AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A novel method for evaluating query reformulation in Retrieval-Augmented Generation (RAG) without relying on costly manual annotations of relevant documents is proposed, which measures both semantic similarity and downstream retrieval performance.
This work conducts an empirical study of how irrelevant retrieved passages affect downstream generation, and proposes a lightweight, context-size classification module that dynamically predicts how much context is required based on query-specific needs.
Retrieval-Augmented Generation (RAG) helps language models give more accurate answers by using information from external documents. Instead of depending only on what the model learned during training, it can search for relevant information when needed. The quality of the retrieved information is important for getting g...
Evaluation on a multi-page technical PDF document shows that the hybrid retrieval and re-ranking stages together raise retrieval precision and reduce irrelevant or unsupported answers compared with retrieval limited to a single method, supporting the use of this approach for reliable, document-grounded question answeri...
Conversational Query Reformulation (CQR) is an important component in Conversational Question Answering (ConvQA), where user queries are often incomplete, ambiguous, and dependent on previous dialogue turns. Recent CQR approaches have shown the effectiveness of large language models (LLMs) in generating standalone quer...
A novel approach which combines small language models with retrieval-augmented generation in medical question answering to provide accurate and comprehensible information, and supports efficient domain knowledge updates without the need for extensive re-training.
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