19 citations · 65 across the 63 of their papers we have counts for
19 papers · 1 filter
FTII-Bench: A Comprehensive Multimodal Benchmark for Flow Text with Image Insertion
Jiacheng Ruan, Yebin Yang, Zehao Lin +4
Benefiting from the revolutionary advances in large language models (LLMs) and foundational vision models, large vision-language models (LVLMs) have also made significant progress.…
Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perception
Jihao Zhao, Zhiyuan Ji, Yuchen Feng +5
While Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for boosting large language models (LLMs) in knowledge-intensive tasks, it often overlooks the crucia…
MM-CamObj: A Comprehensive Multimodal Dataset for Camouflaged Object Scenarios
Jiacheng Ruan, Wenzhen Yuan, Zehao Lin +5
Large visual-language models (LVLMs) have achieved great success in multiple applications. However, they still encounter challenges in complex scenes, especially those involving ca…
QAEncoder: Towards Aligned Representation Learning in Question Answering Systems
Zhengren Wang, Qinhan Yu, Shida Wei +6
Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses. However, the inherent gap between user queries and relevant documents hinders…
Attention Heads of Large Language Models: A Survey
Zifan Zheng, Yezhaohui Wang, Yuxin Huang +5
Since the advent of ChatGPT, Large Language Models (LLMs) have excelled in various tasks but remain as black-box systems. Understanding the reasoning bottlenecks of LLMs has become…
Xinyu: An Efficient LLM-based System for Commentary Generation
Yiquan Wu, Bo Tang, Chenyang Xi +13
Commentary provides readers with a deep understanding of events by presenting diverse arguments and evidence. However, creating commentary is a time-consuming task, even for skille…
Controllable Text Generation for Large Language Models: A Survey
Xun Liang, Hanyu Wang, Yezhaohui Wang +8
In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world applications, LLMs must meet increasingly…
HRDE: Retrieval-Augmented Large Language Models for Chinese Health Rumor Detection and Explainability
Yanfang Chen, Ding Chen, Shichao Song +5
As people increasingly prioritize their health, the speed and breadth of health information dissemination on the internet have also grown. At the same time, the presence of false h…
Internal Consistency and Self-Feedback in Large Language Models: A Survey
Xun Liang, Shichao Song, Zifan Zheng +8
Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations. To address these, studies prefixed with "Self-" such as Self-Consistency, Self-Improve, a…
: Language Modeling with Explicit Memory
Hongkang Yang, Zehao Lin, Wenjin Wang +13
The training and inference of large language models (LLMs) are together a costly process that transports knowledge from raw data to meaningful computation. Inspired by the memory h…
FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models
Junyi Zhu, Shuochen Liu, Yu Yu +6
Large language models (LLMs) excel in generating coherent text, but they often struggle with context awareness, leading to inaccuracies in tasks requiring faithful adherence to pro…
Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning
Xun Liang, Simin Niu, Zhiyu li +7
Retrieval-Augmented Generation (RAG) offers a cost-effective approach to injecting real-time knowledge into large language models (LLMs). Nevertheless, constructing and validating…
Improving Generalization and Convergence by Enhancing Implicit Regularization
Mingze Wang, Jinbo Wang, Haotian He +6
In this work, we propose an Implicit Regularization Enhancement (IRE) framework to accelerate the discovery of flat solutions in deep learning, thereby improving generalization and…
xFinder: Large Language Models as Automated Evaluators for Reliable Evaluation
Qingchen Yu, Zifan Zheng, Shichao Song +4
The continuous advancement of large language models (LLMs) has brought increasing attention to the critical issue of developing fair and reliable methods for evaluating their perfo…
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy
Yu Zhu, Chuxiong Sun, Wenfei Yang +8
Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach to ensure Large Language Models (LLMs) align with human values. However, existing RLHF methods require…
NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism
Miao Li, Ming-Bin Chen, Bo Tang +8
We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. Our c…
Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs
Xun Liang, Hanyu Wang, Shichao Song +5
Controlled Text Generation (CTG) aims to produce texts that exhibit specific desired attributes. In this study, we introduce a pluggable CTG framework for Large Language Models (LL…
Grimoire is All You Need for Enhancing Large Language Models
Ding Chen, Shichao Song, Qingchen Yu +4
In-context Learning (ICL) is one of the key methods for enhancing the performance of large language models on specific tasks by providing a set of few-shot examples. However, the I…
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models
Yuanjie Lyu, Zhiyu Li, Simin Niu +7
Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This method addresse…