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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/chiahsuan156/Spoken-SQuAD
공식 코드https://github.com/chojw/genb
공식 코드https://github.com/chrisc36/autobias
공식 코드Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles
https://github.com/chrisc36/debias
공식 코드Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
https://github.com/chriskuei/fedmatch
공식 코드FedMatch: Federated Learning Over Heterogeneous Question Answering Data
https://github.com/christopher-beckham/clevr-mrt
공식 코드Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests
https://github.com/chuanyang-Zheng/Progressive-Hint
공식 코드Progressive-Hint Prompting Improves Reasoning in Large Language Models
https://github.com/chuanyangjin/MMToM-QA
공식 코드https://github.com/chuhac/atm-rag
공식 코드ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator
https://github.com/chunmeifeng/vqa4cir
공식 코드VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering
https://github.com/chunyangx/overnight_more
공식 코드https://github.com/cic4k/wisebe
공식 코드https://github.com/cindermond/explicit-planning-for-reasoning
공식 코드Explicit Planning Helps Language Models in Logical Reasoning
https://github.com/cindermond/leap
공식 코드Explicit Planning Helps Language Models in Logical Reasoning
https://github.com/cindyxinyiwang/TrDec_pytorch
공식 코드https://github.com/cisco-open/DeepVision/tree/main/recallm
공식 코드RecallM: An Adaptable Memory Mechanism with Temporal Understanding for Large Language Models
https://github.com/cisnlp/mplm-sim
공식 코드mPLM-Sim: Better Cross-Lingual Similarity and Transfer in Multilingual Pretrained Language Models
https://github.com/ckosten/spider4sparql
공식 코드Spider4SPARQL: A Complex Benchmark for Evaluating Knowledge Graph Question Answering Systems
https://github.com/clarin-pl/chatgpt-evaluation-01-2023
공식 코드https://github.com/clause-bielefeld/illusionofcompetence-vqa-explanations
공식 코드https://github.com/climabench/climabench
공식 코드Towards Answering Climate Questionnaires from Unstructured Climate Reports
https://github.com/clinicalml/onboarding_human_ai
공식 코드Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
https://github.com/clinicalml/teaching-to-understand-ai
공식 코드https://github.com/clip-vil/CLIP-ViL
공식 코드https://github.com/clips/clicr
공식 코드CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension
https://github.com/clovaai/length-adaptive-transformer
공식 코드Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search
https://github.com/clovaai/minimal-rnr-qa
공식 코드Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering
https://github.com/clubiedong/kvcachequantization
공식 코드https://github.com/cluebbers/dpo-rlhf-paraphrase-types
공식 코드Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked Data
https://github.com/cmavro/gnn-rag
공식 코드GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
https://github.com/cmavro/rearev_kgqa
공식 코드ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs
https://github.com/cmavro/tempoqr
공식 코드https://github.com/cmriat/l0
공식 코드https://github.com/coastalcph/lex-glue
공식 코드LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
https://github.com/cocacola-lab/chatie
공식 코드ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.