Papers with Code 아카이브
General Knowledge
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 1
- 보존된 평가 기록
- 2
This task aims to evaluate the ability of a model to answer general-knowledge questions.
Source: BIG-bench
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/F-Almeman/GEAR_RD
https://github.com/FenQQQ/Fusion-in-decoder
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
https://github.com/FrancoisMasson1990/Siamese_Network
https://github.com/HLTCHKUST/adapterbot
The Adapter-Bot: All-In-One Controllable Conversational Model
https://github.com/Heidelberg-NLP/discourse-aware-semantic-self-attention
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/IBM/ColBERT-practical
https://github.com/JngwenYe/LIRF
https://github.com/JunnYu/x-transformers-paddle
https://github.com/LuminosoInsight/conceptnet-vector-ensemble
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
https://github.com/MindSpore-scientific/code-7/tree/main/DPT
https://github.com/NathanDuran/Sentence-Encoding-for-DA-Classification
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
https://github.com/PaddlePaddle/PaddleDetection
Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling
https://github.com/Phrase-in-Context/eval
https://github.com/ROpdam/recommender_comparison
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
https://github.com/RooKichenn/CEFPN
https://github.com/SatyamSoni23/Smart-Question-Answering-System-on-Document
Efficient and Robust Question Answering from Minimal Context over Documents
https://github.com/StatNLP/joeynmt
https://github.com/ZIZUN/MAFiD
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
https://github.com/a-rios/ats-models
https://github.com/ais-bonn/nimbro_api
A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics
https://github.com/aivolcano/BERT_MRC_CLS
https://github.com/akamath11/Multiple-choice-comprehension
https://github.com/allenai/dolma
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/allenai/language_fragments
https://github.com/allenai/ruletaker
https://github.com/altomator/Introduction_to_Deep_Learning-2-Face_Detection
https://github.com/amazon-science/efficient-longdoc-classification
https://github.com/amoramine/Pegasus_Longformer_summarization
https://github.com/amoramine/Pegasus_with_Longformer_summarization
https://github.com/avi-jit/SWOW-eval
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
https://github.com/awslabs/diagnostic-robustness-text-to-sql
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
https://github.com/badripatro/MDN-VQG
Multimodal Differential Network for Visual Question Generation
https://github.com/benjaminbeilharz/hierarchical-reinforcement-learning
https://github.com/bramiozo/PubScience
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.