Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models

• URL: http://arxiv.org/abs/2408.02085v3
• Date: Wed, 7 Aug 2024 06:04:31 GMT
• Title: Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
• Authors: Yulei Qin, Yuncheng Yang, Pengcheng Guo, Gang Li, Hang Shao, Yuchen Shi, Zihan Xu, Yun Gu, Ke Li, Xing Sun,
• Abstract summary: Instruction tuning plays a critical role in aligning large language models (LLMs) with human preference. Data assessment and selection methods have been proposed in the fields of natural language processing (NLP) and deep learning. We present a comprehensive review on existing literature of data assessment and selection especially for instruction tuning of LLMs.
• Score: 33.488331159912136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instruction tuning plays a critical role in aligning large language models (LLMs) with human preference. Despite the vast amount of open instruction datasets, naively training a LLM on all existing instructions may not be optimal and practical. To pinpoint the most beneficial datapoints, data assessment and selection methods have been proposed in the fields of natural language processing (NLP) and deep learning. However, under the context of instruction tuning, there still exists a gap in knowledge on what kind of data evaluation metrics can be employed and how they can be integrated into the selection mechanism. To bridge this gap, we present a comprehensive review on existing literature of data assessment and selection especially for instruction tuning of LLMs. We systematically categorize all applicable methods into quality-based, diversity-based, and importance-based ones where a unified, fine-grained taxonomy is structured. For each category, representative methods are elaborated to describe the landscape of relevant research. In addition, comparison between latest methods is conducted on their officially reported results to provide in-depth discussions on their limitations. Finally, we summarize the open challenges and propose the promosing avenues for future studies. All related contents are available at https://github.com/yuleiqin/fantastic-data-engineering.

Related papers

• Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts [1.565361244756411]
  Large language models (LLMs) play a crucial role in natural language processing (NLP) tasks. This study applied prompt-based data augmentation to detect mentions of green practices in Russian social media.
  arXiv  Detail & Related papers  (2024-11-22T12:37:41Z)
• A Survey of Small Language Models [104.80308007044634]
  Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources. We present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques.
  arXiv  Detail & Related papers  (2024-10-25T23:52:28Z)
• Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions [62.12545440385489]
  Large language models (LLMs) have brought substantial advancements in text generation, but their potential for enhancing classification tasks remains underexplored. We propose a framework for thoroughly investigating fine-tuning LLMs for classification, including both generation- and encoding-based approaches. We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task.
  arXiv  Detail & Related papers  (2024-10-02T20:48:28Z)
• Exploring Large Language Models for Feature Selection: A Data-centric Perspective [17.99621520553622]
  Large Language Models (LLMs) have influenced various domains, leveraging their exceptional few-shot and zero-shot learning capabilities. We aim to explore and understand the LLMs-based feature selection methods from a data-centric perspective. Our findings emphasize the effectiveness and robustness of text-based feature selection methods and showcase their potentials using a real-world medical application.
  arXiv  Detail & Related papers  (2024-08-21T22:35:19Z)
• Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets [19.021200954913482]
  The analysis delves into 30 existing cloze-style and multiple-choice MRC benchmark datasets. The paper categorizes recent methodologies into Fine-tuned and Prompt-tuned methods.
  arXiv  Detail & Related papers  (2024-08-04T18:57:21Z)
• A Survey on Data Selection for Language Models [148.300726396877]
  Data selection methods aim to determine which data points to include in a training dataset. Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive. Few organizations have the resources for extensive data selection research.
  arXiv  Detail & Related papers  (2024-02-26T18:54:35Z)
• Large Language Models for Data Annotation: A Survey [49.8318827245266]
  The emergence of advanced Large Language Models (LLMs) presents an unprecedented opportunity to automate the complicated process of data annotation. This survey includes an in-depth taxonomy of data types that LLMs can annotate, a review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation.
  arXiv  Detail & Related papers  (2024-02-21T00:44:04Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media [0.0]
  We present a novel pool-based active learning method for the training of large unlabeled corpus with minimum annotation cost. Our proposed method does not have any parameters to be tuned, making it dataset-independent. Our method achieves a higher performance in comparison to the state-of-the-art active learning strategies.
  arXiv  Detail & Related papers  (2022-01-28T19:19:03Z)
• A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
  The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
  arXiv  Detail & Related papers  (2020-08-02T00:09:03Z)

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.