Search Results for author:
Found 10 papers, 7 papers with code
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision
Widely used alignment techniques, such as reinforcement learning from human feedback (RLHF), rely on the ability of humans to supervise model behavior - for example, to evaluate whether a model faithfully followed instructions or generated safe outputs.
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
Long-Context Understanding
on Ada-LEval (BestAnswer)
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
Many methods for PDE parameter inference involve a large number of evaluations for numerical solutions to PDE through algorithms such as the finite element method, which can be time-consuming, especially for nonlinear PDEs.
Self-critiquing models for assisting human evaluators
On a topic-based summarization task, critiques written by our models help humans find flaws in summaries that they would have otherwise missed.
Training language models to follow instructions with human feedback
In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback.
WebGPT: Browser-assisted question-answering with human feedback
This model's answers are preferred by humans 56% of the time to those of our human demonstrators, and 69% of the time to the highest-voted answer from Reddit.
Recursively Summarizing Books with Human Feedback
Our human labelers are able to supervise and evaluate the models quickly, despite not having read the entire books themselves.
Learning to summarize from human feedback
We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning.
Generative Pretraining from Pixels
Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images.
Ranked #15 on
Image Classification
on STL-10
(using extra training data)
Representation Learning
Self-Supervised Image Classification
Release Strategies and the Social Impacts of Language Models
Large language models have a range of beneficial uses: they can assist in prose, poetry, and programming; analyze dataset biases; and more.
