Papers Hint Generation
“Hint Generation” 태그가 달린 논문 12편 · 필터 해제
HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Text-to-SQL generation bridges the gap between natural language and databases, enabling users to query data without requiring SQL expertise. While large language models (LLMs) have significantly advanced the field, chall…
Hint GenerationText to SQLText-To-SQLWikiHint: A Human-Annotated Dataset for Hint Ranking and Generation
The use of Large Language Models (LLMs) has increased significantly with users frequently asking questions to chatbots. In the time when information is readily accessible, it is crucial to stimulate and preserve human co…
DecoderHint GenerationOne Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks
Students often struggle with solving programming problems when learning to code, especially when they have to do it online, with one of the most common disadvantages of working online being the lack of personalized help.…
Hint GenerationExploring Hint Generation Approaches in Open-Domain Question Answering
Automatic Question Answering (QA) systems rely on contextual information to provide accurate answers. Commonly, contexts are prepared through either retrieval-based or generation-based methods. The former involves retrie…
Hint GenerationOpen-Domain Question AnsweringQuestion AnsweringRetrieval+1Navigating the Landscape of Hint Generation Research: From the Past to the Future
Digital education has gained popularity in the last decade, especially after the COVID-19 pandemic. With the improving capabilities of large language models to reason and communicate with users, envisioning intelligent t…
Hint GenerationSelf-LearningTriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions
Nowadays, individuals tend to engage in dialogues with Large Language Models, seeking answers to their questions. In times when such answers are readily accessible to anyone, the stimulation and preservation of human's c…
Hint GenerationInformation RetrievalLarge Language ModelQuestion AnsweringNext-Step Hint Generation for Introductory Programming Using Large Language Models
Large Language Models possess skills such as answering questions, writing essays or solving programming exercises. Since these models are easily accessible, researchers have investigated their capabilities and risks for …
Hint GenerationAutomating Human Tutor-Style Programming Feedback: Leveraging GPT-4 Tutor Model for Hint Generation and GPT-3.5 Student Model for Hint Validation
Generative AI and large language models hold great promise in enhancing programming education by automatically generating individualized feedback for students. We investigate the role of generative AI models in providing…
Hint GenerationAutoHint: Automatic Prompt Optimization with Hint Generation
This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM). While LLMs have demonstrated remarkable ability in achieving high-quality annotation in v…
Hint GenerationIn-Context LearningPrompt EngineeringZero-Shot LearningA Human-ML Collaboration Framework for Improving Video Content Reviews
We deal with the problem of localized in-video taxonomic human annotation in the video content moderation domain, where the goal is to identify video segments that violate granular policies, e.g., community guidelines on…
Hint GenerationRecursive Tree Grammar Autoencoders
Machine learning on trees has been mostly focused on trees as input to algorithms. Much less research has investigated trees as output, which has many applications, such as molecule optimization for drug discovery, or hi…
DecoderDrug DiscoveryHint GenerationTime Series+2A Survey of Automated Programming Hint Generation -- The HINTS Framework
Automated tutoring systems offer the flexibility and scalability necessary to facilitate the provision of high quality and universally accessible programming education. In order to realise the full potential of these sys…
Hint GenerationSurvey