Question Answering
2911 papers with code • 131 benchmarks • 362 datasets
Question Answering is the task of answering questions (typically reading comprehension questions), but abstaining when presented with a question that cannot be answered based on the provided context.
Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
Benchmarks
These leaderboards are used to track progress in Question Answering
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Libraries
Use these libraries to find Question Answering models and implementationsDatasets
MS COCO
MNIST
SQuAD
Natural Questions
MS MARCO
ConceptNet
TriviaQA
Reddit
HotpotQA
CNN/Daily Mail
Subtasks
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Open-Ended Question Answering
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Open-Domain Question Answering
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Conversational Question Answering
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Answer Selection
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Answer Selection
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Knowledge Base Question Answering
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Community Question Answering
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Zero-Shot Video Question Answer
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Multiple Choice Question Answering (MCQA)
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Long Form Question Answering
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Cross-Lingual Question Answering
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Science Question Answering
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Generative Question Answering
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Mathematical Question Answering
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Temporal/Casual QA
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Logical Reasoning Question Answering
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Multilingual Machine Comprehension in English Hindi
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True or False Question Answering
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Question Quality Assessment
Latest papers with no code
Multi-hop Question Answering over Knowledge Graphs using Large Language Models
Knowledge graphs (KGs) are large datasets with specific structures representing large knowledge bases (KB) where each node represents a key entity and relations amongst them are typed edges.
TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains
QA pairs are generated by exploiting the large language model (LLM) where the input is a text-formatted table.
When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively
Through our analysis, we demonstrate that Adapt-LLM is able to generate the <RET> token when it determines that it does not know how to answer a question, indicating the need for IR, while it achieves notably high accuracy levels when it chooses to rely only on its parametric memory.
QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering
Extractive Question Answering (EQA) in Machine Reading Comprehension (MRC) often faces the challenge of dealing with semantically identical but format-variant inputs.
Neuro-Vision to Language: Image Reconstruction and Language enabled Interaction via Brain Recordings
This extractor consolidates multi-level visual features into one network, simplifying integration with Large Language Models (LLMs).
Capabilities of Gemini Models in Medicine
We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin.
QANA: LLM-based Question Generation and Network Analysis for Zero-shot Key Point Analysis and Beyond
The proliferation of social media has led to information overload and increased interest in opinion mining.
Automated Construction of Theme-specific Knowledge Graphs
Specifically, we start with an entity ontology of the theme from Wikipedia, based on which we then generate candidate relations by Large Language Models (LLMs) to construct a relation ontology.
Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities
The results showed that the efficiency gained through vocabulary expansion had no negative impact on performance, except for the summarization task, and that the combined use of parallel corpora enhanced translation ability.
Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering
Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct answer from a set of options based on a given passage and question.
