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Found 23 papers, 18 papers with code
Tamper-Resistant Safeguards for Open-Weight LLMs
Rapid advances in the capabilities of large language models (LLMs) have raised widespread concerns regarding their potential for malicious use.
The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs.
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
Automated red teaming holds substantial promise for uncovering and mitigating the risks associated with the malicious use of large language models (LLMs), yet the field lacks a standardized evaluation framework to rigorously assess new methods.
Representation Engineering: A Top-Down Approach to AI Transparency
In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience.
Ranked #3 on
Question Answering
on TruthfulQA
An Overview of Catastrophic AI Risks
Rapid advancements in artificial intelligence (AI) have sparked growing concerns among experts, policymakers, and world leaders regarding the potential for increasingly advanced AI systems to pose catastrophic risks.
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications such as healthcare and finance -- where mistakes can be costly.
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios
In experiments, we show how video models that are primarily trained to recognize actions and find contours of objects can be repurposed to understand human preferences and the emotional content of videos.
Forecasting Future World Events with Neural Networks
We test language models on our forecasting task and find that performance is far below a human expert baseline.
How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection
To meet these challenges, we present a new approach to model stealing defenses called gradient redirection.
X-Risk Analysis for AI Research
Artificial intelligence (AI) has the potential to greatly improve society, but as with any powerful technology, it comes with heightened risks and responsibilities.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures
In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy.
What Would Jiminy Cricket Do? Towards Agents That Behave Morally
When making everyday decisions, people are guided by their conscience, an internal sense of right and wrong.
Improving and Assessing Anomaly Detectors for Large-Scale Settings
We conduct extensive experiments in these more realistic settings for out-of-distribution detection and find that a surprisingly simple detector based on the maximum logit outperforms prior methods in all the large-scale multi-class, multi-label, and segmentation tasks, establishing a simple new baseline for future work.
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
How Multipurpose Are Language Models?
By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings.
Measuring Massive Multitask Language Understanding
By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings.
Scaling Out-of-Distribution Detection for Real-World Settings
We conduct extensive experiments in these more realistic settings for out-of-distribution detection and find that a surprisingly simple detector based on the maximum logit outperforms prior methods in all the large-scale multi-class, multi-label, and segmentation tasks, establishing a simple new baseline for future work.
Testing Robustness Against Unforeseen Adversaries
To narrow in on this discrepancy between research and reality we introduce ImageNet-UA, a framework for evaluating model robustness against a range of unforeseen adversaries, including eighteen new non-L_p attacks.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Self-supervision provides effective representations for downstream tasks without requiring labels.
Using Pre-Training Can Improve Model Robustness and Uncertainty
He et al. (2018) have called into question the utility of pre-training by showing that training from scratch can often yield similar performance to pre-training.
Deep Anomaly Detection with Outlier Exposure
We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.
Ranked #3 on
Out-of-Distribution Detection
on CIFAR-100
(using extra training data)
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
We utilize trusted data by proposing a loss correction technique that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers.
