15 research outputs found
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
Large language models (LLMs) can potentially democratize access to medical
knowledge. While many efforts have been made to harness and improve LLMs'
medical knowledge and reasoning capacities, the resulting models are either
closed-source (e.g., PaLM, GPT-4) or limited in scale (<= 13B parameters),
which restricts their abilities. In this work, we improve access to large-scale
medical LLMs by releasing MEDITRON: a suite of open-source LLMs with 7B and 70B
parameters adapted to the medical domain. MEDITRON builds on Llama-2 (through
our adaptation of Nvidia's Megatron-LM distributed trainer), and extends
pretraining on a comprehensively curated medical corpus, including selected
PubMed articles, abstracts, and internationally-recognized medical guidelines.
Evaluations using four major medical benchmarks show significant performance
gains over several state-of-the-art baselines before and after task-specific
finetuning. Overall, MEDITRON achieves a 6% absolute performance gain over the
best public baseline in its parameter class and 3% over the strongest baseline
we finetuned from Llama-2. Compared to closed-source LLMs, MEDITRON-70B
outperforms GPT-3.5 and Med-PaLM and is within 5% of GPT-4 and 10% of
Med-PaLM-2. We release our code for curating the medical pretraining corpus and
the MEDITRON model weights to drive open-source development of more capable
medical LLMs
Exact Preimages of Neural Network Aircraft Collision Avoidance Systems
A common pattern of progress in engineering has seen deep neural networks displacing human-designed logic. There are many advantages to this approach, divorcing decisionmaking from human oversight and intuition has costs as well. One is that deep neural networks can map similar inputs to very different outputs in a way that makes their application to safety-critical problem problematic. We present a method to check that the decisions of a deep neural network are as intended by constructing the exact preimage of its predictions. Preimages generalize verification in the sense that they can be used to verify a wide class of properties, and answer much richer questions besides. We examine the functioning of an aircraft collision avoidance system, and show how exact preimages reduce undue conservatism when examining dynamic safety. Our method iterates backwards through the layers of piecewise linear deep neural networks. Uniquely, we compute \emph{all} intermediate values that correspond to a prediction, propagating this calculation through layers using analytical formulae for layer preimages
Growth accounting, potential output, and the current recession
Total factor productivity - a measure of the efficiency with which labor and capital are used - has fallen during the current recession. But, after adjustment for lower utilization of labor and capital, such productivity has risen strongly over the past two years. These growth-accounting measures suggest that efficiency gains have continued during the recession, boding well for long-term economic growth.Productivity ; Labor productivity ; Capital investments
Challenges for Using Impact Regularizers to Avoid Negative Side Effects
Designing reward functions for reinforcement learning is difficult: besides specifying which behavior is rewarded for a task, the reward also has to discourage undesired outcomes. Misspecified reward functions can lead to unintended negative side effects, and overall unsafe behavior. To overcome this problem, recent work proposed to augment the specified reward function with an impact regularizer that discourages behavior that has a big impact on the environment. Although initial results with impact regularizers seem promising in mitigating some types of side effects, important challenges remain. In this paper, we examine the main current challenges of impact regularizers and relate them to fundamental design decisions. We discuss in detail which challenges recent approaches address and which remain unsolved. Finally, we explore promising directions to overcome the unsolved challenges in preventing negative side effects with impact regularizers
Challenges for Using Impact Regularizers to Avoid Negative Side Effects
Designing reward functions for reinforcement learning is difficult: besides specifying which behavior is rewarded for a task, the reward also has to discourage undesired outcomes. Misspecified reward functions can lead to unintended negative side effects, and overall unsafe behavior. To overcome this problem, recent work proposed to augment the specified reward function with an impact regularizer that discourages behavior that has a big impact on the environment. Although initial results with impact regularizers seem promising in mitigating some types of side effects, important challenges remain. In this paper, we examine the main current challenges of impact regularizers and relate them to fundamental design decisions. We discuss in detail which challenges recent approaches address and which remain unsolved. Finally, we explore promising directions to overcome the unsolved challenges in preventing negative side effects with impact regularizers
Flatten the Curve: Efficiently Training Low-Curvature Neural Networks
The highly non-linear nature of deep neural networks causes them to be
susceptible to adversarial examples and have unstable gradients which hinders
interpretability. However, existing methods to solve these issues, such as
adversarial training, are expensive and often sacrifice predictive accuracy.
In this work, we consider curvature, which is a mathematical quantity which
encodes the degree of non-linearity. Using this, we demonstrate low-curvature
neural networks (LCNNs) that obtain drastically lower curvature than standard
models while exhibiting similar predictive performance, which leads to improved
robustness and stable gradients, with only a marginally increased training
time. To achieve this, we minimize a data-independent upper bound on the
curvature of a neural network, which decomposes overall curvature in terms of
curvatures and slopes of its constituent layers. To efficiently minimize this
bound, we introduce two novel architectural components: first, a non-linearity
called centered-softplus that is a stable variant of the softplus
non-linearity, and second, a Lipschitz-constrained batch normalization layer.
Our experiments show that LCNNs have lower curvature, more stable gradients
and increased off-the-shelf adversarial robustness when compared to their
standard high-curvature counterparts, all without affecting predictive
performance. Our approach is easy to use and can be readily incorporated into
existing neural network models