4 papers
REALM: Reliable Expertise-Aware Language Model Fine-Tuning from Noisy Annotations
Sajjad Ghiasvand, Mark Beliaev, Mahnoosh Alizadeh +1
Supervised fine-tuning of large language models relies on human-annotated data, yet annotation pipelines routinely involve multiple crowdworkers of heterogeneous expertise. Standar…
Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering
Mark Beliaev, Victor Yang, Madhura Raju +2
In this study, we tackle industry challenges in video content classification by exploring and optimizing GPT-based models for zero-shot classification across seven critical categor…
Inverse Reinforcement Learning by Estimating Expertise of Demonstrators
Mark Beliaev, Ramtin Pedarsani
In Imitation Learning (IL), utilizing suboptimal and heterogeneous demonstrations presents a substantial challenge due to the varied nature of real-world data. However, standard IL…
Pricing for Multi-modal Pickup and Delivery Problems with Heterogeneous Users
Mark Beliaev, Negar Mehr, Ramtin Pedarsani
In this paper, we study the pickup and delivery problem with multiple transportation modalities, and address the challenge of efficiently allocating transportation resources while…