1 citations · 1 across the 6 of their papers we have counts for
6 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…
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…
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…
Improve Temporal Awareness of LLMs for Sequential Recommendation
Zhendong Chu, Zichao Wang, Ruiyi Zhang +3
Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. However, it is empirically found that LLMs fall shor…
Blending Reward Functions via Few Expert Demonstrations for Faithful and Accurate Knowledge-Grounded Dialogue Generation
Wanyu Du, Yangfeng Ji
The development of trustworthy conversational information-seeking systems relies on dialogue models that can generate faithful and accurate responses based on relevant knowledge te…