19 citations · 61 across the 17 of their papers we have counts for
19 papers
PDEU-Bench: Benchmarking the Personalized Planning Lifecycle of Tool-Calling LLM Agents
Huayi Lai, Shichao Song, Qingchen Yu +4
Large language model (LLM) agents are evolving from tool-calling systems that execute isolated instructions into task-oriented agents that pursue user goals through sustained, mult…
RoleCDE:Benchmarking and Mitigating Role-Alignment Trade-offs in Role-Playing Agents
Huayi Lai, Shichao Song, Simin Niu +5
Role-playing agents(RPAs) are widely used to steer large language models(LLMs) toward role-consistent behavior, yet existing benchmarks mainly evaluate surface-level fidelity and o…
MemOS: A Memory OS for AI System
Zhiyu Li, Chenyang Xi, Chunyu Li +36
Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the…
MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models
Zhiyu Li, Shichao Song, Hanyu Wang +19
Large Language Models (LLMs) have emerged as foundational infrastructure in the pursuit of Artificial General Intelligence (AGI). Despite their remarkable capabilities in language…
SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models
Xun Liang, Hanyu Wang, Huayi Lai +7
Large Language Models have achieved remarkable success across various natural language processing tasks, yet their high computational cost during inference remains a major bottlene…
SurveyX: Academic Survey Automation via Large Language Models
Xun Liang, Jiawei Yang, Yezhaohui Wang +11
Large Language Models (LLMs) have demonstrated exceptional comprehension capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for automated sur…
SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model
Xun Liang, Simin Niu, Zhiyu Li +8
The indexing-retrieval-generation paradigm of retrieval-augmented generation (RAG) has been highly successful in solving knowledge-intensive tasks by integrating external knowledge…
TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles
Qingchen Yu, Shichao Song, Ke Fang +5
As the application of Large Language Models (LLMs) expands, the demand for reliable evaluations increases. Existing LLM evaluation benchmarks primarily rely on static datasets, mak…
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…
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…
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…
Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities
Xiaomin Yu, Yezhaohui Wang, Yanfang Chen +5
In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods.…
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…
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…
UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation
Xun Liang, Shichao Song, Simin Niu +8
Large language models (LLMs) have emerged as pivotal contributors in contemporary natural language processing and are increasingly being applied across a diverse range of industrie…