Papers with Code 아카이브
Multi-task Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 5
- 보존된 평가 기록
- 79
The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. https://arxiv.org/pdf/2009.03300.pdf
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Scaling Instruction-Finetuned Language Models
GLM-130B: An Open Bilingual Pre-trained Model
Atlas: Few-shot Learning with Retrieval Augmented Language Models
Solving Quantitative Reasoning Problems with Language Models
UL2: Unifying Language Learning Paradigms
GPT-NeoX-20B: An Open-Source Autoregressive Language Model
PaLM: Scaling Language Modeling with Pathways
Training Compute-Optimal Large Language Models
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Merging Models with Fisher-Weighted Averaging
Evaluating Large Language Models Trained on Code
Measuring Massive Multitask Language Understanding
Language Models are Few-Shot Learners
UnifiedQA: Crossing Format Boundaries With a Single QA System
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
RoBERTa: A Robustly Optimized BERT Pretraining Approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.