Papers by Fengting Yuchi

2 papers
LLMs Know More About Numbers than They Can Say (2026.eacl-short)

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Challenge: Large language models (LLMs) are increasingly used in mathematical, scientific, financial and engineering domains.
Approach: They probe the hidden states of several smaller open-source LLMs to find out how big they are .
Outcome: The proposed model improves verbalized accuracy by 3.22% over base models.
FedDQC: Data Quality Control in Federated Instruction-tuning of Large Language Models (2025.findings-acl)

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Challenge: Federated Learning (FL) enables privacy-preserving collaborative instruction tuning of large language models.
Approach: They propose a federated instruction tuning framework with dynamic data quality control to solve this problem.
Outcome: The proposed framework improves performance on mixed-quality datasets on synthetic and real-world datasets.

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