Papers by Ruijun Jiang

2 papers
Exploring Multilingual Pre-trained Language Model for Aspect-based Sentiment Analysis (2026.findings-acl)

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Challenge: Aspect-based sentiment analysis studies have focused on English datasets, but labeled data is scarce.
Approach: They propose a multilingual pre-trained language model that leverages bilingual pre-training to leverage aspects-based sentiment analysis.
Outcome: The proposed model outperforms state-of-the-art models across multiple languages.
MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning (2026.findings-acl)

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Challenge: Large Language Models (LLMs) exhibit severe hallucinations, which undermine reliability of automated scientific document understanding systems.
Approach: They propose a framework for mitigating scientific measurement hallucinations through enhanced reasoning and targeted optimization.
Outcome: The proposed framework significantly reduces hallucination rates and improves overall accuracy on the MeasEval benchmark.

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