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Question Answering

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하위 태스크: 19
평가표: 153
보존된 평가 기록: 1,781

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 )

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/nctu-eva-lab/antifakeprompt

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AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors

https://github.com/nctu-eva-lab/vhd11k

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T2Vs Meet VLMs: A Scalable Multimodal Dataset for Visual Harmfulness Recognition

https://github.com/ndnlp/parity

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Overcoming a Theoretical Limitation of Self-Attention

https://github.com/necla-ml/SNLI-VE

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Visual Entailment Task for Visually-Grounded Language Learning

https://github.com/nedjmaou/varifocal_fact_checking_qg

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Varifocal Question Generation for Fact-checking

https://github.com/neelabhsinha/qa-prompting

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QA-prompting: Improving Summarization with Large Language Models using Question-Answering

https://github.com/neelabhsinha/vlm-selection-tasks-domains-knowledge-type

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Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge Types

https://github.com/nelson-liu/lost-in-the-middle

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Lost in the Middle: How Language Models Use Long Contexts

https://github.com/nercms-mmap/friendsqa

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FriendsQA: A New Large-Scale Deep Video Understanding Dataset with Fine-grained Topic Categorization for Story Videos

https://github.com/netmanaiops/opseval-datasets

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OpsEval: A Comprehensive IT Operations Benchmark Suite for Large Language Models

https://github.com/neubig/globalutility

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Systematic Inequalities in Language Technology Performance across the World's Languages

https://github.com/neuhai/storysparkqa

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StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children's Story-Based Learning

https://github.com/neuir/hippo

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HIPPO: Enhancing the Table Understanding Capability of Large Language Models through Hybrid-Modal Preference Optimization

https://github.com/neuir/m2rag

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Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts

https://github.com/neulab/ragged

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RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems

https://github.com/neuralaudio/hear-eval-kit

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HEAR: Holistic Evaluation of Audio Representations

https://github.com/neuralmind-ai/information-extraction-t5

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Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents

https://github.com/neuralmind-ai/portuguese-bert

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Portuguese Named Entity Recognition using BERT-CRF

https://github.com/neuralmind-ai/visconde

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Visconde: Multi-document QA with GPT-3 and Neural Reranking

https://github.com/neurasearch/geometry-diagram-description

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GOLD: Geometry Problem Solver with Natural Language Description

https://github.com/neurasearch/neurips-2022-submission-3358

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ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler

https://github.com/neuroailab/lr-identify

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Identifying Learning Rules From Neural Network Observables

https://github.com/nevermorelch/ccg

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Consistency of Compositional Generalization across Multiple Levels

https://github.com/nevermorelch/mscg

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Multi-Sourced Compositional Generalization in Visual Question Answering

https://github.com/nguyentthong/MAMA

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MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning

https://github.com/nguyentthong/Paper-VLP

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Vision-and-Language Pretraining

https://github.com/nianlonggu/memsum-dqa

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MemSum-DQA: Adapting An Efficient Long Document Extractive Summarizer for Document Question Answering

https://github.com/niansong1996/retrieval_marginalization

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Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization

https://github.com/nicofirst1/speaker-adaptation

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Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind

https://github.com/nicola-decao/KnowledgeEditor

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Editing Factual Knowledge in Language Models

https://github.com/nicola-decao/diffmask

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How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking

https://github.com/night-chen/toolqa

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ToolQA: A Dataset for LLM Question Answering with External Tools

https://github.com/nightingal3/fig-qa

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Testing the Ability of Language Models to Interpret Figurative Language

https://github.com/nightingal3/lm-compositionality

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Are Representations Built from the Ground Up? An Empirical Examination of Local Composition in Language Models

https://github.com/nijianmo/arc-etrr-code

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Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering

https://github.com/nika2312/qa_explaination

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Explaining Question Answering Models through Text Generation

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