One-Shot Learning as Instruction Data Prospector for Large Language Models

• URL: http://arxiv.org/abs/2312.10302v4
• Date: Mon, 3 Jun 2024 13:46:16 GMT
• Title: One-Shot Learning as Instruction Data Prospector for Large Language Models
• Authors: Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li,
• Abstract summary: textscNuggets uses one-shot learning to select high-quality instruction data from extensive datasets. We show that instruction tuning with the top 1% of examples curated by textscNuggets substantially outperforms conventional methods employing the entire dataset.
• Score: 108.81681547472138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may compromise model performance. To address this challenge, we introduce \textsc{Nuggets}, a novel and efficient methodology that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets. \textsc{Nuggets} assesses the potential of individual instruction examples to act as effective one-shot learning instances, thereby identifying those that can significantly improve performance across diverse tasks. \textsc{Nuggets} utilizes a scoring system based on the impact of candidate examples on the perplexity of a diverse anchor set, facilitating the selection of the most advantageous data for instruction tuning. Through comprehensive evaluations on two benchmarks, including MT-Bench and Alpaca-Eval, we show that instruction tuning with the top 1\% of examples curated by \textsc{Nuggets} substantially outperforms conventional methods employing the entire dataset.

Related papers

• Less for More: Enhancing Preference Learning in Generative Language Models with Automated Self-Curation of Training Corpora [4.008122785948581]
  Ambiguity in language presents challenges in developing more enhanced language models. We introduce a self-curation method that preprocesses annotated datasets by leveraging proxy models trained directly on these datasets. Our method enhances preference learning by automatically detecting and removing ambiguous annotations within the dataset.
  arXiv  Detail & Related papers  (2024-08-23T02:27:14Z)
• Less is More: High-value Data Selection for Visual Instruction Tuning [127.38740043393527]
  We propose a high-value data selection approach TIVE, to eliminate redundancy within the visual instruction data and reduce the training cost. Our approach using only about 15% data can achieve comparable average performance to the full-data fine-tuned model across eight benchmarks.
  arXiv  Detail & Related papers  (2024-03-14T16:47:25Z)
• RECOST: External Knowledge Guided Data-efficient Instruction Tuning [25.985023475991625]
  We argue that most current data-efficient instruction-tuning methods are highly dependent on the quality of the original instruction-tuning dataset. We propose a framework dubbed as textbfRECOST, which integrates external-knowledge-base re-ranking and diversity-consistent sampling into a single pipeline.
  arXiv  Detail & Related papers  (2024-02-27T09:47:36Z)
• Prioritizing Informative Features and Examples for Deep Learning from Noisy Data [4.741012804505562]
  We propose a systemic framework that prioritizes informative features and examples to enhance each stage of the development process. We first propose an approach to extract only informative features that are inherent to solving a target task by using auxiliary out-of-distribution data. Next, we introduce an approach that prioritizes informative examples from unlabeled noisy data in order to reduce the labeling cost of active learning.
  arXiv  Detail & Related papers  (2024-02-27T07:15:35Z)
• Influence Scores at Scale for Efficient Language Data Sampling [3.072340427031969]
  "influence scores" are used to identify important subsets of data. In this paper, we explore the applicability of influence scores in language classification tasks.
  arXiv  Detail & Related papers  (2023-11-27T20:19:22Z)
• Towards General and Efficient Active Learning [20.888364610175987]
  Active learning aims to select the most informative samples to exploit limited annotation budgets. We propose a novel general and efficient active learning (GEAL) method in this paper. Our method can conduct data selection processes on different datasets with a single-pass inference of the same model.
  arXiv  Detail & Related papers  (2021-12-15T08:35:28Z)
• Online Coreset Selection for Rehearsal-based Continual Learning [65.85595842458882]
  In continual learning, we store a subset of training examples (coreset) to be replayed later to alleviate catastrophic forgetting. We propose Online Coreset Selection (OCS), a simple yet effective method that selects the most representative and informative coreset at each iteration. Our proposed method maximizes the model's adaptation to a target dataset while selecting high-affinity samples to past tasks, which directly inhibits catastrophic forgetting.
  arXiv  Detail & Related papers  (2021-06-02T11:39:25Z)
• Multitask Learning for Class-Imbalanced Discourse Classification [74.41900374452472]
  We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP.
  arXiv  Detail & Related papers  (2021-01-02T07:13:41Z)
• DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks [88.62288327934499]
  We propose a novel augmentation method with language models trained on the linearized labeled sentences. Our method is applicable to both supervised and semi-supervised settings.
  arXiv  Detail & Related papers  (2020-11-03T07:49:15Z)
• Improving Multi-Turn Response Selection Models with Complementary Last-Utterance Selection by Instance Weighting [84.9716460244444]
  We consider utilizing the underlying correlation in the data resource itself to derive different kinds of supervision signals. We conduct extensive experiments in two public datasets and obtain significant improvement in both datasets.
  arXiv  Detail & Related papers  (2020-02-18T06:29:01Z)

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.