# Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data

arXiv: https://arxiv.org/abs/2404.02422 · HTML: https://mlsanity.com/paper/2404.02422/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Self-training for few-shot NLP, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Placed in

- Synthetic data and self-improvement for LLMs / [Self-training for few-shot NLP](https://mlsanity.com/synth/self-training-for-few-shot-nlp/)

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://mlsanity.com/paper/2205.05638/) (2022)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://mlsanity.com/paper/2306.15895/) (2023)
- [AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators](https://mlsanity.com/paper/2303.16854/) (2023)
- [One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning](https://mlsanity.com/paper/2306.07967/) (2023)
- [LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging](https://mlsanity.com/paper/2209.09900/) (2022)

## Built on by (most cited first, 7 of 7)

- [Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs](https://mlsanity.com/paper/2408.12060/) (2024)
- [Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions](https://mlsanity.com/paper/2410.02028/) (2024)
- [Little Giants: Synthesizing High-Quality Embedding Data at Scale](https://mlsanity.com/paper/2410.18634/) (2024)
- [Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions](https://mlsanity.com/paper/2409.14572/) (2024)
- [Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence](https://mlsanity.com/paper/2510.16657/) (2025)
- [An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification](https://mlsanity.com/paper/2409.03203/) (2024)
- [A Comprehensive Dataset for Human vs. AI Generated Text Detection](https://mlsanity.com/paper/2510.22874/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. In-Context Learning (ICL) typically achieves better accuracy than the 0-shot setting, but it pays in terms of efficiency, due to the longer input prompt. In this paper, we propose a strategy to make LLMs as efficient as 0-shot text classifiers, while getting comparable or better accuracy than ICL. Our solution targets the low resource setting, i.e., when only 4 examples per class are available. Using a single LLM and few-shot real data we perform a sequence of generation, filtering and Parameter-Efficient Fine-Tuning steps to create a robust and efficient classifier. Experimental results show that our approach leads to competitive results on multiple text classification datasets.