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Found 13 papers, 7 papers with code
Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation
Radiology reports are an instrumental part of modern medicine, informing key clinical decisions such as diagnosis and treatment.
Improving alignment of dialogue agents via targeted human judgements
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines.
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models
Large language models produce human-like text that drive a growing number of applications.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
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Challenges in Detoxifying Language Models
Large language models (LM) generate remarkably fluent text and can be efficiently adapted across NLP tasks.
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications
In this direction, we first introduce a general formulation of probabilistic specifications for neural networks, which captures both probabilistic networks (e. g., Bayesian neural networks, MC-Dropout networks) and uncertain inputs (distributions over inputs arising from sensor noise or other perturbations).
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
In this work, we propose a first-order dual SDP algorithm that (1) requires memory only linear in the total number of network activations, (2) only requires a fixed number of forward/backward passes through the network per iteration.
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification
Many real-world physical control systems are required to satisfy constraints upon deployment.
Plug-and-Play Conversational Models
These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model.
Counter-example Guided Learning of Bounds on Environment Behavior
There is a growing interest in building autonomous systems that interact with complex environments.
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities.
Scalable Neural Learning for Verifiable Consistency with Temporal Specifications
Formal verification of machine learning models has attracted attention recently, and significant progress has been made on proving simple properties like robustness to small perturbations of the input features.
Detecting Adversarial Examples via Neural Fingerprinting
Deep neural networks are vulnerable to adversarial examples, which dramatically alter model output using small input changes.
