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Found 5 papers, 3 papers with code
Steering Without Side Effects: Improving Post-Deployment Control of Language Models
To demonstrate the generality and transferability of our method beyond jailbreaks, we show that our KTS model can be steered to reduce bias towards user-suggested answers on TruthfulQA.
Inverse Scaling: When Bigger Isn't Better
Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e. g., due to flaws in the training objective and data.
Normative Disagreement as a Challenge for Cooperative AI
However, the mixed-motive environments typically studied have a single cooperative outcome on which all agents can agree.
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Test-time data augmentation$-$averaging the predictions of a machine learning model across multiple augmented samples of data$-$is a widely used technique that improves the predictive performance.
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand.
