Papers by Enmao Diao

3 papers
From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models (2026.acl-long)

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Challenge: Structured pruning is a practical approach to deploying large language models (LLMs) but it fails to capitalize on modest task-specific calibration signals, causing limited downstream gains.
Approach: They propose a method that removes attention heads and MLP channels using loss-based important scores . they use perplexity for language modeling and a margin-based objective for decision-style tasks .
Outcome: The proposed method lowers perplexity and improves accuracy at higher sparsity . it also stabilizes accuracy and mitigates perxity collapse without fine-tuning .
ESC: Efficient Speech Coding with Cross-Scale Residual Vector Quantized Transformers (2024.emnlp-main)

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Challenge: Existing neural speech codecs trade model complexity for reconstruction performance . ESC is a lightweight, parameter-efficient speech coder .
Approach: They propose an efficient speech codec based on a cross-scale residual vector quantization scheme and transformers that can achieve high-fidelity speech reconstruction with significantly lower model complexity.
Outcome: The proposed codec achieves high-fidelity speech reconstruction with significantly lower model complexity.
AID: Adaptive Integration of Detectors for Safe AI with Language Models (2025.naacl-long)

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Challenge: Large language models (LLMs) are increasingly used to generate human-like text, but safety concerns have emerged with the deployment of LLMs.
Approach: They propose an approach that orchestrates the strengths of multiple pretrained detectors to ensure comprehensive effectiveness in diverse scenarios.
Outcome: The proposed approach can improve the area under the curve (AUC) by 0.07 to 0.21, with a median of 0.12, compared to the best individual detectors developed for specific safety aspects.

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