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Untied embeddings boost private LLM training accuracy and efficiency

By PulseAugur Editorial · [2 sources] ·

A new study published on arXiv explores the impact of weight tying in decoder-only Large Language Models (LLMs) when fine-tuned using Differentially Private Stochastic Gradient Descent (DP-SGD). The research found that untying the input and output embeddings consistently outperformed weight-tied models, leading to accuracy gains of up to 4.74% on benchmarks like SST-2 and QNLI. Furthermore, untied embeddings facilitate more memory-efficient DP-SGD training by enabling the use of ghost clipping, resulting in over 60% lower memory usage compared to weight-tied models. AI

IMPACT Untied embeddings offer a more efficient and effective approach for private LLM fine-tuning, potentially influencing future model designs.

RANK_REASON The cluster contains a research paper detailing findings on LLM architecture and training methods.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Untied embeddings boost private LLM training accuracy and efficiency

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The cluster contains a research paper detailing findings on LLM architecture and training methods.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Razan El Mais, Ali Chehab, Ibrahim Issa, Razane Tajeddine ·

    Is Weight Tying Still Beneficial for Decoder-Only LLMs in Private Settings Under DP-SGD?

    arXiv:2609.40335v1 Announce Type: new Abstract: Differentially Private Stochastic Gradient Descent (DP-SGD) is a leading approach for privacy-preserving fine-tuning of large language models (LLMs). Many decoder-only LLMs employ weight tying between input and output embeddings, a …

  2. Mastodon — mastodon.social TIER_1 English(EN) · notatechguy ·

    Weight tying costs private LLM training 4.74 accuracy points Untying input and output embeddings in decoder-only LLMs lifts accuracy up to 4.74 percentage point

    Weight tying costs private LLM training 4.74 accuracy points Untying input and output embeddings in decoder-only LLMs lifts accuracy up to 4.74 percentage points under differential privacy fine-tuning. https://www. notatechguy.com/weight-tying-c osts-private-llm-training-4-74-acc…

    LINKS notatechguy.com/weight-tying-costs-privat…

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