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Answer Generation

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Showing 126–150 of 280 papers

TitleStatusHype
Causal Reasoning through Two Layers of Cognition for Improving Generalization in Visual Question Answering—0
CC-RAG: Structured Multi-Hop Reasoning via Theme-Based Causal Graphs—0
Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark—0
CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System—0
Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs—0
Conclusion-Supplement Answer Generation for Non-Factoid Questions—0
Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model—0
Context Embeddings for Efficient Answer Generation in RAG—0
Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context—0
Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering—0
Conversational QA Dataset Generation with Answer Revision—0
CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG—0
Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation—0
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization—0
Deep Bayesian Active Learning for Multiple Correct Outputs—0
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems—0
EDUQA: Educational Domain Question Answering System using Conceptual Network Mapping—0
Enhancing Multi-hop Reasoning in Vision-Language Models via Self-Distillation with Multi-Prompt Ensembling—0
Evaluating Question Answering Evaluation—0
Evidence Contextualization and Counterfactual Attribution for Conversational QA over Heterogeneous Data with RAG Systems—0
EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering—0
Parrot Mind: Towards Explaining the Complex Task Reasoning of Pretrained Large Language Models with Template-Content Structure—0
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models—0
Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs—0
Extraction Meets Abstraction: Ideal Answer Generation for Biomedical Questions—0
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