Jingyao Li

MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks

2023 · arXiv (Cornell University)

Large Language Models (LLMs) have showcased impressive capabilities in handling straightforward programming tasks. However, their performance tends to falter when confronted with more challenging programming problems. We observe that conventional models often generate solutions as …

BAL: Balancing Diversity and Novelty for Active Learning

2023 · arXiv (Cornell University)

The objective of Active Learning is to strategically label a subset of the dataset to maximize performance within a predetermined labeling budget. In this study, we harness features acquired through self-supervised learning. We introduce a …

Logits-Based Finetuning

2025

In recent years, developing compact and efficient large language models (LLMs) has emerged as a thriving area of research.Traditional Supervised Fine-Tuning (SFT), which relies on singular ground truth labels, often fails to capture token-level dependencies …