Zhengkai Yang +8

Academic posters are vital for scholarly communication, yet their manual creation is time-consuming. However, automated academic poster generation faces significant challenges in p…

cs.CL2025

SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models

Xianfu Cheng, Wei Zhang, Shiwei Zhang +16

The increasing application of multi-modal large language models (MLLMs) across various sectors have spotlighted the essence of their output reliability and accuracy, particularly t…

cs.CL2024

TableBench: A Comprehensive and Complex Benchmark for Table Question Answering

Xianjie Wu, Jian Yang, Linzheng Chai +10

Recent advancements in Large Language Models (LLMs) have markedly enhanced the interpretation and processing of tabular data, introducing previously unimaginable capabilities. Desp…

cs.CL20241 cited

Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model

Xia Hou, Qifeng Li, Jian Yang +8

Instruction tuning as an effective technique aligns the outputs of large language models (LLMs) with human preference. But how to generate the seasonal multi-turn dialogues from ra…

cs.PL20241 cited

McEval: Massively Multilingual Code Evaluation

Linzheng Chai, Shukai Liu, Jian Yang +15

Code large language models (LLMs) have shown remarkable advances in code understanding, completion, and generation tasks. Programming benchmarks, comprised of a selection of code c…

cs.CL2024

Towards Real-world Scenario: Imbalanced New Intent Discovery

Shun Zhang, Chaoran Yan, Jian Yang +5

New Intent Discovery (NID) aims at detecting known and previously undefined categories of user intent by utilizing limited labeled and massive unlabeled data. Most prior works ofte…