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.
Libraries
Use these libraries to find Open-Domain Question Answering models and implementationsLatest 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
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
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
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
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
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
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.