12 citations · 29 across the 52 of their papers we have counts for
5 papers · 1 filter
ReHoPER: Receding-Horizon Planning for Enhanced Reasoning
Saeed Ahmadnia, Cornelia Caragea
We propose ReHoPER, an inference-only, zero-shot method that improves large language models' reasoning by generating and answering intermediate questions along multiple paths befor…
Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition
Qi Zhang, Fangping Lan, Cornelia Caragea +2
In-context learning (ICL) with large language models (LLMs) has emerged as a powerful alternative to fine-tuning for Named Entity Recognition (NER), achieving strong performance wi…
CaliDist: Calibrating Large Language Models via Behavioral Robustness to Distraction
Mohammad Anas Jawad, Cornelia Caragea
Existing calibration methods for Large Language Models (LLMs) often overlook a critical dimension of trustworthiness: a model's behavioral robustness to irrelevant or misleading in…
LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification
Jacob Ativo, Bharaneeshwar Balasubramaniyam, Anh Tran +4
Semi-supervised learning approaches have been investigated as a means to enhance the analysis of social media data in disaster management contexts. In this work, we present the fir…
BLooP: Zero-Shot Abstractive Summarization using Large Language Models with Bigram Lookahead Promotion
Varun Iyer, Cornelia Caragea
Abstractive summarization requires models to generate summaries that convey information in the source document. While large language models can generate summaries without fine-tuni…