Question Selection
14 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Question Selection
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BIG-bench
Most implemented papers
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
In this paper, we formulate the task of asking clarifying questions in open-domain information-seeking conversational systems.
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing
arghosh/bobcat
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Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
Modelling Sentence Pairs with Tree-structured Attentive Encoder
We describe an attentive encoder that combines tree-structured recursive neural networks and sequential recurrent neural networks for modelling sentence pairs.
Crowdsourced Collective Entity Resolution with Relational Match Propagation
Knowledge bases (KBs) store rich yet heterogeneous entities and facts.
ComQA:Compositional Question Answering via Hierarchical Graph Neural Networks
benywon/ComQA
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In compositional question answering, the systems should assemble several supporting evidence from the document to generate the final answer, which is more difficult than sentence-level or phrase-level QA.
Balancing Test Accuracy and Security in Computerized Adaptive Testing
Computerized adaptive testing (CAT) is a form of personalized testing that accurately measures students' knowledge levels while reducing test length.
Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance.
Fast and Adaptive Questionnaires for Voting Advice Applications
Our findings indicate that employing the IDEAL model both as encoder and decoder, combined with a PosteriorRMSE method for question selection, significantly improves the accuracy of recommendations, achieving 74% accuracy after asking the same number of questions as in the condensed version.
