Open-Domain Question Answering
199 papers with code • 15 benchmarks • 26 datasets
Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.
Benchmarks
These leaderboards are used to track progress in Open-Domain Question Answering
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Libraries
Use these libraries to find Open-Domain Question Answering models and implementations
SQuAD
Natural Questions
TriviaQA
LAMA
WebQuestions
ScienceQA
SearchQA
ELI5
KILT
DuReader
Latest papers with no code
Improving Long Text Understanding with Knowledge Distilled from Summarization Model
Long text understanding is important yet challenging for natural language processing.
Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation
The proposed algorithm compresses the cluttered raw retrieved documents into a compact set of crucial concepts distilled from the informative nodes of AMR by referring to reliable linguistic features.
Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization
This paper introduces Stochastic RAG--a novel approach for end-to-end optimization of retrieval-augmented generation (RAG) models that relaxes the simplifying assumptions of marginalization and document independence, made in most prior work.
Is Table Retrieval a Solved Problem? Join-Aware Multi-Table Retrieval
Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems.
Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization
In addition, it is still unclear how well an OpenQA model can transfer to completely new knowledge domains.
Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts
With our method, the origin language models can cover several times longer contexts while keeping the computing requirements close to the baseline.
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction
Simply concatenating all the retrieved documents brings large amounts of unnecessary tokens for LLMs, which degenerates the efficiency of black-box RAG.
Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering
Finally, based on these observations, we propose a method to mitigate overfitting to specific context quality by introducing bias to the cross-attention distribution, which we demonstrate to be effective in improving the performance of FiD models on different context quality.
To Generate or to Retrieve? On the Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering
Medical open-domain question answering demands substantial access to specialized knowledge.
Answerability in Retrieval-Augmented Open-Domain Question Answering
To address this limitation, we discovered an efficient approach for training models to recognize such excerpts.
