Evidence Selection
11 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
AmbiFC: Fact-Checking Ambiguous Claims with Evidence
Automated fact-checking systems verify claims against evidence to predict their veracity.
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
Open-ended question answering requires models to find appropriate evidence to form wellreasoned, comprehensive and helpful answers.
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation
This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items.
Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering
Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the corresponding QA method.
A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Readers of academic research papers often read with the goal of answering specific questions.
Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network
The proposed model mainly focuses on the evidence selection phase of long document question answering.
SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images
Visual question answering on document images that contain textual, visual, and layout information, called document VQA, has received much attention recently.
Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence.
Halu-J: Critique-Based Hallucination Judge
To address these challenges, we introduce Halu-J, a critique-based hallucination judge with 7 billion parameters.
Benchmarking Retrieval-Augmented Multimomal Generation for Document Question Answering
Document Visual Question Answering (DocVQA) faces dual challenges in processing lengthy multimodal documents (text, images, tables) and performing cross-modal reasoning.