24 citations · 70 across the 46 of their papers we have counts for
14 papers · 2 filters
MdEval: Massively Multilingual Code Debugging
Shukai Liu, Linzheng Chai, Jian Yang +15
Code large language models (LLMs) have made significant progress in code debugging by directly generating the correct code based on the buggy code snippet. Programming benchmarks,…
FuzzCoder: Byte-level Fuzzing Test via Large Language Model
Liqun Yang, Jian Yang, Chaoren Wei +13
Fuzzing is an important dynamic program analysis technique designed for finding vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malic…
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
Hongcheng Guo, Wei Zhang, Junhao Chen +9
Recently advancements in large multimodal models have led to significant strides in image comprehension capabilities. Despite these advancements, there is a lack of the robust benc…
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…
UniCoder: Scaling Code Large Language Model via Universal Code
Tao Sun, Linzheng Chai, Jian Yang +6
Intermediate reasoning or acting steps have successfully improved large language models (LLMs) for handling various downstream natural language processing (NLP) tasks. When applyin…
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…
EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation
Yinghao Zhu, Changyu Ren, Zixiang Wang +7
The integration of multimodal Electronic Health Records (EHR) data has significantly advanced clinical predictive capabilities. Existing models, which utilize clinical notes and mu…
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…
m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt
Jian Yang, Hongcheng Guo, Yuwei Yin +7
Multilingual translation supports multiple translation directions by projecting all languages in a shared space, but the translation quality is undermined by the difference between…
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…
C-ICL: Contrastive In-context Learning for Information Extraction
Ying Mo, Jiahao Liu, Jian Yang +4
There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on task…
xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning
Linzheng Chai, Jian Yang, Tao Sun +8
Chain-of-thought (CoT) has emerged as a powerful technique to elicit reasoning in large language models and improve a variety of downstream tasks. CoT mainly demonstrates excellent…