4 citations · 6 across the 16 of their papers we have counts for
5 papers · 1 filter
Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training
Lei Liu, Hao Zhu, Yue Shen +4
Continual Pre-training (CPT) serves as a fundamental approach for adapting foundation models to domain-specific applications. Scaling laws for pre-training define a power-law relat…
HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented Generation for Multi-hop Question Answering
Duolin Sun, Dan Yang, Yue Shen +7
The Retrieval-Augmented Generation (RAG) approach enhances question-answering systems and dialogue generation tasks by integrating information retrieval (IR) technologies with larg…
DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval
Duolin Sun, Meixiu Long, Dan Yang +9
Retrieval-augmented generation has achieved strong performance on knowledge-intensive tasks where query-document relevance can be identified through direct lexical or semantic matc…
PRGB Benchmark: A Robust Placeholder-Assisted Algorithm for Benchmarking Retrieval-Augmented Generation
Zhehao Tan, Yihan Jiao, Dan Yang +7
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge, where the LLM's ability to generate responses based on the combination…
HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation
YiHan Jiao, ZheHao Tan, Dan Yang +5
Retrieval-augmented generation (RAG) has become a fundamental paradigm for addressing the challenges faced by large language models in handling real-time information and domain-spe…