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Found 8 papers, 5 papers with code
Data Unlearning in Diffusion Models
Existing concept unlearning techniques require an anchor prompt/class/distribution to guide unlearning, which is not available in the data unlearning setting.
Cannot or Should Not? Automatic Analysis of Refusal Composition in IFT/RLHF Datasets and Refusal Behavior of Black-Box LLMs
Refusals - instances where large language models (LLMs) decline or fail to fully execute user instructions - are crucial for both AI safety and AI capabilities and the reduction of hallucinations in particular.
Simple linear attention language models balance the recall-throughput tradeoff
In this work, we explore whether we can improve language model efficiency (e. g. by reducing memory consumption) without compromising on recall.
Black-Box Access is Insufficient for Rigorous AI Audits
External audits of AI systems are increasingly recognized as a key mechanism for AI governance.
SuperHF: Supervised Iterative Learning from Human Feedback
Here, we focus on two prevalent methods used to align these models, Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF).
PIGEON: Predicting Image Geolocations
We train two models for evaluations on street-level data and general-purpose image geolocalization; the first model, PIGEON, is trained on data from the game of Geoguessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally.
Ranked #1 on
Photo geolocation estimation
on YFCC4k
Sumformer: Universal Approximation for Efficient Transformers
Natural language processing (NLP) made an impressive jump with the introduction of Transformers.
Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization
Image geolocalization is the challenging task of predicting the geographic coordinates of origin for a given photo.
Ranked #1 on
Photo geolocation estimation
on Im2GPS
(Training images metric)
