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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/danqi/rc-cnn-dailymail

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A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

https://github.com/daod/spring

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One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models

https://github.com/daromog/q-vid

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Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering

https://github.com/darthreca/segmentation-losses-nlp

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Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning

https://github.com/dataminr-ai/event-extraction-as-question-generation-and-answering

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Event Extraction as Question Generation and Answering

https://github.com/datascienceuibk/arabicaqa

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ArabicaQA: A Comprehensive Dataset for Arabic Question Answering

https://github.com/datascienceuibk/chroniclingamericaqa

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ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages

https://github.com/datascienceuibk/complextempqa

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ComplexTempQA: A Large-Scale Dataset for Complex Temporal Question Answering

https://github.com/datascienceuibk/hintqa

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Exploring Hint Generation Approaches in Open-Domain Question Answering

https://github.com/datascienceuibk/multiocr-qa

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MultiOCR-QA: Dataset for Evaluating Robustness of LLMs in Question Answering on Multilingual OCR Texts

https://github.com/datascienceuibk/triviahg

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TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions

https://github.com/david-li0406/dalk

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DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature

https://github.com/david-yoon/propagate-selector

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Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks

https://github.com/davidbrandfonbrener/color-filter-olmo

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CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training

https://github.com/davidmascharka/tbd-nets

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Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning

https://github.com/davidzhangyuanhan/bamboo

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Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy

https://github.com/davidzoltowski/ssmdm

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Unifying and generalizing models of neural dynamics during decision-making

https://github.com/dayihengliu/CRQDA

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Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space

https://github.com/dcgm/biblio-dataset

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BiblioPage: A Dataset of Scanned Title Pages for Bibliographic Metadata Extraction

https://github.com/ddemszky/conversational-uptake

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Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions

https://github.com/ddhruvkr/contradoc

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ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models

https://github.com/ddw2aigroup2cqupt/pa-llava

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PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding

https://github.com/debayan/gett-qa

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GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering

https://github.com/debayan/pnel

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PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs

https://github.com/debayan/sparql-vocab-substitution

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The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing

https://github.com/debrup-61/mathsensei

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MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning

https://github.com/declare-lab/CICERO

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CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues

https://github.com/declare-lab/lg-vqa

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Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts

https://github.com/declare-lab/llm-puzzletest

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Are Language Models Puzzle Prodigies? Algorithmic Puzzles Unveil Serious Challenges in Multimodal Reasoning

https://github.com/declare-lab/puzzle-reasoning

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Are Language Models Puzzle Prodigies? Algorithmic Puzzles Unveil Serious Challenges in Multimodal Reasoning

https://github.com/declare-lab/safety-arithmetic

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Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations

https://github.com/declare-lab/sas-vqa

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Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models

https://github.com/declare-lab/sealing

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Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models

https://github.com/declare-lab/team

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Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering

https://github.com/deep-spin/TVmax

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Sparse and Structured Visual Attention

https://github.com/deep-spin/infinite-video

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$\infty$-Video: A Training-Free Approach to Long Video Understanding via Continuous-Time Memory Consolidation

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