5 papers
Efficient semantic uncertainty quantification in language models via diversity-steered sampling
Ji Won Park, Kyunghyun Cho
Accurately estimating semantic aleatoric and epistemic uncertainties in large language models (LLMs) is particularly challenging in free-form question answering (QA), where obtaini…
Semiparametric conformal prediction
Ji Won Park, Robert Tibshirani, Kyunghyun Cho
Many risk-sensitive applications require well-calibrated prediction sets over multiple, potentially correlated target variables, for which the prediction algorithm may report corre…
Supervised Contrastive Block Disentanglement
Taro Makino, Ji Won Park, Natasa Tagasovska +11
Real-world datasets often combine data collected under different experimental conditions. This yields larger datasets, but also introduces spurious correlations that make it diffic…
Concept Bottleneck Language Models For protein design
Aya Abdelsalam Ismail, Tuomas Oikarinen, Amy Wang +8
We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our arc…
Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing
Andreas Loukas, Karolis Martinkus, Ed Wagstaff +1
As training datasets grow larger, we aspire to develop models that generalize well to any diverse test distribution, even if the latter deviates significantly from the training dat…