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Question Answering

2909 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 )

Libraries

Use these libraries to find Question Answering models and implementations
huggingface/transformers
27 papers
125,425
facebookresearch/ParlAI
5 papers
10,432
dmlc/gluon-nlp
5 papers
2,549
faceonlive/ai-research
5 papers
186
See all 11 libraries.

Datasets

Subtasks

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Can a Multichoice Dataset be Repurposed for Extractive Question Answering?

no code yet • 26 Apr 2024

The rapid evolution of Natural Language Processing (NLP) has favored major languages such as English, leaving a significant gap for many others due to limited resources.

 
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2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion

no code yet • 26 Apr 2024

To tackle this challenging MMNER task on the dataset, we introduce a new model called 2M-NER, which aligns the text and image representations using contrastive learning and integrates a multimodal collaboration module to effectively depict the interactions between the two modalities.

 
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TIGQA:An Expert Annotated Question Answering Dataset in Tigrinya

no code yet • 26 Apr 2024

The absence of explicitly tailored, accessible annotated datasets for educational purposes presents a notable obstacle for NLP tasks in languages with limited resources. This study initially explores the feasibility of using machine translation (MT) to convert an existing dataset into a Tigrinya dataset in SQuAD format.

 
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Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering

no code yet • 26 Apr 2024

In customer service technical support, swiftly and accurately retrieving relevant past issues is critical for efficiently resolving customer inquiries.

 
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Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models

no code yet • 25 Apr 2024

In the realm of Medical Visual Language Models (Med-VLMs), the quest for universal efficient fine-tuning mechanisms remains paramount, especially given researchers in interdisciplinary fields are often extremely short of training resources, yet largely unexplored.

 
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Türkçe Dil Modellerinin Performans Karşılaştırması Performance Comparison of Turkish Language Models

no code yet • 25 Apr 2024

Yet, despite the increasing number of these models, there is no comprehensive comparison of their performance for Turkish.

 
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From Local to Global: A Graph RAG Approach to Query-Focused Summarization

no code yet • 24 Apr 2024

To combine the strengths of these contrasting methods, we propose a Graph RAG approach to question answering over private text corpora that scales with both the generality of user questions and the quantity of source text to be indexed.

 
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Assessing The Potential Of Mid-Sized Language Models For Clinical QA

no code yet • 24 Apr 2024

Large language models, such as GPT-4 and Med-PaLM, have shown impressive performance on clinical tasks; however, they require access to compute, are closed-source, and cannot be deployed on device.

 
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Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering

no code yet • 24 Apr 2024

Vision-language models, while effective in general domains and showing strong performance in diverse multi-modal applications like visual question-answering (VQA), struggle to maintain the same level of effectiveness in more specialized domains, e. g., medical.

 
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KS-LLM: Knowledge Selection of Large Language Models with Evidence Document for Question Answering

no code yet • 24 Apr 2024

Large language models (LLMs) suffer from the hallucination problem and face significant challenges when applied to knowledge-intensive tasks.

 
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