Search Results for author:
Found 9 papers, 5 papers with code
Improving Code Generation by Training with Natural Language Feedback
The potential for pre-trained large language models (LLMs) to use natural language feedback at inference time has been an exciting recent development.
Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure
We demonstrate a situation in which Large Language Models, trained to be helpful, harmless, and honest, can display misaligned behavior and strategically deceive their users about this behavior without being instructed to do so.
Training Language Models with Language Feedback at Scale
Third, finetuning the language model to maximize the likelihood of the chosen refinement given the input.
Few-shot Adaptation Works with UnpredicTable Data
Finetuning on the resulting dataset leads to improved FSL performance on Natural Language Processing (NLP) tasks, but not proportionally to dataset scale.
Instance-wise algorithm configuration with graph neural networks
We pose this task as a supervised learning problem: First, we compile a large dataset of the solver performance for various configurations and all provided MILP instances.
Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema
Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema.
Training Language Models with Language Feedback
We learn from language feedback on model outputs using a three-step learning algorithm.
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals.
Black-Box Access is Insufficient for Rigorous AI Audits
The effectiveness of an audit, however, depends on the degree of system access granted to auditors.
