15 citations · 36 across the 11 of their papers we have counts for
7 papers · 1 filter
Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data
Younghun Lee, Sungchul Kim, Ryan A. Rossi +2
Large Language Models (LLMs) have been achieving competent performance on a wide range of downstream tasks, yet existing work shows that inference on structured data is challenging…
Hallucination Diversity-Aware Active Learning for Text Summarization
Yu Xia, Xu Liu, Tong Yu +5
Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i.e., texts that are factually incorrect or unsupported. Existing methods for alleviating hallu…
Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models
Younghun Lee, Sungchul Kim, Tong Yu +2
Large Language Models (LLMs) have been widely used as general-purpose AI agents showing comparable performance on many downstream tasks. However, existing work shows that it is cha…
Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow +6
Large language models (LLMs) have shown remarkable advances in language generation and understanding but are also prone to exhibiting harmful social biases. While recognition of th…
Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations
Zhihui Xie, Handong Zhao, Tong Yu +1
Large pretrained multilingual language models (ML-LMs) have shown remarkable capabilities of zero-shot cross-lingual transfer, without direct cross-lingual supervision. While these…
Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances
Zhendong Chu, Ruiyi Zhang, Tong Yu +4
To achieve state-of-the-art performance, one still needs to train NER models on large-scale, high-quality annotated data, an asset that is both costly and time-intensive to accumul…