25 citations · 54 across the 28 of their papers we have counts for
9 papers · 1 filter
GePBench: Evaluating Fundamental Geometric Perception for Multimodal Large Language Models
Shangyu Xing, Changhao Xiang, Yuteng Han +6
Geometric shapes play important roles in both physical world and human cognition. While multimodal large language models (MLLMs) have made significant advancements in visual unders…
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
Xuanwang Zhang, Yunze Song, Yidong Wang +10
Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hall…
AutoSurvey: Large Language Models Can Automatically Write Surveys
Yidong Wang, Qi Guo, Wenjin Yao +10
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial…
Extroversion or Introversion? Controlling The Personality of Your Large Language Models
Yanquan Chen, Zhen Wu, Junjie Guo +2
Large language models (LLMs) exhibit robust capabilities in text generation and comprehension, mimicking human behavior and exhibiting synthetic personalities. However, some LLMs h…
AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability
Fei Zhao, Taotian Pang, Chunhui Li +4
Multimodal Large Language Models (MLLMs) are widely regarded as crucial in the exploration of Artificial General Intelligence (AGI). The core of MLLMs lies in their capability to a…
Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation
Fangxu Yu, Junjie Guo, Zhen Wu +1
Emotion Recognition in Conversation (ERC) involves detecting the underlying emotion behind each utterance within a conversation. Effectively generating representations for utteranc…
Knowledge-aware Dual-side Attribute-enhanced Recommendation
Taotian Pang, Xingyu Lou, Fei Zhao +5
\textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved promising performance. How…
Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering
Yuan Gao, Yiheng Zhu, Yuanbin Cao +6
Open Domain Multi-Hop Question Answering (ODMHQA) plays a crucial role in Natural Language Processing (NLP) by aiming to answer complex questions through multi-step reasoning over…
EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models
Shangyu Xing, Fei Zhao, Zhen Wu +5
Multimodal large language models (MLLMs) have attracted increasing attention in the past few years, but they may still generate descriptions that include objects not present in the…