Search Results for author:
Found 7 papers, 6 papers with code
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Ranked #1 on
Legal Reasoning
on LegalBench (Rule-recall)
Text and Code Embeddings by Contrastive Pre-Training
Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20. 8% relative improvement over prior best work on code search.
Ranked #1 on
Passage Ranking
on MS MARCO
Evaluating Large Language Models Trained on Code
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities.
Ranked #1 on
Multi-task Language Understanding
on BBH-alg
Learning Transferable Visual Models From Natural Language Supervision
State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
Ranked #1 on
Zero-Shot Learning
on COCO-MLT
(using extra training data)
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Ranked #1 on
Question Answering
on CoQA
(Overall metric)
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development.
Computers and Society
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
We formalize human intervention for RL and show how to reduce the human labor required by training a supervised learner to imitate the human's intervention decisions.
