29 citations · 134 across the 28 of their papers we have counts for
29 papers
TATK: Triple-Aware Top-K Learning with Knowledge-Grounded Verification for LLM-based Sequential Recommendation
Yuchen Guan, Jiaye Liu, Yifei Han +3
LLM-based sequential recommenders usually cast next-item prediction as text generation, but this interface is poorly matched to full-catalog top-K ranking. We propose TATK, a Tripl…
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST
Shuyu Zhang, Yifan Wei, Xinru Wang +5
Zero-shot Dialog State Tracking (zs-DST) is essential for enabling Task-Oriented Dialog Systems (TODs) to generalize to new domains without costly data annotation. A central challe…
AutoTIR: Autonomous Tools Integrated Reasoning via Reinforcement Learning
Yifan Wei, Xiaoyan Yu, Yixuan Weng +3
Large Language Models (LLMs), when enhanced through reasoning-oriented post-training, evolve into powerful Large Reasoning Models (LRMs). Tool-Integrated Reasoning (TIR) further ex…
AI Scientists Fail Without Strong Implementation Capability
Minjun Zhu, Qiujie Xie, Yixuan Weng +4
The emergence of Artificial Intelligence (AI) Scientist represents a paradigm shift in scientific discovery, with large language models (LLMs) taking the lead as the primary execut…
Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge
Bin Li, Shenxi Liu, Yixuan Weng +3
Following the successful hosts of the 1-st (NLPCC 2023 Foshan) CMIVQA and the 2-rd (NLPCC 2024 Hangzhou) MMIVQA challenges, this year, a new task has been introduced to further adv…
ReGraP-LLaVA: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant
Yifan Xiang, Zhenxi Zhang, Bin Li +6
Multimodal Large Language Models have shown strong performance across multimodal tasks, and recent personalized MLLMs can recognize user-specific concepts and generate contextual c…
DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process
Minjun Zhu, Yixuan Weng, Linyi Yang +1
Large Language Models (LLMs) are increasingly utilized in scientific research assessment, particularly in automated paper review. However, existing LLM-based review systems face si…
Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMs
Haoran Fan, Bin Li, Yixuan Weng +1
While LLMs have demonstrated remarkable potential in time series forecasting, their practical deployment remains constrained by excessive computational demands and memory footprint…
CycleResearcher: Improving Automated Research via Automated Review
Yixuan Weng, Minjun Zhu, Guangsheng Bao +4
The automation of scientific discovery has been a long-standing goal within the research community, driven by the potential to accelerate knowledge creation. While significant prog…
Does Knowledge Localization Hold True? Surprising Differences Between Entity and Relation Perspectives in Language Models
Yifan Wei, Xiaoyan Yu, Yixuan Weng +4
Large language models encapsulate knowledge and have demonstrated superior performance on various natural language processing tasks. Recent studies have localized this knowledge to…
Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model
Fei Xia, Yixuan Weng, Shizhu He +2
Taxonomies, which organize domain concepts into hierarchical structures, are crucial for building knowledge systems and downstream applications. As domain knowledge evolves, taxono…
ControlLM: Crafting Diverse Personalities for Language Models
Yixuan Weng, Shizhu He, Kang Liu +2
As language models continue to scale in size and capability, they display an array of emerging behaviors, both beneficial and concerning. This heightens the need to control model b…
Assessing Knowledge Editing in Language Models via Relation Perspective
Yifan Wei, Xiaoyan Yu, Huanhuan Ma +4
Knowledge Editing (KE) for modifying factual knowledge in Large Language Models (LLMs) has been receiving increasing attention. However, existing knowledge editing methods are enti…
LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models
Yixuan Weng, Zhiqi Wang, Huanxuan Liao +4
With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to…
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database
Minjun Zhu, Yixuan Weng, Shizhu He +2
In Textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps. While existing benchmarks are li…
Large Language Models Need Holistically Thought in Medical Conversational QA
Yixuan Weng, Bin Li, Fei Xia +5
The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care. Despite the succes…
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks
Yixuan Weng, Minjun Zhu, Fei Xia +4
Language models' (LMs) proficiency in handling deterministic symbolic reasoning and rule-based tasks remains limited due to their dependency implicit learning on textual data. To e…
Artificial Text Detection with Multiple Training Strategies
Bin Li, Yixuan Weng, Qiya Song +1
As the deep learning rapidly promote, the artificial texts created by generative models are commonly used in news and social media. However, such models can be abused to generate p…
Large Language Models are Better Reasoners with Self-Verification
Yixuan Weng, Minjun Zhu, Fei Xia +6
Recently, with the chain of thought (CoT) prompting, large language models (LLMs), e.g., GPT-3, have shown strong reasoning ability in several natural language processing tasks suc…
Visual Answer Localization with Cross-modal Mutual Knowledge Transfer
Yixuan Weng, Bin Li
The goal of visual answering localization (VAL) in the video is to obtain a relevant and concise time clip from a video as the answer to the given natural language question. Early…
ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains
Minjun Zhu, Yixuan Weng, Shizhu He +2
The ability of reasoning over evidence has received increasing attention in question answering (QA). Recently, natural language database (NLDB) conducts complex QA in knowledge bas…
Learning to Locate Visual Answer in Video Corpus Using Question
Bin Li, Yixuan Weng, Bin Sun +1
We introduce a new task, named video corpus visual answer localization (VCVAL), which aims to locate the visual answer in a large collection of untrimmed instructional videos using…
Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation
Bin Li, Yixuan Weng, Ziyu Ma +2
This paper introduces the schemes of Team LingJing's experiments in NLPCC-2022-Shared-Task-4 Multi-modal Dialogue Understanding and Generation (MDUG). The MDUG task can be divided…
LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
Fei Xia, Bin Li, Yixuan Weng +5
The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic. This paper presents a medical conversa…