POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference Optimization

Balancing safety and usefulness in large language models has become a critical challenge in recent years. Models often exhibit unsafe behavior or adopt an overly cautious approach, leading to frequent overrefusal of benign prompts, which reduces their usefulness. Addressing these issues requires methods that maintain safety while avoiding overrefusal. In this work, we examine how the overgeneration of training data using advanced teacher models (e.g., GPT-4o), including responses to both general-purpose and toxic prompts, influences the safety and overrefusal balance of instruction-following language models. Additionally, we present POROver, a strategy to use preference optimization methods in order to reduce overrefusal, via employing a superior teacher model's completions. Our results show that overgenerating completions for general-purpose prompts significantly improves the balance between safety and usefulness. Specifically, the F1 score calculated between safety and usefulness increases from 70.8% to 88.3%. Moreover, overgeneration for toxic prompts substantially reduces overrefusal, decreasing it from 94.4% to 45.2%. Furthermore, preference optimization algorithms, when applied with carefully curated preference data, can effectively reduce a model's overrefusal from 45.2% to 15.0% while maintaining comparable safety levels. Our code and data are available at https://github.com/batuhankmkaraman/POROver.

Balancing Interpretability and Predictive Accuracy for Unsupervised Tensor Mining

The PARAFAC tensor decomposition has enjoyed an increasing success in exploratory multi-aspect data mining scenarios. A major challenge remains the estimation of the number of latent factors (i.e., the rank) of the decomposition, which yields high-quality, interpretable results. Previously, we have proposed an automated tensor mining method which leverages a well-known quality heuristic from the field of Chemometrics, the Core Consistency Diagnostic (CORCONDIA), in order to automatically determine the rank for the PARAFAC decomposition. In this work we set out to explore the trade-off between 1) the interpretability/quality of the results (as expressed by CORCONDIA), and 2) the predictive accuracy of the results, in order to further improve the rank estimation quality. Our preliminary results indicate that striking a good balance in that trade-off benefits rank estimation.