6 citations · 35 across the 27 of their papers we have counts for
28 papers
SonicBench: Dissecting the Physical Perception Bottleneck in Large Audio Language Models
Yirong Sun, Yanjun Chen, Xin Qiu +8
Large Audio Language Models (LALMs) excel at semantic and paralinguistic tasks, yet their ability to perceive the fundamental physical attributes of audio such as pitch, loudness,…
A Survey on Large Language Model Benchmarks
Shiwen Ni, Guhong Chen, Shuaimin Li +11
In recent years, with the rapid development of the depth and breadth of large language models' capabilities, various corresponding evaluation benchmarks have been emerging in incre…
MGHFT: Multi-Granularity Hierarchical Fusion Transformer for Cross-Modal Sticker Emotion Recognition
Jian Chen, Yuxuan Hu, Haifeng Lu +4
Although pre-trained visual models with text have demonstrated strong capabilities in visual feature extraction, sticker emotion understanding remains challenging due to its relian…
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift
Yong Zhang, Feng Liang, Guanghu Yuan +3
Federated learning (FL) enables collaborative training of a global model in the centralized server with data from multiple parties while preserving privacy. However, data heterogen…
Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation
Dingwei Chen, Ziqiang Liu, Feiteng Fang +6
Large Language Models (LLMs) demonstrate remarkable capabilities in text understanding and generation. However, their tendency to produce factually inconsistent outputs, commonly r…
AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling
Ancheng Xu, Di Yang, Renhao Li +13
Traditional in-person psychological counseling remains primarily niche, often chosen by individuals with psychological issues, while online automated counseling offers a potential…
PersonaMath: Boosting Mathematical Reasoning via Persona-Driven Data Augmentation
Jing Luo, Longze Chen, Run Luo +12
While closed-source Large Language Models (LLMs) demonstrate strong mathematical problem-solving abilities, open-source models still face challenges with such tasks. To bridge this…
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification
Yuxuan Hu, Chenwei Zhang, Min Yang +3
With the rapid development of deep learning methods, there have been many breakthroughs in the field of text classification. Models developed for this task have been shown to achie…
Training on the Benchmark Is Not All You Need
Shiwen Ni, Xiangtao Kong, Chengming Li +4
The success of Large Language Models (LLMs) relies heavily on the huge amount of pre-training data learned in the pre-training phase. The opacity of the pre-training process and th…
Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches
Yanjie Dong, Haijun Zhang, Chengming Li +3
Since the release of GPT2-1.5B in 2019, the large language models (LLMs) have evolved from specialized deep models to versatile foundation models. While demonstrating remarkable ze…
Lower Layers Matter: Alleviating Hallucination via Multi-Layer Fusion Contrastive Decoding with Truthfulness Refocused
Dingwei Chen, Feiteng Fang, Shiwen Ni +6
Large Language Models (LLMs) have demonstrated exceptional performance across various natural language processing tasks. However, they occasionally generate inaccurate and counterf…
AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents
Guhong Chen, Liyang Fan, Zihan Gong +8
Current research in LLM-based simulation systems lacks comprehensive solutions for modeling real-world court proceedings, while existing legal language models struggle with dynamic…
APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation
Yuxuan Hu, Minghuan Tan, Chenwei Zhang +5
Empathetic response generation is designed to comprehend the emotions of others and select the most appropriate strategies to assist them in resolving emotional challenges. Empathy…
Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey
Feng Liang, Zhen Zhang, Haifeng Lu +4
With rapidly increasing distributed deep learning workloads in large-scale data centers, efficient distributed deep learning framework strategies for resource allocation and worklo…
CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling
Chenhao Zhang, Renhao Li, Minghuan Tan +7
Using large language models (LLMs) to assist psychological counseling is a significant but challenging task at present. Attempts have been made on improving empathetic conversation…
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
Ziqiang Liu, Feiteng Fang, Xi Feng +23
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks. In response, numerous challe…
CPsyExam: A Chinese Benchmark for Evaluating Psychology using Examinations
Jiahao Zhao, Jingwei Zhu, Minghuan Tan +8
In this paper, we introduce a novel psychological benchmark, CPsyExam, constructed from questions sourced from Chinese language examinations. CPsyExam is designed to prioritize psy…
CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment
Feiteng Fang, Liang Zhu, Min Yang +6
Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial a…
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property
Shiwen Ni, Minghuan Tan, Yuelin Bai +9
Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. However, there is limited understanding of how well LLMs p…
Layer-wise Regularized Dropout for Neural Language Models
Shiwen Ni, Min Yang, Ruifeng Xu +2
Among the various pre-trained neural language models that are popular today, dropout is already an indispensable regularization technique. To solve the inconsistency between traini…
E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models
Jinchang Hou, Chang Ao, Haihong Wu +8
With the accelerating development of Large Language Models (LLMs), many LLMs are beginning to be used in the Chinese K-12 education domain. The integration of LLMs and education is…
Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models
Shiwen Ni, Dingwei Chen, Chengming Li +3
Recent advancements in Large Language Models (LLMs) have showcased their remarkable capabilities in text understanding and generation. However, even stronger LLMs are susceptible t…
Expression Syntax Information Bottleneck for Math Word Problems
Jing Xiong, Chengming Li, Min Yang +2
Math Word Problems (MWP) aims to automatically solve mathematical questions given in texts. Previous studies tend to design complex models to capture additional information in the…
Self-Distillation with Meta Learning for Knowledge Graph Completion
Yunshui Li, Junhao Liu, Chengming Li +1
In this paper, we propose a selfdistillation framework with meta learning(MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddin…