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Found 19 papers, 13 papers with code
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
Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark
And how do we measure these behaviors in general-purpose models such as GPT-4?
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.
Towards Robustness of Neural Networks
All of the datasets were created for testing robustness and measuring progress in robustness.
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 #8 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 #95 on
Math Word Problem Solving
on MATH
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.
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.
Towards Machine Ethics with Language Models
We show how to assess a language model’s knowledge of basic concepts of morality.
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.
Ranked #60 on
Multi-task Language Understanding
on MMLU
Aligning AI With Shared Human Values
We show how to assess a language model's knowledge of basic concepts of morality.
Ranked #1 on
Average
on hendrycks2020ethics
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
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.
DIODE: A Dense Indoor and Outdoor DEpth Dataset
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements.
Natural Adversarial Examples
We also curate an adversarial out-of-distribution detection dataset called ImageNet-O, which is the first out-of-distribution detection dataset created for ImageNet models.
Ranked #39 on
Domain Generalization
on ImageNet-A
