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Found 15 papers, 8 papers with code
Specific versus General Principles for Constitutional AI
Constitutional AI offers an alternative, replacing human feedback with feedback from AI models conditioned only on a list of written principles.
Measuring Faithfulness in Chain-of-Thought Reasoning
Large language models (LLMs) perform better when they produce step-by-step, "Chain-of-Thought" (CoT) reasoning before answering a question, but it is unclear if the stated reasoning is a faithful explanation of the model's actual reasoning (i. e., its process for answering the question).
The Capacity for Moral Self-Correction in Large Language Models
We believe that at this level of scale, language models obtain two capabilities that they can use for moral self-correction: (1) they can follow instructions and (2) they can learn complex normative concepts of harm like stereotyping, bias, and discrimination.
Discovering Language Model Behaviors with Model-Written Evaluations
We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse.
Constitutional AI: Harmlessness from AI Feedback
In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences.
DeepChrome 2.0: Investigating and Improving Architectures, Visualizations, & Experiments
Results from cross-cell prediction experiments, where the model is trained and tested on datasets of varying sizes, cell-types, and correlations, suggest the relationship between histone modification signals and gene expression is independent of cell type.
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
We provide our own analysis of the data and find a variety of harmful outputs, which range from offensive language to more subtly harmful non-violent unethical outputs.
Language Models (Mostly) Know What They Know
We study whether language models can evaluate the validity of their own claims and predict which questions they will be able to answer correctly.
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
We apply preference modeling and reinforcement learning from human feedback (RLHF) to finetune language models to act as helpful and harmless assistants.
Pretraining & Reinforcement Learning: Sharpening the Axe Before Cutting the Tree
Pretraining is a common technique in deep learning for increasing performance and reducing training time, with promising experimental results in deep reinforcement learning (RL).
Measuring Coding Challenge Competence With APPS
Recent models such as GPT-Neo can pass approximately 20% of the test cases of introductory problems, so we find that machine learning models are now beginning to learn how to code.
Ranked #10 on
Code Generation
on APPS
Measuring Mathematical Problem Solving With the MATH Dataset
To facilitate future research and increase accuracy on MATH, we also contribute a large auxiliary pretraining dataset which helps teach models the fundamentals of mathematics.
Ranked #121 on
Math Word Problem Solving
on MATH
A Rigorous Evaluation of Real-World Distribution Shifts
Motivated by this, we introduce a new data augmentation method which advances the state-of-the-art and outperforms models pretrained with 1000x more labeled data.
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work.
Ranked #29 on
Domain Generalization
on ImageNet-R
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Self-supervision provides effective representations for downstream tasks without requiring labels.
