16 citations · 40 across the 43 of their papers we have counts for
12 papers · 1 filter
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…
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…
: 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…
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…