Ask Good Questions for Large Language Models
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Abstract (arXiv)
Recent advances in large language models (LLMs) have significantly improved the performance of dialog systems, yet current approaches often fail to provide accurate guidance of topic due to their inability to discern user confusion in related concepts. To address this, we introduce the Ask-Good-Question (AGQ) framework, which features an improved Concept-Enhanced Item Response Theory (CEIRT) model to better identify users' knowledge levels. Our contributions include applying the CEIRT model along with LLMs to directly generate guiding questions based on the inspiring text, greatly improving information retrieval efficiency during the question & answer process. Through comparisons with other baseline methods, our approach outperforms by significantly enhencing the users' information retrieval experiences.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 28 references that are in the map. First posted on arXiv 19 Aug 2025.
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References · 10
- 2017Attention Is All You Needcited by 60,361
- 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
- 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,302
- 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,121
- 2023Do Large Language Models Know What They Don't Know?cited by 243
- 2017Learning to Ask: Neural Question Generation for Reading Comprehensioncited by 186
- 2023Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentationcited by 147
- 2023Inspecting and Editing Knowledge Representations in Language Modelscited by 141
- 2023R-Tuning: Instructing Large Language Models to Say `I Don't Know'cited by 111
- 2020End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systemscited by 65
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