Enhancing SLM via ChatGPT and Dataset Augmentation

• URL: http://arxiv.org/abs/2409.12599v1
• Date: Thu, 19 Sep 2024 09:24:36 GMT
• Title: Enhancing SLM via ChatGPT and Dataset Augmentation
• Authors: Tom Pieper, Mohamad Ballout, Ulf Krumnack, Gunther Heidemann, Kai-Uwe Kühnberger,
• Abstract summary: We employ knowledge distillation-based techniques and synthetic dataset augmentation to bridge the performance gap between large language models (LLMs) and small language models (SLMs) Our methods involve two forms of rationale generation--information extraction and informed reasoning--to enrich the ANLI dataset. Our findings reveal that the incorporation of synthetic rationales significantly improves the model's ability to comprehend natural language, leading to 1.3% and 2.3% higher classification accuracy, respectively, on the ANLI dataset.
• Score: 0.3844771221441211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores the enhancement of small language models through strategic dataset augmentation via ChatGPT-3.5-Turbo, in the domain of Natural Language Inference (NLI). By employing knowledge distillation-based techniques and synthetic dataset augmentation, we aim to bridge the performance gap between large language models (LLMs) and small language models (SLMs) without the immense cost of human annotation. Our methods involve two forms of rationale generation--information extraction and informed reasoning--to enrich the ANLI dataset. We then fine-tune T5-Small on these augmented datasets, evaluating its performance against an established benchmark. Our findings reveal that the incorporation of synthetic rationales significantly improves the model's ability to comprehend natural language, leading to 1.3\% and 2.3\% higher classification accuracy, respectively, on the ANLI dataset, demonstrating the potential of leveraging LLMs for dataset augmentation. This approach not only enhances the performance of smaller models on complex tasks but also introduces a cost-effective method for fine-tuning smaller language models. By advancing our understanding of knowledge distillation and fine-tuning strategies, this work contributes to the ongoing effort to create more capable and efficient NLP systems.

Related papers

• Language Models are Graph Learners [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, including Graph Neural Networks (GNNs) and Graph Transformers (GTs) We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation [2.9921619703037274]
  We propose a retrieval augmented generation (RAG) framework backed by a large language model (LLM) to correct the output of a smaller model for the linguistic task of morphological glossing. We leverage linguistic information to make up for the lack of data and trainable parameters, while allowing for inputs from written descriptive grammars interpreted and distilled through an LLM. We show that a compact, RAG-supported model is highly effective in data-scarce settings, achieving a new state-of-the-art for this task and our target languages.
  arXiv  Detail & Related papers  (2024-10-01T04:20:14Z)
• Unlocking the Potential of Model Merging for Low-Resource Languages [66.7716891808697]
  Adapting large language models to new languages typically involves continual pre-training (CT) followed by supervised fine-tuning (SFT) We propose model merging as an alternative for low-resource languages, combining models with distinct capabilities into a single model without additional training. Experiments based on Llama-2-7B demonstrate that model merging effectively endows LLMs for low-resource languages with task-solving abilities, outperforming CT-then-SFT in scenarios with extremely scarce data.
  arXiv  Detail & Related papers  (2024-07-04T15:14:17Z)
• CLAIM Your Data: Enhancing Imputation Accuracy with Contextual Large Language Models [0.18416014644193068]
  This paper introduces the Contextual Language model for Accurate Imputation Method (CLAIM) Unlike traditional imputation methods, CLAIM utilizes contextually relevant natural language descriptors to fill missing values. Our evaluations across diverse datasets and missingness patterns reveal CLAIM's superior performance over existing imputation techniques.
  arXiv  Detail & Related papers  (2024-05-28T00:08:29Z)
• TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale [66.01943465390548]
  We introduce TriSum, a framework for distilling large language models' text summarization abilities into a compact, local model. Our method enhances local model performance on various benchmarks. It also improves interpretability by providing insights into the summarization rationale.
  arXiv  Detail & Related papers  (2024-03-15T14:36:38Z)
• ExaRanker-Open: Synthetic Explanation for IR using Open-Source LLMs [60.81649785463651]
  We introduce ExaRanker-Open, where we adapt and explore the use of open-source language models to generate explanations. Our findings reveal that incorporating explanations consistently enhances neural rankers, with benefits escalating as the LLM size increases.
  arXiv  Detail & Related papers  (2024-02-09T11:23:14Z)
• Contextualization Distillation from Large Language Model for Knowledge Graph Completion [51.126166442122546]
  We introduce the Contextualization Distillation strategy, a plug-in-and-play approach compatible with both discriminative and generative KGC frameworks. Our method begins by instructing large language models to transform compact, structural triplets into context-rich segments. Comprehensive evaluations across diverse datasets and KGC techniques highlight the efficacy and adaptability of our approach.
  arXiv  Detail & Related papers  (2024-01-28T08:56:49Z)
• Improving Small Language Models on PubMedQA via Generative Data Augmentation [4.96649519549027]
  Large Language Models (LLMs) have made remarkable advancements in the field of natural language processing. Small Language Models (SLMs) are known for their efficiency, but they often struggle with limited capacity and training data. We introduce a novel method aimed at improving SLMs in the medical domain using LLM-based generative data augmentation.
  arXiv  Detail & Related papers  (2023-05-12T23:49:23Z)
• SDA: Improving Text Generation with Self Data Augmentation [88.24594090105899]
  We propose to improve the standard maximum likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning phase for automatic data augmentation. Unlike most existing sentence-level augmentation strategies, our method is more general and could be easily adapted to any MLE-based training procedure.
  arXiv  Detail & Related papers  (2021-01-02T01:15:57Z)
• Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning [30.5853328612593]
  In this work, we explore fine-tuning methods of BERT -- a pre-trained Transformer based language model. Our experimental results show an advantage in model performance by maximizing the approximate knowledge gain of the model. We analyze the benefits of freezing layers of the language model during fine-tuning to reduce the number of trainable parameters.
  arXiv  Detail & Related papers  (2020-12-04T08:34:39Z)

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.