HintMiner is an automatic tool that mines question hints from web Q&A posts using a language‑model‑based MiningNet. It retrieves many Q&A posts, extracts hints via a transformer‑based encoder‑decoder with copying mechanisms, and is trained with a self‑supervised objective on large online data. Evaluated on 60,000 Stack Overflow questions, HintMiner achieves an average BLEU score of 36.17% and ROUGE‑2 of 36.29%.
arXiv:2502.00857v2 Announce Type: replace Abstract: Large Language Models (LLMs) increasingly provide direct answers to user questions, raising concerns about reduced engagement in critical thinking...
arXiv:2504. 07385v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluation poses significant challenges in cost, scalability, and completeness.
arXiv:2606. 06197v1 Announce Type: cross Abstract: Question answering (QA) systems have achieved notable progress with the advent of large language models (LLMs).
arXiv:2609.07093v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely a...
arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.
The paper presents a decision‑support system that enhances retrieval‑augmented generation (RAG) for customer contact centers by first identifying customer questions in real time. If a query matches a frequently asked question (FAQ), the system retrieves the answer directly from the FAQ database; otherwise it generates an answer via RAG, delivering responses to agents within two seconds. The approach reduces manual query formulation, lowers average handling times, and cuts operational costs, and it includes an automated workflow that uses LLMs to extract FAQs from historical transcripts when none are predefined.
CONTRA is a training‑free method that discovers and qualifies behavior‑changing questions for selective clarification in large language model (LLM) code generation. It first generates candidate questions, filters out those unrelated to required behavior or already resolved, then creates programs conditioned on two plausible answers to check for stable behavioral differences on shared inputs. Experiments on ClarifyCodeBench show that CONTRA achieves the highest F1 across four coding agents, outperforming baselines by 13.88 percentage points, and it is also implemented as a Claude Code plugin for practical use.
arXiv:2505.16227v4 Announce Type: replace-cross Abstract: Personalizing jargon detection and explanation is essential for making technical documents accessible to readers with diverse disciplinary ba...
arXiv:2606. 29090v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty.
arXiv:2606.13120v2 Announce Type: replace Abstract: Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing be...
arXiv:2510. 01427v3 Announce Type: replace Abstract: At the core of Deep Research is knowledge mining, the task of extracting structured information from massive unstructured text in response to user instructions.
arXiv:2606. 10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved.