Selected Publications
Gemma 2: Improving Open Language Models at a Practical Size
We introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameter.
Beyond task performance: evaluating and reducing the flaws of large multimodal models with in-context-learning
We investigate large multimodal models and their limitations such as hallucinations and lack of explainability. We then show that multimodal in-context learning can reduce some of these flaws.
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
We introduce rewarded soup, a new strategy to trade-off between multiple rewards when fine-tuning foundation models with RLHF; we first learn one network for each reward, and then linearly interpolate their weights.
Diverse Weight Averaging for Out-of-Distribution Generalization
To improve out-of-distribution generalization on DomainBed, we average diverse weights obtained from different training runs; this strategy is motivated by an extension of the bias-variance theory to weight averaging.
