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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/microsoft/PICa

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An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA

https://github.com/microsoft/ProphetNet

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CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

https://github.com/microsoft/TAP

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TAP: Text-Aware Pre-training for Text-VQA and Text-Caption

https://github.com/microsoft/UniTAB

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UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling

https://github.com/microsoft/batchprompt

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BatchPrompt: Accomplish more with less

https://github.com/microsoft/biogpt

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BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining

https://github.com/microsoft/cream

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Searching the Search Space of Vision Transformer

https://github.com/microsoft/dialoglm

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DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization

https://github.com/microsoft/fiber

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Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone

https://github.com/microsoft/hitab

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HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation

https://github.com/microsoft/kid

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Knowledge Infused Decoding

https://github.com/microsoft/lavender

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LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling

https://github.com/microsoft/lmops

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Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

https://github.com/microsoft/macaw

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Macaw: An Extensible Conversational Information Seeking Platform

https://github.com/microsoft/pengi

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Pengi: An Audio Language Model for Audio Tasks

https://github.com/microsoft/reina

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Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data

https://github.com/microsoft/seed-encoder

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Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder

https://github.com/microsoft/simxns

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Allies: Prompting Large Language Model with Beam Search

https://github.com/microsoft/swinbert

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SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning

https://github.com/microsoft/taser

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Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering

https://github.com/microsoft/tora

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ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving

https://github.com/microsoft/unilm

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Unified Language Model Pre-training for Natural Language Understanding and Generation

https://github.com/microsoft/unilm/blob/master/Diff-Transformer/

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Differential Transformer

https://github.com/microsoft/unilm/tree/master/beit

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Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

https://github.com/microsoft/unilm/tree/master/layoutlmv3

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LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking

https://github.com/microsoft/xpretrain

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Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning

https://github.com/microsoft/xtreme-distil-transformers

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XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation

https://github.com/midas-research/video-persuasion

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A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot

https://github.com/migalkin/SQuAD-es-mt

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El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks

https://github.com/migalkin/rwl

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Weisfeiler and Leman Go Relational

https://github.com/mightyzau/infmllm

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InfMLLM: A Unified Framework for Visual-Language Tasks

https://github.com/mihir3009/logicbench

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LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models

https://github.com/mihirp1998/Disentangling-3D-Prototypical-Nets

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Disentangling 3D Prototypical Networks For Few-Shot Concept Learning

https://github.com/mikecovlee/mLoRA

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MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts

https://github.com/mikejqzhang/mitigating_misalignment

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Mitigating Temporal Misalignment by Discarding Outdated Facts

https://github.com/mikemccabe210/mapper_comparison

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Mapper Comparison with Wasserstein Metrics

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