Papers by Shuchang Zhou
Enhancing Tool Learning in Large Language Models with Hierarchical Error Checklists (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have advanced natural language processing, but their effectiveness is often hampered by parameter mis-filling during tool calling. |
| Approach: | They propose a hierarchical tool error checklist framework to diagnose and mitigate tool-calling errors without relying on extensive real-world interactions. |
| Outcome: | The proposed framework improves parameter-filling accuracy and tool-calling success rates compared to baseline methods. |
Active Learning Approaches to Enhancing Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | a limited human translation budget is required to train neural machine translation models. |
| Approach: | They propose to integrate active learning into neural machine translation techniques . they propose a word frequency based acquisition function and an uncertainty based method . |
| Outcome: | The proposed method outperforms other acquisition functions on a limited human translation budget. |
Multi-split Reversible Transformers Can Enhance Neural Machine Translation (2021.eacl-main)
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| Challenge: | Large-scale transformers have been shown to improve neural machine translation performance but training these wider and deeper networks could be extremely memory intensive. |
| Approach: | They propose a multi-split based reversible transformer and a backpropagation algorithm that does not need to store activations for most layers. |
| Outcome: | The proposed model outperforms the vanilla transformer by at least 1.4 BLEU points in three datasets. |