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Found 10 papers, 7 papers with code
Feedback Loops With Language Models Drive In-Context Reward Hacking
Language models influence the external world: they query APIs that read and write to web pages, generate content that shapes human behavior, and run system commands as autonomous agents.
Orca 2: Teaching Small Language Models How to Reason
Research on training small LMs has often relied on imitation learning to replicate the output of more capable models.
Ranked #1 on
Crass AI
on BIG-bench
Teaching Language Models to Hallucinate Less with Synthetic Tasks
We also find that optimizing the system message rather than the model weights can be critical; fine-tuning the entire model on the synthetic task can counterintuitively increase hallucination.
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text.
Mass-Producing Failures of Multimodal Systems with Language Models
Because CLIP is the backbone for most state-of-the-art multimodal systems, these inputs produce failures in Midjourney 5. 1, DALL-E, VideoFusion, and others.
Automatically Auditing Large Language Models via Discrete Optimization
Auditing large language models for unexpected behaviors is critical to preempt catastrophic deployments, yet remains challenging.
Capturing Failures of Large Language Models via Human Cognitive Biases
Large language models generate complex, open-ended outputs: instead of outputting a class label they write summaries, generate dialogue, or produce working code.
Selective Classification Can Magnify Disparities Across Groups
In this paper, we find that while selective classification can improve average accuracies, it can simultaneously magnify existing accuracy disparities between various groups within a population, especially in the presence of spurious correlations.
Robust Encodings: A Framework for Combating Adversarial Typos
We instantiate RobEn to defend against a large family of adversarial typos.
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries.
Ranked #1 on
Multi-Label Classification
on MRNet
