Papers by Jianheng Huang

2 papers
A Learning Rate Path Switching Training Paradigm for Version Updates of Large Language Models (2024.emnlp-main)

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Challenge: Version updates are an indispensable requirement for Large Language Models . a large learning rate in the first stage and a complete learning decay process are crucial for version updates of LLMs.
Approach: They propose a learning rate path switching training paradigm for version updates of Large Language Models.
Outcome: The proposed paradigm reduces training cost to 58% when training four versions of LLMs compared to PTFS and CPT .
Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal (2024.acl-long)

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Challenge: Existing methods to train LLMs on previous training data are not feasible in real-world applications because of catastrophic forgetting.
Approach: They propose a framework that uses the LLM to generate synthetic instances for rehearsal and refine the instance outputs based on the synthetic inputs.
Outcome: The proposed framework achieves superior or comparable performance compared to conventional rehearsal-based approaches while being more data-efficient.

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