16 citations · 54 across the 56 of their papers we have counts for
19 papers · 1 filter
Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction
Tencent WorkBuddy Bench Team, Siqi Cai, Shaopeng Chen +35
We introduce Tencent WorkBuddy Bench, a multi-domain evaluation suite for coding agents; this report documents its construction methodology, scoring protocol, and a cross-model lea…
CRAM: Centroid-Routing and Adaptive MoE for Multimodal Continual Instruction Tuning
Jun-Tao Tang, Zhen-Hao Xie, Yu-Cheng Shi +1
Multimodal Large Language Models (MLLMs) unify heterogeneous vision-language tasks under a shared generative framework via instruction tuning, yet real-world deployment demands con…
DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification
Rui Liu, Dian Yu, Zhenwen Liang +6
Aligning Multimodal Large Language Models (MLLMs) requires reliable reward models, yet existing single-step evaluators can suffer from lazy judging, exploiting language priors over…
SmartSnap: Proactive Evidence Seeking for Self-Verifying Agents
Shaofei Cai, Yulei Qin, Haojia Lin +10
Agentic reinforcement learning (RL) holds great promise for the development of autonomous agents under complex GUI tasks, but its scalability remains severely hampered by the verif…
LTD-Bench: Evaluating Large Language Models by Letting Them Draw
Liuhao Lin, Ke Li, Zihan Xu +5
Current evaluation paradigms for large language models (LLMs) represent a critical blind spot in AI research--relying on opaque numerical metrics that conceal fundamental limitatio…
Training-Free Group Relative Policy Optimization
Yuzheng Cai, Siqi Cai, Yuchen Shi +10
Recent advances in Large Language Model (LLM) agents have demonstrated their promising general capabilities. However, their performance in specialized real-world domains often degr…
Grammar-Guided Evolutionary Search for Discrete Prompt Optimisation
Muzhaffar Hazman, Minh-Khoi Pham, Shweta Soundararajan +10
Prompt engineering has proven to be a crucial step in leveraging pretrained large language models (LLMs) in solving various real-world tasks. Numerous solutions have been proposed…
Automating Expert-Level Medical Reasoning Evaluation of Large Language Models
Shuang Zhou, Wenya Xie, Jiaxi Li +16
As large language models (LLMs) become increasingly integrated into clinical decision-making, ensuring transparent and trustworthy reasoning is essential. However, existing evaluat…
SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?
Yucheng Shi, Tianze Yang, Canyu Chen +4
Large Language Models (LLMs) have shown remarkable capabilities in general domains but often struggle with tasks requiring specialized knowledge. Conventional Retrieval-Augmented G…
LUCY: Linguistic Understanding and Control Yielding Early Stage of Her
Heting Gao, Hang Shao, Xiong Wang +12
The film Her features Samantha, a sophisticated AI audio agent who is capable of understanding both linguistic and paralinguistic information in human speech and delivering real-ti…
AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction
Yuchen Shi, Guochao Jiang, Tian Qiu +1
The relation extraction (RE) in complex scenarios faces challenges such as diverse relation types and ambiguous relations between entities within a single sentence, leading to the…
Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference
Hao Zhen, Yucheng Shi, Yongcan Huang +2
Harnessing the power of Large Language Models (LLMs), this study explores the use of three state-of-the-art LLMs, specifically GPT-3.5-turbo, LLaMA3-8B, and LLaMA3-70B, for crash s…
MGH Radiology Llama: A Llama 3 70B Model for Radiology
Yucheng Shi, Peng Shu, Zhengliang Liu +5
In recent years, the field of radiology has increasingly harnessed the power of artificial intelligence (AI) to enhance diagnostic accuracy, streamline workflows, and improve patie…
P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Models
Guochao Jiang, Zepeng Ding, Yuchen Shi +1
In recent years, the rise of large language models (LLMs) has made it possible to directly achieve named entity recognition (NER) without any demonstration samples or only using a…
Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency
Yuchen Shi, Deqing Yang, Jingping Liu +3
Previous works of negation understanding mainly focus on negation cue detection and scope resolution, without identifying negation subject which is also significant to the downstre…
Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languages
Zihao Li, Yucheng Shi, Zirui Liu +4
The development of Large Language Models (LLMs) relies on extensive text corpora, which are often unevenly distributed across languages. This imbalance results in LLMs performing s…
ToNER: Type-oriented Named Entity Recognition with Generative Language Model
Guochao Jiang, Ziqin Luo, Yuchen Shi +3
In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task. It has…
Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
Yucheng Shi, Qiaoyu Tan, Xuansheng Wu +3
Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge, leading to potentially outdated or inaccurate r…
MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering
Yucheng Shi, Shaochen Xu, Tianze Yang +5
Large Language Models (LLMs), although powerful in general domains, often perform poorly on domain-specific tasks such as medical question answering (QA). In addition, LLMs tend to…