How does the domain shift between synthetic training data and target benchmarks (e.g., HotPotQA vs MuSiQue) af

Abstract

Abstract: Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases. This enhances the accuracy and credibility of the generation, particularly for knowledge-intensive tasks, and allows for continuous knowledge updates and integration of domain-specific information. RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of exte

Research Question

How does the domain shift between synthetic training data and target benchmarks (e.g., HotPotQA vs MuSiQue) affect the MRR@10 improvement from adversarial retriever training across different hop complexities?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance