5 citations · 10 across the 11 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser
Xianfu Cheng, Hang Zhang, Jian Yang +9
In the domain of Document AI, parsing semi-structured image form is a crucial Key Information Extraction (KIE) task. The advent of pre-trained multimodal models significantly empow…
RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations
Shun Zhang, Chaoran Yan, Jian Yang +4
New Intent Discovery (NID) strives to identify known and reasonably deduce novel intent groups in the open-world scenario. But current methods face issues with inaccurate pseudo-la…
New Intent Discovery with Attracting and Dispersing Prototype
Shun Zhang, Jian Yang, Jiaqi Bai +4
New Intent Discovery (NID) aims to recognize known and infer new intent categories with the help of limited labeled and large-scale unlabeled data. The task is addressed as a featu…
Multilingual Entity and Relation Extraction from Unified to Language-specific Training
Zixiang Wang, Jian Yang, Tongliang Li +5
Entity and relation extraction is a key task in information extraction, where the output can be used for downstream NLP tasks. Existing approaches for entity and relation extractio…