A Framework for Cost-Effective and Self-Adaptive LLM Shaking and Recovery Mechanism

• URL: http://arxiv.org/abs/2403.07283v1
• Date: Tue, 12 Mar 2024 03:30:04 GMT
• Title: A Framework for Cost-Effective and Self-Adaptive LLM Shaking and Recovery Mechanism
• Authors: Zhiyu Chen, Yu Li, Suochao Zhang, Jingbo Zhou, Jiwen Zhou, Chenfu Bao, Dianhai Yu
• Abstract summary: We introduce a cost-effective and self-adaptive LLM shaking tuning and recovery mechanism, named CypherTalk. With carefully designed horizontal and vertical shaking operators, we can achieve comparable accuracy results with SOTA privacy-preserving LLM schemes. Experiments also show that with the CypherTalk framework, users can achieve reliable accuracy when using optimized shaking operator settings.
• Score: 33.330424243007265
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As Large Language Models (LLMs) gain great success in real-world applications, an increasing number of users are seeking to develop and deploy their customized LLMs through cloud services. Nonetheless, in some specific domains, there are still concerns regarding cost and trade-offs between privacy issues and accuracy. In this study, we introduce a cost-effective and self-adaptive LLM shaking tuning and recovery mechanism, named CypherTalk. With carefully designed horizontal and vertical shaking operators, we can achieve comparable accuracy results with SOTA privacy-preserving LLM schemes using Cryptography-based or Differential Privacy-based methods. Experiments also show that with the CypherTalk framework, users can achieve reliable accuracy when using optimized shaking operator settings. To our best knowledge, this is the first work that considers cost, and trade-off between model utility and privacy in LLM scenarios.

Related papers

• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression. LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model. Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• Differentially Private Steering for Large Language Model Alignment [55.30573701583768]
  We present the first study of aligning Large Language Models with private datasets. Our work proposes the textitunderlinePrivate underlineSteering for LLM underlineAment (PSA) algorithm. Our results show that PSA achieves DP guarantees for LLM alignment with minimal loss in performance.
  arXiv  Detail & Related papers  (2025-01-30T17:58:36Z)
• Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions [59.5243730853157]
  Federated learning (FL) provides a privacy-preserving solution for fine-tuning pre-trained large language models (LLMs) using distributed private datasets. This article conducts a comparative analysis of three advanced federated LLM (FedLLM) frameworks that integrate knowledge distillation (KD) and split learning (SL) to mitigate these issues.
  arXiv  Detail & Related papers  (2025-01-08T11:37:06Z)
• A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs [74.35290684163718]
  A primary challenge in large language model (LLM) development is their onerous pre-training cost. This paper explores a promising paradigm to improve LLM pre-training efficiency and quality by leveraging a small language model (SLM)
  arXiv  Detail & Related papers  (2024-10-24T14:31:52Z)
• Model-Based Privacy-Preserving Knowledge Transfer for Large Language Models [34.949731264918846]
  Llamdex is a framework that enhances large language models (LLMs) using only models trained on domain-specific data. Our approach significantly enhances the accuracy of domain-specific tasks, achieving up to a 26% accuracy improvement.
  arXiv  Detail & Related papers  (2024-10-14T13:18:20Z)
• Understanding the Performance and Estimating the Cost of LLM Fine-Tuning [9.751868268608675]
  Fine-tuning Large Language Models (LLMs) for specific tasks in a cost-effective manner. In this paper, we characterize sparse Mixture of Experts (MoE) based LLM fine-tuning to understand their accuracy and runtime performance. We also develop and validate an analytical model to estimate the cost of LLM fine-tuning on the cloud.
  arXiv  Detail & Related papers  (2024-08-08T16:26:07Z)
• Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data [53.70870879858533]
  We introduce a Federated Domain-specific Knowledge Transfer framework. It enables domain-specific knowledge transfer from LLMs to SLMs while preserving clients' data privacy. The proposed FDKT framework consistently and greatly improves SLMs' task performance by around 5% with a privacy budget of less than 10.
  arXiv  Detail & Related papers  (2024-05-23T06:14:35Z)
• SMART: Automatically Scaling Down Language Models with Accuracy Guarantees for Reduced Processing Fees [21.801053526411415]
  Large Language Models (LLMs) have significantly boosted performance in natural language processing (NLP) tasks. The deployment of high-performance LLMs incurs substantial costs, primarily due to the increased number of parameters aimed at enhancing model performance. We introduce SMART, a novel framework designed to minimize the inference costs of NLP tasks while ensuring sufficient result quality.
  arXiv  Detail & Related papers  (2024-03-11T17:45:47Z)
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models [52.98743860365194]
  We propose a new fine-tuning method called Self-Play fIne-tuNing (SPIN) At the heart of SPIN lies a self-play mechanism, where the LLM refines its capability by playing against instances of itself. This sheds light on the promise of self-play, enabling the achievement of human-level performance in LLMs without the need for expert opponents.
  arXiv  Detail & Related papers  (2024-01-02T18:53:13Z)
• Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production [3.41402911469979]
  Many companies use large language models (LLMs) offered as a service, like OpenAI's GPT-4, to create AI-enabled product experiences. At the same time, a flurry of open-source small language models (SLMs) has been made available for commercial use. This paper presents a systematic evaluation methodology and a characterization of modern open-source SLMs.
  arXiv  Detail & Related papers  (2023-12-20T19:27:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.