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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/SoumyaTGhosh/hs-bnn-public

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Model Selection in Bayesian Neural Networks via Horseshoe Priors

https://github.com/SouravDutta91/bert-attn-analysis-qa

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Whatcha lookin' at? DeepLIFTing BERT's Attention in Question Answering

https://github.com/SparkJiao/Self-Training-MRC

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A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction

https://github.com/SpencerWhitehead/novelvqa

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Separating Skills and Concepts for Novel Visual Question Answering

https://github.com/SpencerWhitehead/vqap2

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Learning from Lexical Perturbations for Consistent Visual Question Answering

https://github.com/StonyBrookNLP/deformer

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DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering

https://github.com/StonyBrookNLP/multee

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Repurposing Entailment for Multi-Hop Question Answering Tasks

https://github.com/SunDoge/L-GCN

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Location-aware Graph Convolutional Networks for Video Question Answering

https://github.com/SuperSupermoon/MedViLL

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Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training

https://github.com/THU-KEG/AtomR

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AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge Reasoning

https://github.com/THUDM/CogQA

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Cognitive Graph for Multi-Hop Reading Comprehension at Scale

https://github.com/THUDM/InversePrompting

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Controllable Generation from Pre-trained Language Models via Inverse Prompting

https://github.com/THUDM/LongCite

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LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA

https://github.com/THUNLP-MT/ActiView

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ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models

https://github.com/THUNLP-MT/EscapeCraft

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How Do Multimodal Large Language Models Handle Complex Multimodal Reasoning? Placing Them in An Extensible Escape Game

https://github.com/TIGER-AI-Lab/LLM-AMT

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Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answering (Published in Findings of EMNLP 2024)

https://github.com/TJURLLAB-EAI/MFE-ETP

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MFE-ETP: A Comprehensive Evaluation Benchmark for Multi-modal Foundation Models on Embodied Task Planning

https://github.com/TRUMANCFY/MixGR

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$\texttt{MixGR}$: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity

https://github.com/TUDB-Labs/MixLoRA

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

https://github.com/TXH-mercury/VALOR

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VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset

https://github.com/Tagore-7/Small-Language-Models-for-the-Democratization-of-Financial-Literacy

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Exploring the Readiness of Prominent Small Language Models for the Democratization of Financial Literacy

https://github.com/TaoZhen1110/CUDRT_Benchmark

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Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT

https://github.com/TencentARC/ST-LLM

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ST-LLM: Large Language Models Are Effective Temporal Learners

https://github.com/TencentARC/ViT-Lens

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ViT-Lens: Initiating Omni-Modal Exploration through 3D Insights

https://github.com/TheAthleticCoder/iREL-at-SemEval-2024-Task-9

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iREL at SemEval-2024 Task 9: Improving Conventional Prompting Methods for Brain Teasers

https://github.com/Theo-Jaunet/VisQA

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VisQA: X-raying Vision and Language Reasoning in Transformers

https://github.com/TianduoWang/MsAT

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Learning Multi-Step Reasoning by Solving Arithmetic Tasks

https://github.com/Trusted-AI/AIX360

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Leveraging Latent Features for Local Explanations

https://github.com/Tzoulio/Large_Models_Dialogue_for_Active_Perception

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Large Models in Dialogue for Active Perception and Anomaly Detection

https://github.com/U-Alberta/QA-partial-marks

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Accurate and Nuanced Open-QA Evaluation Through Textual Entailment

https://github.com/UCSB-NLP-Chang/KVLink

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KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse

https://github.com/UKPLab/TWEAC-qa-agent-selection

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TWEAC: Transformer with Extendable QA Agent Classifiers

https://github.com/UKPLab/acl2024-DARA

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DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs

https://github.com/UKPLab/beir

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BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

https://github.com/UKPLab/coref-reasoning-in-qa

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Coreference Reasoning in Machine Reading Comprehension

https://github.com/UKPLab/emnlp2019-duplicate_question_detection

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Neural Duplicate Question Detection without Labeled Training Data

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