Open-Domain Question Answering
195 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
Spiral of Silences: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering
The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent.
KazQAD: Kazakh Open-Domain Question Answering Dataset
We introduce KazQAD -- a Kazakh open-domain question answering (ODQA) dataset -- that can be used in both reading comprehension and full ODQA settings, as well as for information retrieval experiments.
Multi-Granularity Guided Fusion-in-Decoder
eunseongc/mgfid
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In Open-domain Question Answering (ODQA), it is essential to discern relevant contexts as evidence and avoid spurious ones among retrieved results.
ArabicaQA: A Comprehensive Dataset for Arabic Question Answering
In conclusion, ArabicaQA, AraDPR, and the benchmarking of LLMs in Arabic question answering offer significant advancements in the field of Arabic NLP.
Denoising Table-Text Retrieval for Open-Domain Question Answering
deokhk/dotter
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Previous studies in table-text open-domain question answering have two common challenges: firstly, their retrievers can be affected by false-positive labels in training datasets; secondly, they may struggle to provide appropriate evidence for questions that require reasoning across the table.
Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers
ibm-ecosystem-engineering/blended-rag
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Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a private knowledge base of documents with Large Language Models (LLM) to build Generative Q\&A (Question-Answering) systems.
Imagination Augmented Generation: Learning to Imagine Richer Context for Question Answering over Large Language Models
xnhyacinth/iag
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Retrieval-Augmented-Generation and Gener-ation-Augmented-Generation have been proposed to enhance the knowledge required for question answering over Large Language Models (LLMs).
DESIRE-ME: Domain-Enhanced Supervised Information REtrieval using Mixture-of-Experts
Open-domain question answering requires retrieval systems able to cope with the diverse and varied nature of questions, providing accurate answers across a broad spectrum of query types and topics.
Beyond Memorization: The Challenge of Random Memory Access in Language Models
sail-sg/lm-random-memory-access
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Through carefully-designed synthetic tasks, covering the scenarios of full recitation, selective recitation and grounded question answering, we reveal that LMs manage to sequentially access their memory while encountering challenges in randomly accessing memorized content.
REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering
rucaibox/rear
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By combining the improvements in both architecture and training, our proposed REAR can better utilize external knowledge by effectively perceiving the relevance of retrieved documents.
