SCAR: Efficient Instruction-Tuning for Large Language Models via Style Consistency-Aware Response Ranking

• URL: http://arxiv.org/abs/2406.10882v6
• Date: Sat, 12 Oct 2024 14:05:16 GMT
• Title: SCAR: Efficient Instruction-Tuning for Large Language Models via Style Consistency-Aware Response Ranking
• Authors: Zhuang Li, Yuncheng Hua, Thuy-Trang Vu, Haolan Zhan, Lizhen Qu, Gholamreza Haffari,
• Abstract summary: This research identifies two key stylistic elements in responses: linguistic form and semantic surprisal. Inspired by this, we introduce Style Consistency-Aware Response Ranking (SCAR) SCAR prioritizes instruction-response pairs in the training set based on their response stylistic consistency.
• Score: 56.93151679231602
• License:
• Abstract: Recent studies have shown that maintaining a consistent response style by human experts and enhancing data quality in training sets can significantly improve the performance of fine-tuned Large Language Models (LLMs) while reducing the number of training examples needed. However, the precise definition of style and the relationship between style, data quality, and LLM performance remains unclear. This research identifies two key stylistic elements in responses: linguistic form and semantic surprisal. We find that, among training data of comparable quality, higher consistency in these response elements leads to better LLM performance. Inspired by this, we introduce Style Consistency-Aware Response Ranking (SCAR), which automatically prioritizes instruction-response pairs in the training set based on their response stylistic consistency. By selecting the most style-consistent examples, sometimes as few as 0.7% of the full dataset, the fine-tuned LLMs can match or even surpass the performance of models trained on the entire dataset in coding and open-ended question-answering benchmarks. Code and data are available at https://github.com/zhuang-li/SCAR .

Related papers

• Aligning Instruction Tuning with Pre-training [81.4748965653345]
  We propose Aligning Instruction Tuning with Pre-training (AITP) to align instruction tuning with pre-training distributions. We show consistent performance improvements with AITP on three fully open large language models (LLMs) across eight benchmarks.
  arXiv  Detail & Related papers  (2025-01-16T08:27:40Z)
• A Systematic Examination of Preference Learning through the Lens of Instruction-Following [83.71180850955679]
  We use a novel synthetic data generation pipeline to generate 48,000 instruction unique-following prompts. With our synthetic prompts, we use two preference dataset curation methods - rejection sampling (RS) and Monte Carlo Tree Search (MCTS) Experiments reveal that shared prefixes in preference pairs, as generated by MCTS, provide marginal but consistent improvements. High-contrast preference pairs generally outperform low-contrast pairs; however, combining both often yields the best performance.
  arXiv  Detail & Related papers  (2024-12-18T15:38:39Z)
• Experience of Training a 1.7B-Parameter LLaMa Model From Scratch [10.39475177812483]
  We share insights gained from training DMaS-LLaMa-Lite on approximately 20 billion tokens of data. We chronicle the full training trajectory, documenting how evolving validation loss levels and downstream benchmarks reflect transitions from incoherent text to fluent, contextually grounded output. By detailing these experiences and offering training logs, checkpoints, and sample outputs, we aim to guide future researchers and practitioners in refining their pretraining strategies.
  arXiv  Detail & Related papers  (2024-12-17T21:15:52Z)
• Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning [71.2981957820888]
  We propose a novel Star-Agents framework, which automates the enhancement of data quality across datasets. The framework initially generates diverse instruction data with multiple LLM agents through a bespoke sampling method. The generated data undergo a rigorous evaluation using a dual-model method that assesses both difficulty and quality.
  arXiv  Detail & Related papers  (2024-11-21T02:30:53Z)
• How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics [49.9329723199239]
  We propose a method for the automated creation of a challenging test set without relying on the manual construction of artificial and unrealistic examples. We categorize the test set of popular NLI datasets into three difficulty levels by leveraging methods that exploit training dynamics. When our characterization method is applied to the training set, models trained with only a fraction of the data achieve comparable performance to those trained on the full dataset.
  arXiv  Detail & Related papers  (2024-10-04T13:39:21Z)
• Align$^2$LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation [56.75665429851673]
  This paper introduces a novel instruction curation algorithm, derived from two unique perspectives, human and LLM preference alignment. Experiments demonstrate that we can maintain or even improve model performance by compressing synthetic multimodal instructions by up to 90%.
  arXiv  Detail & Related papers  (2024-09-27T08:20:59Z)
• Large Language Model-guided Document Selection [23.673690115025913]
  Large Language Model (LLM) pre-training exhausts an ever growing compute budget. Recent research has demonstrated that careful document selection enables comparable model quality with only a fraction of the FLOPs. We explore a promising direction for scalable general-domain document selection.
  arXiv  Detail & Related papers  (2024-06-07T04:52:46Z)
• Towards Efficient Active Learning in NLP via Pretrained Representations [1.90365714903665]
  Fine-tuning Large Language Models (LLMs) is now a common approach for text classification in a wide range of applications. We drastically expedite this process by using pretrained representations of LLMs within the active learning loop. Our strategy yields similar performance to fine-tuning all the way through the active learning loop but is orders of magnitude less computationally expensive.
  arXiv  Detail & Related papers  (2024-02-23T21:28:59Z)
• Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning [39.73918872205541]
  Many recent methods focus on improving the data quality but often overlook the compatibility of the data with the student model being finetuned. This paper introduces Selective Reflection-Tuning, a novel paradigm that synergizes a teacher LLM's reflection and introspection for improving existing data quality. This teacher-student collaboration produces high-quality and student-compatible instruction-response pairs, resulting in sample-efficient instruction tuning.
  arXiv  Detail & Related papers  (2024-02-15T17:06:21Z)

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.