Tim G. J. Rudner

Fine-tuning off-the-shelf pre-trained neural networks has become the default starting point for a wide range of challenging prediction tasks—especially in computer vision and natural language processing, where pre-trained models trained on millions or even billions of data points are publicly available and can be fine-tuned with a moderate compute budget. However, while fine-tuned models have been shown to significantly improve predictive performance compared to models trained from scratch, they can exhibit poor calibration and fail to reliably identify challenging distribution shifts. In this paper, we improve uncertainty quantification in fine-tuned models by constructing a data-driven, uncertainty-aware fine-tuning prior that assigns high probability density to parameters that induce predictive functions with high uncertainty on input points that are meaningfully different from the data. We derive a tractable variational objective to perform approximate inference in models with data-driven, uncertainty-aware priors and evaluate models fine-tuned with such priors on different transfer learning tasks. We show that fine-tuning with uncertainty-aware priors significantly improves calibration, selective prediction, and semantic shift detection on computer vision and natural language classification tasks.

A critical component in the trustworthiness of LLMs is reliable uncertainty communication, yet LLMs often use assertive language when conveying false claims, leading to over-reliance and eroded trust. We present the first systematic study of faithful confidence calibration of LLMs, benchmarking models’ ability to use linguistic expressions of uncertainty that faithfully reflect their intrinsic uncertainty, across a comprehensive array of models, datasets, and prompting strategies. Our results demonstrate that LLMs largely fail at this task, and that existing interventions are insufficient: standard prompt approaches provide only marginal gains, and existing, factuality-based calibration techniques can even harm faithful calibration. To address this critical gap, we introduce MetaFaith, a novel prompt-based calibration approach inspired by human metacognition. We show that MetaFaith robustly improves faithful calibration across diverse models and task domains, enabling up to 61% improvement in faithfulness and achieving an 83% win rate over original generations as judged by humans.

Machine learning models often perform poorly under subpopulation shifts in the data distribution. Developing methods that allow machine learning models to better generalize to such shifts is crucial for safe deployment in real-world settings. In this paper, we develop a family of group-aware prior (GAP) distributions over neural network parameters that explicitly favor models that generalize well under subpopulation shifts. We design a simple group-aware prior that only requires access to a small set of data with group information and demonstrate that training with this prior yields state-of-the-art performance—even when only retraining the final layer of a previously trained non-robust model. Group aware-priors are conceptually simple, complementary to existing approaches, such as attribute pseudo labeling and data reweighting, and open up promising new avenues for harnessing Bayesian inference to enable robustness to subpopulation shifts.

Modern language models can contain billions of parameters, raising the question of whether they can generalize beyond the training data or simply regurgitate their training corpora. We provide the first non-vacuous generalization bounds for pretrained large language models (LLMs), indicating that language models are capable of discovering regularities that generalize to unseen data. In particular, we derive a compression bound that is valid for the unbounded log-likelihood loss, and we extend the bound to handle subsampling, accelerating bound computation on massive datasets. To achieve the extreme level of compression required for non-vacuous generalization bounds, we devise SubLoRA, a low-dimensional non-linear parameterization. Using this approach, we find that larger models have better generalization bounds and are more compressible than smaller models.

We investigate the mechanistic sources of uncertainty in large language models (LLMs), an area with important implications for language model reliability and trustworthiness. To do so, we conduct a series of experiments designed to identify whether the factuality of generated responses and a model’s uncertainty originate in separate or shared circuits in the model architecture. We approach this question by adapting the well-established mechanistic interpretability techniques of causal tracing and two styles of zero-ablation to study the effect of different circuits on LLM generations. Our experiments on eight different models and five datasets, representing tasks predominantly requiring factual recall, provide strong evidence that a model’s uncertainty is produced in the same parts of the network that are responsible for the factuality of generated responses.

Vision- and language-guided embodied AI requires a fine-grained understanding of the physical world through language and visual inputs. Such capabilities are difficult to learn solely from task-specific data, which has led to the emergence of pre-trained vision-language models as a tool for transferring representations learned from internet-scale data to downstream tasks and new domains. However, commonly used contrastively trained representations such as in CLIP have been shown to fail at enabling embodied agents to gain sufficiently fine-grained scene understanding—a capability vital for control. To address this shortcoming, we consider representations from pre-trained text-to-image diffusion models, which are explicitly optimized to generate image from text prompts and as such, contain text-conditioned representations that reflect highly fine-grained visuo-spatial information. Using pre-trained text-to-image diffusion models, we construct \em \ouralgolong which allow learning downstream control policies that generalize to complex, open-ended environments. We show that policies learned using \ouralgolong are competitive with state-of-the-art approaches on a broad range of simulated control tasks and exhibit high success rates on difficult control tasks that require generalization to unseen objects at test time. Most notably, we show that \ouralgolong enable learning policies that exhibit state-of-the-art performance on OVMM, a difficult open-vocabulary navigation benchmark.

Generative models have the potential to accelerate key steps in the discovery of novel molecular therapeutics and materials. Diffusion models have recently emerged as a powerful approach, excelling at unconditional sample generation and, with data-driven guidance, conditional generation within their training distribution. Reliably sampling from optimal regions beyond the training data, however, remains an open challenge—with current methods predominantly focusing on modifying the diffusion process itself. Here, we explore a different approach and present a simple plug-and-play regularization framework that leverages unlabeled data and smoothness constraints to improve the out-of-distribution generalization of guided diffusion models. Our method is probabilistically motivated and leads to substantial performance gains across various settings, including continuous, discrete, and graph-structured diffusion processes. We demonstrate significant improvements in performance for applications in chemistry, materials science, and protein design.

Parameter-space regularization in neural network optimization is a fundamental tool for improving generalization. However, standard parameter-space regularization methods make it challenging to encode explicit preferences about desired predictive functions into neural network training. In this work, we approach regularization in neural networks from a probabilistic perspective and show that by viewing parameter-space regularization as specifying an empirical prior distribution over the model parameters, we can derive a probabilistically well-motivated regularization technique that allows explicitly encoding information about desired predictive functions into neural network training. This method—which we refer to as function-space empirical Bayes (FS-EB)—includes both parameter- and function-space regularization, is mathematically simple, easy to implement, and incurs only minimal computational overhead compared to standard regularization techniques. We evaluate the utility of this regularization technique empirically and demonstrate that the proposed method leads to near-perfect semantic shift detection, highly-calibrated predictive uncertainty estimates, successful task adaption from pre-trained models, and improved generalization under covariate shift