Retrieval Augmented Generation (RAG) framework mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge, yet faces two critical challenges: (1) the distribution gap between user queries and knowledge bases in a specific domain, and (2) incomplete coverage of required knowledge for complex queries. Existing solutions either require task-specific annotations or neglect inherent connections among query, context, and missing knowledge interactions. We propose a reasoning-based missing knowledge RAG framework that synergistically resolves both issues through Chain-of-Thought reasoning. By leveraging open-source LLMs, our method generates structured missing knowledge queries in a single inference pass while aligning query knowledge distributions, and integrates reasoning traces into answer generation. Experiments on open-domain medical and general question answering (QA) datasets demonstrate significant improvements in context recall and answer accuracy. Our approach achieves effective knowledge supplementation without additional training, offering enhanced interpretability and robustness for real-world QA applications.

Recently slot filling has witnessed great development thanks to deep learning and the availability of large-scale annotated data. However, it poses a critical challenge to handle a novel domain whose samples are never seen during training. The recognition performance might be greatly degraded due to severe domain shifts. Most prior works deal with this problem in a two-pass pipeline manner based on metric learning. In practice, these dominant pipeline models may be limited in computational efficiency and generalization capacity because of non-parallel inference and context-free discrete label embeddings. To this end, we re-examine the typical metric-based methods, and propose a new adaptive end-to-end metric learning scheme for the challenging zero-shot slot filling. Considering simplicity, efficiency and generalizability, we present a cascade-style joint learning framework coupled with context-aware soft label representations and slot-level contrastive representation learning to mitigate the data and label shift problems effectively. Extensive experiments on public benchmarks demonstrate the superiority of the proposed approach over a series of competitive baselines.