Papers by Michael Hedderich
Meta Self-Refinement for Robust Learning with Weak Supervision (2023.eacl-main)
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| Challenge: | Recent methods leverage self-training to build noise-resistant models . however, the teacher trained under weak supervision may have fitted a substantial amount of noise and therefore produce incorrect pseudo-labels. |
| Approach: | They propose a framework that encourages teacher to refine its pseudo-labels to effectively combat label noise from weak supervision. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by 11.4% in accuracy and 9.26% in F1 score on eight NLP benchmarks. |
The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models (2024.findings-emnlp)
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Bolei Ma, Xinpeng Wang, Tiancheng Hu, Anna-Carolina Haensch, Michael Hedderich, Barbara Plank, Frauke Kreuter
| Challenge: | Recent advances in Large Language Models have sparked interest in validating human-like cognitive-behavioral traits. |
| Approach: | They examine whether LLM outputs reflect human-like cognitive-behavioral traits . they find that measuring AOVs embedded within LLMs remains opaque . |
| Outcome: | The proposed model can be used to evaluate human-like cognitive-behavioral traits . the proposed model could be used in writing assistants and other applications . |
MCSE: Multimodal Contrastive Learning of Sentence Embeddings (2022.naacl-main)
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| Challenge: | Existing approaches to learning semantically meaningful sentence embeddings are limited by the complexity of pre-trained models. |
| Approach: | They propose a sentence embedding learning approach that exploits both visual and textual information via a multimodal contrastive objective. |
| Outcome: | The proposed approach improves the state-of-the-art average Spearman’s correlation by 1.7% on a variety of semantic textual similarity tasks. |