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Found 9 papers, 8 papers with code
Meta-Chunking: Learning Efficient Text Segmentation via Logical Perception
Retrieval-Augmented Generation (RAG), while serving as a viable complement to large language models (LLMs), often overlooks the crucial aspect of text chunking within its pipeline, which impacts the quality of knowledge-intensive tasks.
TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles
This approach allows for the relatively dynamic generation of evaluation datasets, mitigating the risk of model cheating while aligning assessments more closely with genuine user needs for reasoning capabilities, thus enhancing the reliability of evaluations.
QAEncoder: Towards Aligned Representation Learning in Question Answering System
Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses.
Controllable Text Generation for Large Language Models: A Survey
This paper systematically reviews the latest advancements in CTG for LLMs, offering a comprehensive definition of its core concepts and clarifying the requirements for control conditions and text quality.
HRDE: Retrieval-Augmented Large Language Models for Chinese Health Rumor Detection and Explainability
HealthRCN is the largest known dataset of Chinese health information rumors to date.
Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning
Retrieval-Augmented Generation (RAG) offers a cost-effective approach to injecting real-time knowledge into large language models (LLMs).
Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities
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
For each of these CRUD categories, we have developed comprehensive datasets to evaluate the performance of RAG systems.
UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation
These techniques encompass the use of directed hallucination induction and strategies that deliberately alter authentic text to produce hallucinations.
