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SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning

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arxiv 2405.00705 v2 pith:6U357Z4A submitted 2024-04-23 cs.CL cs.LG

Yexiao He , Ziyao Wang , Zheyu Shen , Guoheng Sun , Yucong Dai , Yongkai Wu , Hongyi Wang , Ang Li This is my paper · ORCID
classification cs.CLcs.LG
keywords datasetsshedllmsdatafine-tuningperformanceachieveacross
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The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning. Recent studies have discovered that LLMs can achieve desirable performance with only a small amount of high-quality data, suggesting that a large amount of the data in these extensive datasets is redundant or even harmful. Identifying high-quality data from vast datasets to curate small yet effective datasets has emerged as a critical challenge. In this paper, we introduce SHED, an automated dataset refinement framework based on Shapley value for instruction fine-tuning. SHED eliminates the need for human intervention or the use of commercial LLMs. Moreover, the datasets curated through SHED exhibit transferability, indicating they can be reused across different LLMs with consistently high performance. We conduct extensive experiments to evaluate the datasets curated by SHED. The results demonstrate SHED's superiority over state-of-the-art methods across various tasks and LLMs; notably, datasets comprising only 10% of the original data selected by SHED achieve performance comparable to or surpassing that of the full datasets.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection

    cs.CL 2025-05 conditional novelty 6.0 of 10

    RICo scores instruction examples by their in-context perplexity effect on an assessment set, then trains a lightweight selector to pick top-scoring data, achieving better benchmark results from 5% to 15% of the original data.

  2. SCAR: Shapley Credit Assignment for More Efficient RLHF

    cs.AI 2025-05 conditional novelty 5.0 of 10

    SCAR redistributes the terminal RLHF reward to tokens and spans via Shapley values, preserving the total return while improving training efficiency and final reward across three LLM alignment tasks.

  3. A Survey of LLM $\times$ DATA

    cs.DB 2025-05 conditional novelty 5.0 of 10

    A comprehensive survey of the bidirectional links between LLMs and data management, organized as DATA4LLM and LLM4DATA with a new 'IaaS' data-quality framework.

  4. Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers

    cs.AI 2025-02 conditional novelty 5.0 of 10

    A taxonomy-based survey of bidirectional game theory and LLM research, spanning evaluation, alignment, economic competition, and LLM-driven game solving.

  5. Data-efficient LLM Fine-tuning for Code Generation

    cs.CL 2025-04 conditional novelty 4.0 of 10

    Selecting the hardest 30% to 40% of code examples per cluster and packing tokens by length matches or beats full-data fine-tuning on HumanEval and MBPP while cutting training time and GPU memory.

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