Papers with Code 아카이브
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/nightdessert/WeCheck/blob/main/README.md
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning
https://github.com/ninglab/ecellm
https://github.com/ninodimontalcino/moralchoice
https://github.com/nju-websoft/FBPrompt
Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering
https://github.com/nkluge-correa/teenytinyllama
https://github.com/nl4opt/nl4opt-competition
Evaluating LLM Reasoning in the Operations Research Domain with ORQA
https://github.com/nlinc1905/dsilt-tsa
https://github.com/nlmatics/llmsherpa
https://github.com/nlp-research/bilm-tf
https://github.com/nlp-rise/climateeval
Towards Answering Climate Questionnaires from Unstructured Climate Reports
https://github.com/nlpaueb/deep-relevance-ranking
Deep Relevance Ranking Using Enhanced Document-Query Interactions
https://github.com/nlpodyssey/spago/blob/main/pkg/ml/nn/gmlp/model.go
https://github.com/nlpxucan/wizardlm
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
https://github.com/nlx-group/overlapy
https://github.com/nmfisher/bert-modified
https://github.com/nnaakkaaii/g2-MLP
https://github.com/noahtren/Graph-Attention-Networks-TensorFlow-2
https://github.com/noelkelias/multitok
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/noise-learning/selfmix
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/noiseQA/NoiseQA
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering
https://github.com/noiseQA/noiseQA-gen
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering
https://github.com/noriyukipy/tfchat
https://github.com/noriyukipy/tfdlg
https://github.com/nosaydomore/MobileBert_paddle
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
https://github.com/npow/ubuntu-corpus
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/nrc-cnrc/sockeye-multisource
https://github.com/nsfzyzz/generalization_metrics_for_nlp
https://github.com/nsourlos/pneumonia_detection
https://github.com/nthakur20/income
Unsupervised Dense Information Retrieval with Contrastive Learning
https://github.com/ntunlplab/traditional-chinese-alpaca
https://github.com/nuochenpku/orca
Open-Domain Question Answering Goes Conversational via Question Rewriting
https://github.com/nvidia/tensorrt-model-optimizer
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
https://github.com/nvidia/transformerengine
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/nvlabs/maskllm
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
https://github.com/nvshrao/Bidirectional-Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.