Papers with Code 아카이브
Question Answering
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- 평가표
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- 보존된 평가 기록
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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/chandu7077/mybert
https://github.com/chang-github-00/llm-predictive-decoding
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
https://github.com/chaoyi-wu/finetune_llama
https://github.com/charles9n/bert-sklearn
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chartgalaxy/chartgalaxy
InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts
https://github.com/checkstep/mole-stance
https://github.com/chen-gx/toolevo
ToolQA: A Dataset for LLM Question Answering with External Tools
https://github.com/chen-xiong-yi/OwnBERT
https://github.com/cheng-Ye/daguan-competition-master
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/cheng18/bilm-tf
https://github.com/chengchingwen/transformers.jl
https://github.com/chenguolin/DiffSplat
https://github.com/chengyui/nuwats
Position: What Can Large Language Models Tell Us about Time Series Analysis
https://github.com/cheongwoong/impact_of_cooccurrence
https://github.com/chesterdu/contrastive_summary
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/chia-hsuan-lee/spoken-squad
https://github.com/chiayewken/bert-qa
https://github.com/chirag26495/DAN_VQA
https://github.com/chiral-carbon/kg-for-science
Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
https://github.com/chrisc36/bottom-up-attention-vqa
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
https://github.com/chrisleunglokhin/Capstone-BERT
https://github.com/chrisociepa/allamo
https://github.com/christianspybrook/article_recommender
https://github.com/chujiezheng/ChID-Dataset
https://github.com/chun5212021202/ACM-Net
https://github.com/chunghyunhee/twitter_disaster_NLP
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/cipnlu/InformationExtraction
https://github.com/circlePi/BERT_Chinese_Text_Class_By_pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/circlemind-ai/fast-graphrag
HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
https://github.com/cjlovering/Towards-Interpretable-Reinforcement-Learning-Using-Attention-Augmented-Agents-Replication
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
https://github.com/ckinateder/pytsetlinmachineparallel
A Relational Tsetlin Machine with Applications to Natural Language Understanding
https://github.com/clarenceguan/rc-cnn-daily-pytorch
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
https://github.com/clarenceguan/rc-cnn-dailymail-pytorch
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
https://github.com/cle-ros/RoutingNetworks
Routing Networks and the Challenges of Modular and Compositional Computation
https://github.com/clear-nus/llm-human-model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.