activity
20212024
most citedTraining language models to follow instructions with human feedback

4.3k citations · 4.4k across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2024★ 44 cited

OpenAI o1 System Card

OpenAI, :, Aaron Jaech +261

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the…

cs.CV2023

Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning

Chong Zhang, Mingyu Jin, Qinkai Yu +3

Generalized zero-shot learning (GZSL) aims to recognize samples from both seen and unseen classes using only seen class samples for training. However, GZSL methods are prone to bia…

cs.CV2023★ 2 cited

A Simple and Effective Baseline for Attentional Generative Adversarial Networks

Mingyu Jin, Chong Zhang, Qinkai Yu +3

Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task. In recent years, AttnG…

cs.CL2022★ 16 cited

A Holistic Approach to Undesired Content Detection in the Real World

Todor Markov, Chong Zhang, Sandhini Agarwal +5

We present a holistic approach to building a robust and useful natural language classification system for real-world content moderation. The success of such a system relies on a ch…

cs.CL2022★ 4.3k cited

Training language models to follow instructions with human feedback

Long Ouyang, Jeff Wu, Xu Jiang +17

Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic,…

cs.CL2021★ 9 cited

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing

Tao Gui, Xiao Wang, Qi Zhang +31

Various robustness evaluation methodologies from different perspectives have been proposed for different natural language processing (NLP) tasks. These methods have often focused o…