Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis
Oscar Chew, Hsuan-Tien Lin, Kai-Wei Chang, and Kuan-Hao Huang, in EACL-Findings, 2024.
Abstract
Bib Entry
@inproceedings{chew2024understanding,
title = {Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis},
author = {Chew, Oscar and Lin, Hsuan-Tien and Chang, Kai-Wei and Huang, Kuan-Hao},
booktitle = {EACL-Findings},
year = {2024}
}
Related Publications
-
Control Large Language Models via Divide and Conquer, EMNLP, 2024
-
Re-ReST: Reflection-Reinforced Self-Training for Language Agents, EMNLP, 2024
-
Agent Lumos: Unified and Modular Training for Open-Source Language Agents, ACL, 2024
-
Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension, ICML, 2024
-
TrustLLM: Trustworthiness in Large Language Models, ICML, 2024
-
The steerability of large language models toward data-driven personas, NAACL, 2024
-
AI-Assisted Summarization of Radiologic Reports: Evaluating GPT3davinci, BARTcnn, LongT5booksum, LEDbooksum, LEDlegal, and LEDclinical, American Journal of Neuroradiology, 2024
-
Few-Shot Representation Learning for Out-Of-Vocabulary Words, ACL, 2019
-
Learning Word Embeddings for Low-resource Languages by PU Learning, NAACL, 2018
-
Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment, IJCAI, 2018
-
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context, ACL RepL4NLP Workshop, 2017