4 papers
Expert-Grounded Automatic Prompt Engineering for Extracting Lattice Constants of High-Entropy Alloys from Scientific Publications using Large Language Models
Shunshun Liu, Talon R. Booth, Yangfeng Ji +2
Large language models (LLMs) have shown promise for scientific data extraction from publications, but rely on manual prompt refinement. We present an expert-grounded automatic prom…
A Comparative Study of Learning Paradigms in Large Language Models via Intrinsic Dimension
Saahith Janapati, Yangfeng Ji
The performance of Large Language Models (LLMs) on natural language tasks can be improved through both supervised fine-tuning (SFT) and in-context learning (ICL), which operate via…
Monte Carlo Sampling for Analyzing In-Context Examples
Stephanie Schoch, Yangfeng Ji
Prior works have shown that in-context learning is brittle to presentation factors such as the order, number, and choice of selected examples. However, ablation-based guidance on s…
In-Context Learning (and Unlearning) of Length Biases
Stephanie Schoch, Yangfeng Ji
Large language models have demonstrated strong capabilities to learn in-context, where exemplar input-output pairings are appended to the prompt for demonstration. However, existin…