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Question Answering
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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/novelqa/novelqa.github.io
공식 코드NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens
https://github.com/noviscl/answerequiv
공식 코드What's in a Name? Answer Equivalence For Open-Domain Question Answering
https://github.com/noviscl/calibrateqa
공식 코드https://github.com/npoe/covid-qa
공식 코드https://github.com/npow/ubottu
공식 코드The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/nsmq-ai/nsmqai
공식 코드Towards an AI to Win Ghana's National Science and Maths Quiz
https://github.com/ntaix/chinese-python-qa-dataset
공식 코드QACP: An Annotated Question Answering Dataset for Assisting Chinese Python Programming Learners
https://github.com/nttmdlab-nlp/VisualMRC
공식 코드https://github.com/nttmdlab-nlp/instructdoc
공식 코드https://github.com/nttmdlab-nlp/slidevqa
공식 코드SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images
https://github.com/ntunlp/chatgpt_eval
공식 코드A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets
https://github.com/ntunlp/llmsanitize
공식 코드How Much are Large Language Models Contaminated? A Comprehensive Survey and the LLMSanitize Library
https://github.com/ntunlp/middlesum
공식 코드On Context Utilization in Summarization with Large Language Models
https://github.com/nurakib/event-question-answering
공식 코드Asking and Answering Questions to Extract Event-Argument Structures
https://github.com/nusnlp/amanda
공식 코드A Question-Focused Multi-Factor Attention Network for Question Answering
https://github.com/nusnlp/complex-tr
공식 코드https://github.com/nusnlp/contextemb-wsd
공식 코드Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations
https://github.com/nusnlp/diffqre
공식 코드On the Robustness of Question Rewriting Systems to Questions of Varying Hardness
https://github.com/nv-tlabs/padl
공식 코드https://github.com/nvlabs/omnidrive
공식 코드OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
https://github.com/nvlabs/vila
공식 코드https://github.com/nweir127/microtheories
공식 코드From Models to Microtheories: Distilling a Model's Topical Knowledge for Grounded Question Answering
https://github.com/nyu-dl/SearchQA
공식 코드SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
https://github.com/nyu-dl/WebNav
공식 코드https://github.com/nyu-dl/dl4ir-doc2query
공식 코드https://github.com/nyu-dl/dl4mt-seqgen
공식 코드A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models
https://github.com/nyu-mll/bbq
공식 코드https://github.com/nyu-mll/crowdsourcing-protocol-comparison
공식 코드https://github.com/nyu-mll/quality
공식 코드https://github.com/nyuad-cai/medarabiq
공식 코드MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks
https://github.com/nyuolab/MedMobile
공식 코드MedMobile: A mobile-sized language model with expert-level clinical capabilities
https://github.com/nyuolab/medg-krp
공식 코드https://github.com/nzjin/odtqa
공식 코드Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval
https://github.com/oacore/core-gpt-evaluation
공식 코드https://github.com/oaklight/mango
공식 코드MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.