https://github.com/opengvlab/llama-adapter
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/openmatch/ance-tele
https://github.com/opennlg/openba-v2
https://github.com/opennmt/ctranslate2
https://github.com/openpsi-project/realhf
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
https://github.com/openrlhf/openrlhf
Training language models to follow instructions with human feedback
https://github.com/openrobotlab/pointllm
https://github.com/opent03/LangevinDQN-torch
https://github.com/orhonovich/q-squared
https://github.com/orionbench/orionbench
InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts
https://github.com/osu-nlp-group/mind2web
https://github.com/overfitter/biobert_embedding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/overwindows/PALM
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
https://github.com/owainwest/uniprot_bert
https://github.com/oyar99/ir-ag-reg
RIRAG: Regulatory Information Retrieval and Answer Generation
https://github.com/pablonm3/cs224u
https://github.com/paolanu/BERT_epitope
https://github.com/paralleldrive/sudolang-llm-support
Prompting Is Programming: A Query Language for Large Language Models
https://github.com/parcorelab/comscribe
https://github.com/park-cheol/ASR-Conformer
https://github.com/patrickhart/jaxdl
https://github.com/pauldevos/python-notes
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/paulskeie/stadnamn
https://github.com/peiyance/revolve
https://github.com/peizeli96/MHys
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering
https://github.com/peizeli96/Object-attribute-matters-in-visual-question-answering
https://github.com/pengfeiliheu/mumc
PathVQA: 30000+ Questions for Medical Visual Question Answering
https://github.com/pengming617/bert_classification
https://github.com/pengshuyuan/Bert
https://github.com/pengsl-lab/mssl
https://github.com/pengsl-lab/mssl2drug
https://github.com/penny9287/sockeye
https://github.com/pennylaneai/generative-quantum-states
https://github.com/pfecht/bert-exploration
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/phbst/tinyrag
Learning to Filter Context for Retrieval-Augmented Generation
https://github.com/phellonchen/DMRM
Dual Attention Networks for Visual Reference Resolution in Visual Dialog
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.