mlsanityData to 14 Oct 2026

QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-Tuning

arXiv 2502.01703 ↗First posted 3 Feb 2025cs.LG cs.AI cs.CLcited by 2

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Abstract (arXiv)

Fine-tuning large language models (LLMs) is often constrained by the computational costs of processing massive datasets. We propose QLESS (Quantized Low-rank Gradient Similarity Search), which integrates gradient quantization with the LESS framework to enable memory-efficient data valuation and selection. QLESS employs a two-step compression process: first, it obtains low-dimensional gradient representations through LoRA-based random projection; then, it quantizes these gradients to low-bitwidth representations. Experiments on multiple LLM architectures (LLaMA, Mistral, Qwen) and benchmarks (MMLU, BBH, TyDiQA) show that QLESS achieves comparable data selection performance to LESS while reducing memory usage by up to 16x. Even 1-bit gradient quantization preserves data valuation quality. These findings underscore QLESS as a practical, scalable approach to identifying informative examples within strict memory constraints.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 41 references that are in the map. First posted on arXiv 3 Feb 2025.

Builds on

25 of 41 references.

References · 25

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  5. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  6. 2023Mistral 7Bcited by 3,164
  7. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,807
  8. 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
  9. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 739
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  11. 2020Estimating Training Data Influence by Tracing Gradient Descentcited by 580
  12. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
13 more
  1. 2024LESS: Selecting Influential Data for Targeted Instruction Tuningcited by 532
  2. 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 488
  3. 2018Sparsified SGD with Memorycited by 376
  4. 2017Sparse Communication for Distributed Gradient Descentcited by 249
  5. 2023Controlling Text-to-Image Diffusion by Orthogonal Finetuningcited by 208
  6. 2017Gradient Sparsification for Communication-Efficient Distributed Optimizationcited by 201
  7. 2023DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Modelscited by 123
  8. 2023Parameter-Efficient Orthogonal Finetuning via Butterfly Factorizationcited by 114
  9. 2023Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluationcited by 109
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  12. 2013Coding for Random Projectionscited by 8
  13. 2013A Quantized Johnson Lindenstrauss Lemma: The Finding of Buffon's Needlecited by 4

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Citing papers per year: 2026: 2.

2 papers.

  1. 2026Unifying Data, Memory, and Compute Efficiency in LLM training: A Surveycited by 0
  2. 2026Scaling Influence Functions in LLMs through Eigenbasis-Corrected One-Bit Gradient Projectioncited by 0

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