4 papers · 1 filter
BEFT: Bias-Efficient Fine-Tuning of Language Models in Low-Data Regimes
Baichuan Huang, Ananth Balashankar, Amir Aminifar
Fine-tuning the bias terms of large language models (LLMs) has the potential to achieve unprecedented parameter efficiency while maintaining competitive performance, particularly i…
Robust LLM Performance Certification via Constrained Maximum Likelihood Estimation
Minghe Shen, Ananth Balashankar, Adam Fisch +2
The ability to rigorously estimate the failure rates of large language models (LLMs) is a prerequisite for their safe deployment. Currently, however, practitioners often face a tra…
Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual Alignment
Zhaofeng Wu, Ananth Balashankar, Yoon Kim +2
Aligning language models (LMs) based on human-annotated preference data is a crucial step in obtaining practical and performant LM-based systems. However, multilingual human prefer…
Automated Adversarial Discovery for Safety Classifiers
Yash Kumar Lal, Preethi Lahoti, Aradhana Sinha +2
Safety classifiers are critical in mitigating toxicity on online forums such as social media and in chatbots. Still, they continue to be vulnerable to emergent, and often innumerab…