把神经符号推理融入RAG，让问答系统不仅能答，还能说清为什么。
arXiv:2608.06292v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves question answering by grounding large language models (L…
诊断小型LLM在网络安全问答中的表现，为微调前提供关键评估参考
arXiv:2607.18725v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA)…
针对小众领域问答的上下文对齐新方法，提升大模型在专业场景下的精准回答能力。
arXiv:2607.11891v1 Announce Type: new Abstract: The deployment of large language models (LLMs) in specialized domains like medical diagnostics and fin…
研究医疗大模型问答中的不公平现象，揭示不同人群间的表现差异。
arXiv:2510.17476v2 Announce Type: replace Abstract: Equitable access to reliable health information is vital when integrating AI into healthcare. Yet,…
把检索增强生成用在公共卫生问答上，让大模型回答更可靠，医疗场景值得一看。
arXiv:2607.06641v1 Announce Type: cross Abstract: Large language models (LLMs) achieve promising results on medical question answering benchmarks, yet…
长上下文问答的显存瓶颈，靠这套压缩方案直接缓解，高效又实用。
arXiv:2509.19228v2 Announce Type: replace Abstract: Large Language Models (LLMs) face significant computational challenges when processing long contex…
法语医疗问答场景下LLM适应的权衡实证研究，揭示模型调优的关键取舍与性能边界。
arXiv:2606.19266v1 Announce Type: new Abstract: The development of large language models (LLMs) has led to an increased focus on their adaptation to s…
对海量数据湖上的问答代理瓶颈进行系统梳理，揭示大规模场景下QA Agent的关键设计要素。
arXiv:2606.13904v1 Announce Type: cross Abstract: Exploratory question answering (EQA) over data lakes requires an LLM agent to discover relevant sour…
利用大语言模型提升基于上下文的问答系统答案提取准确率，提供了实验验证与新思路。
arXiv:2606.06197v1 Announce Type: cross Abstract: Question answering (QA) systems have achieved notable progress with the advent of large language mod…
将强化学习引入知识库问答，大幅提升大模型在复杂KBQA任务上的推理与准确率。
arXiv:2512.10999v3 Announce Type: replace Abstract: Knowledge Base Question Answering (KBQA) challenges models to bridge the gap between natural langu…
新研究提出知识依赖估计方法，让问答系统更可靠。
arXiv:2605.28047v1 Announce Type: new Abstract: Reliable question answering requires identifying not only whether an answer is correct, but also which…
基于最新研究的实体链接代理，为问答系统提供精准知识检索与链接能力
arXiv:2508.03865v4 Announce Type: replace Abstract: Some Question Answering (QA) systems rely on knowledge bases (KBs) to provide accurate answers. En…
针对研究的无幻觉问答系统，ACL-Verbatim论文提出消除大模型幻觉的全新方法，学术价值高。
arXiv:2605.21102v1 Announce Type: new Abstract: Academic researchers need efficient and reliable methods for collecting high-quality information from …
神经符号框架，将一阶逻辑自动转化为自然语言语句，革新语义解析与定理验证
arXiv:2605.18155v1 Announce Type: new Abstract: Translating formal language into natural language is a foundational challenge in NLP, driving various …
预注册实验对比向量RAG与LLM编译维基在小型研究语料库上的问答效果，透明严谨。
arXiv:2605.18490v1 Announce Type: new Abstract: We preregistered a comparison of two ways to help an LLM answer questions over a small research corpus…