16 citations · 38 across the 18 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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.…
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…