28 citations · 85 across the 15 of their papers we have counts for
3 papers · 1 filter
Understanding Forgetting in LLM Supervised Fine-Tuning and Preference Learning -- A Convex Optimization Perspective
Heshan Fernando, Han Shen, Parikshit Ram +4
The post-training of LLMs, which typically consists of the supervised fine-tuning (SFT) stage and the preference learning stage (RLHF or DPO), is crucial to effective and safe LLM…
On the Utility of Domain-Adjacent Fine-Tuned Model Ensembles for Few-shot Problems
Md Ibrahim Ibne Alam, Parikshit Ram, Soham Dan +2
Large Language Models (LLMs) have been observed to perform well on a wide range of downstream tasks when fine-tuned on domain-specific data. However, such data may not be readily a…
Enhancing In-context Learning via Linear Probe Calibration
Momin Abbas, Yi Zhou, Parikshit Ram +4
In-context learning (ICL) is a new paradigm for natural language processing that utilizes Generative Pre-trained Transformer (GPT)-like models. This approach uses prompts that incl…