Interactive Classification by Asking Informative Questions
We study the potential for interaction in natural language classification. We add a limited form of interaction for intent classification, where users provide an initial query using natural language, and the system asks for additional information using binary or multi-choice questions. At each turn, our system decides between asking the most informative question or making the final classification prediction.The simplicity of the model allows for bootstrapping of the system without interaction data, instead relying on simple crowdsourcing tasks. We evaluate our approach on two domains, showing the benefit of interaction and the advantage of learning to balance between asking additional questions and making the final prediction.
Code (1)
Tasks
ClassificationGeneral Classificationintent-classificationIntent ClassificationSimilar Papers 제목 키워드 기반
Asking More Informative Questions for Grounded Retrieval
When a model is trying to gather information in an interactive setting, it benefits from asking informative questions. However, in the case of a grounded multi-turn image identification task, previous studies have been c…
Question AnsweringQuestion SelectionRetrievalVisual Question Answering+1Open-domain clarification question generation without question examples
An overarching goal of natural language processing is to enable machines to communicate seamlessly with humans. However, natural language can be ambiguous or unclear. In cases of uncertainty, humans engage in an interact…
Question GenerationQuestion-GenerationActive Preference Inference using Language Models and Probabilistic Reasoning
Actively inferring user preferences, for example by asking good questions, is important for any human-facing decision-making system. Active inference allows such systems to adapt and personalize themselves to nuanced ind…
Decision MakingMAQuA: Adaptive Question-Asking for Multidimensional Mental Health Screening using Item Response Theory
Recent advances in large language models (LLMs) offer new opportunities for scalable, interactive mental health assessment, but excessive querying by LLMs burdens users and is inefficient for real-world screening across …
Learning to Retrieve Videos by Asking Questions
The majority of traditional text-to-video retrieval systems operate in static environments, i.e., there is no interaction between the user and the agent beyond the initial textual query provided by the user. This can be …
AI AgentRetrievalText to Video RetrievalVideo Retrieval