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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/irenezihuili/cgprompt

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Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education

https://github.com/irenezihuili/graphusion

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Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective

https://github.com/iriscxy/fes

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Towards Improving Faithfulness in Abstractive Summarization

https://github.com/is2ai/kazqad

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KazQAD: Kazakh Open-Domain Question Answering Dataset

https://github.com/isaacwasserman/LongRAG

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Enhancing Retrieval in QA Systems with Derived Feature Association

https://github.com/isakzhang/answer-helpfulness-prediction

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Review-guided Helpful Answer Identification in E-commerce

https://github.com/ischlag/Fast-Weight-Memory-public

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Learning Associative Inference Using Fast Weight Memory

https://github.com/ischlag/TP-Transformer

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Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving

https://github.com/isfhub/maskcode

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Unmasking the Limits of Large Language Models: A Systematic Evaluation of Masked Text Processing Ability through MskQA and MskCal

https://github.com/ishan00/meta-learning-for-multi-task-multilingual

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Meta-Learning for Effective Multi-task and Multilingual Modelling

https://github.com/ist-daslab/sparsegpt

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SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

https://github.com/itaigat/perceptual-score

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Perceptual Score: What Data Modalities Does Your Model Perceive?

https://github.com/itaigat/removing-bias-in-multi-modal-classifiers

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Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies

https://github.com/itaishufaro/bitsandbandits

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On Bits and Bandits: Quantifying the Regret-Information Trade-off

https://github.com/itemzheng/kddaug

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Rethinking Data Augmentation for Robust Visual Question Answering

https://github.com/iu-aes-ai4code/codequestionanswering

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Leveraging Large Language Models in Code Question Answering: Baselines and Issues

https://github.com/ixa-ehu/antidote-casimedicos

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CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures

https://github.com/j-min/VL-T5

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Unifying Vision-and-Language Tasks via Text Generation

https://github.com/jackroos/VL-BERT

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VL-BERT: Pre-training of Generic Visual-Linguistic Representations

https://github.com/jacobandreas/nmn2

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Learning to Compose Neural Networks for Question Answering

https://github.com/jacobpfau/introspective-self-consistency

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Self-Consistency of Large Language Models under Ambiguity

https://github.com/jacobswan1/Video2Commonsense

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Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning

https://github.com/jacoxu/HMN4QA

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Hierarchical Memory Networks for Answer Selection on Unknown Words

https://github.com/jadecxliu/codeqa

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CodeQA: A Question Answering Dataset for Source Code Comprehension

https://github.com/jaehyeoklee-119/crest

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Self-Training Meets Consistency: Improving LLMs' Reasoning With Consistency-Driven Rationale Evaluation

https://github.com/jantrienes/ecir2019-qac

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Identifying Unclear Questions in Community Question Answering Websites

https://github.com/jarvisustc/doctoragent-rl

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DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical Dialogue

https://github.com/jasonforjoy/model-editing-hurt

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Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue

https://github.com/jasonyux/localrqa

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LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems

https://github.com/jasonzhang929/BVFT_empirical_experiments

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Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning

https://github.com/jayetri/DrugEHRQA-A-Question-Answering-Dataset-on-Structured-and-Unstructured-Electronic-Health-Records

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DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries

https://github.com/jayjhaveri1906/cse291_medlm

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MedLM: Exploring Language Models for Medical Question Answering Systems

https://github.com/jayleicn/ClipBERT

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Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling

https://github.com/jayleicn/TVQA-PLUS

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TVQA+: Spatio-Temporal Grounding for Video Question Answering

https://github.com/jayleicn/TVQAplus

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TVQA+: Spatio-Temporal Grounding for Video Question Answering

https://github.com/jayleicn/singularity

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Revealing Single Frame Bias for Video-and-Language Learning

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