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Tag

#question-answering

5 articles tagged #question-answering

arxivMay 28bullish

A Benchmark Construction and Evaluation Framework for Specialist Domains: Case Study on Defense-related Documents

arXiv:2604.17943v2 Announce Type: replace Abstract: RAG-based question-answering (QA) in specialist domains faces a cold-start problem: lack of evaluative benchmarks and absence of labeled data for post-training. We present DoRA (Domain-oriented RAG Assessment), a novel benchmark construction and ev

ME1 model#benchmark#evaluation#specialist-domainsRead on arxiv →
arxivMay 14bullish

State-Centric Decision Process

arXiv:2605.12755v1 Announce Type: new Abstract: Language environments such as web browsers, code terminals, and interactive simulations emit raw text rather than states, and provide none of the runtime structure that MDP analysis requires. No explicit state space, no observation-to-state mapping, no

#planning#scientific-exploration#question-answeringRead on arxiv →
arxivApr 23

Knowledge Capsules: Structured Nonparametric Memory Units for LLMs

arXiv:2604.20487v1 Announce Type: cross Abstract: Large language models (LLMs) encode knowledge in parametric weights, making it costly to update or extend without retraining. Retrieval-augmented generation (RAG) mitigates this limitation by appending retrieved text to the input, but operates purely

#research#language-models#knowledge-retrievalRead on arxiv →
arxivApr 21bullish

Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints

arXiv:2507.16727v3 Announce Type: replace Abstract: Improving the reliability of large language models (LLMs) is critical for deploying them in real-world scenarios. In this paper, we propose \textbf{Deliberative Searcher}, the first framework to integrate certainty calibration with retrieval-based

#reliability#research#question-answeringRead on arxiv →
arxivApr 7bullish

PDF Retrieval Augmented Question Answering

arXiv:2506.18027v2 Announce Type: replace Abstract: This paper presents an advancement in Question-Answering (QA) systems using a Retrieval Augmented Generation (RAG) framework to enhance information extraction from PDF files. Recognizing the richness and diversity of data within PDFs--including tex

RELA2 models#question-answering#multimodal#information-extractionRead on arxiv →
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