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Open-Ended Question Answering

Open-ended questions are defined as those that simply pose the question, without imposing any constraints on the format of the response. This distinguishes them from questions with a predetermined answer format.

Papers

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Showing 1–25 of 796 papers

TitleStatusHype
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question AnsweringCode4
Leveraging Latent Features for Local ExplanationsCode2
GreaseLM: Graph REASoning Enhanced Language Models for Question AnsweringCode2
Neptune: The Long Orbit to Benchmarking Long Video UnderstandingCode2
Language Models Can See: Plugging Visual Controls in Text GenerationCode2
Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam GenerationCode2
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language ModelsCode2
M2I: From Factored Marginal Trajectory Prediction to Interactive PredictionCode2
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?Code2
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingCode2
Legal Case Document Summarization: Extractive and Abstractive Methods and their EvaluationCode2
Masked Structural Growth for 2x Faster Language Model Pre-trainingCode1
Cross-modal Contrastive Learning for Multimodal Fake News DetectionCode1
CoSCL: Cooperation of Small Continual Learners is Stronger than a Big OneCode1
BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud RegistrationCode1
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax DiseasesCode1
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the LeastCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
A Few More Examples May Be Worth Billions of ParametersCode1
Background Data Resampling for Outlier-Aware ClassificationCode1
Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender AssignmentCode1
Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question AnsweringCode1
Ranked Voting based Self-Consistency of Large Language ModelsCode1
Argument Mining Driven Analysis of Peer-ReviewsCode1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
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