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Informativeness

Papers

Showing 201–225 of 735 papers

TitleStatusHype
AdAEM: An Adaptively and Automated Extensible Measurement of LLMs' Value Difference—0
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards—0
Enhancing Abstractiveness of Summarization Models through Calibrated Distillation—0
Converging Dimensions: Information Extraction and Summarization through Multisource, Multimodal, and Multilingual Fusion—0
Diverse mini-batch Active Learning—0
Diversified and Adaptive Negative Sampling on Knowledge Graphs—0
Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation—0
Document Filtering for Long-tail Entities—0
Best Practices for Crowd-based Evaluation of German Summarization: Comparing Crowd, Expert and Automatic Evaluation—0
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification—0
Enhancing Pre-Trained Decision Transformers with Prompt-Tuning Bandits—0
Does Korean defeat phonotactic word segmentation?—0
Contrastive Speaker-Aware Learning for Multi-party Dialogue Generation with LLMs—0
Do Speakers Produce Discourse Connectives Rationally?—0
Contrastive Reasons Detection and Clustering from Online Polarized Debate—0
Do we become wiser with time? On causal equivalence with tiered background knowledge—0
A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization—0
Context-aware Visual Storytelling with Visual Prefix Tuning and Contrastive Learning—0
Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory—0
A Manually Annotated Chinese Corpus for Non-task-oriented Dialogue Systems—0
EdinburghNLP at WNUT-2020 Task 2: Leveraging Transformers with Generalized Augmentation for Identifying Informativeness in COVID-19 Tweets—0
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization—0
Are Current Decoding Strategies Capable of Facing the Challenges of Visual Dialogue?—0
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact—0
Active Scene Learning—0
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