PoliPrompt: A High-Performance Cost-Effective LLM-Based Text Classification Framework for Political Science

• URL: http://arxiv.org/abs/2409.01466v1
• Date: Mon, 2 Sep 2024 21:05:31 GMT
• Title: PoliPrompt: A High-Performance Cost-Effective LLM-Based Text Classification Framework for Political Science
• Authors: Menglin Liu, Ge Shi,
• Abstract summary: This paper introduces a three-stage in-context learning approach that leverages large language models to improve classification accuracy. We validate our approach using datasets from the BBC news reports, Kavanaugh Supreme Court confirmation, and 2018 election campaign ads. The results show significant improvements in classification F1 score (+0.36 for zero-shot classification) with manageable economic costs.
• Score: 1.6967824074619953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in large language models (LLMs) have opened new avenues for enhancing text classification efficiency in political science, surpassing traditional machine learning methods that often require extensive feature engineering, human labeling, and task-specific training. However, their effectiveness in achieving high classification accuracy remains questionable. This paper introduces a three-stage in-context learning approach that leverages LLMs to improve classification accuracy while minimizing experimental costs. Our method incorporates automatic enhanced prompt generation, adaptive exemplar selection, and a consensus mechanism that resolves discrepancies between two weaker LLMs, refined by an advanced LLM. We validate our approach using datasets from the BBC news reports, Kavanaugh Supreme Court confirmation, and 2018 election campaign ads. The results show significant improvements in classification F1 score (+0.36 for zero-shot classification) with manageable economic costs (-78% compared with human labeling), demonstrating that our method effectively addresses the limitations of traditional machine learning while offering a scalable and reliable solution for text analysis in political science.

Related papers

• 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)
• Co-training for Low Resource Scientific Natural Language Inference [65.37685198688538]
  We propose a novel co-training method that assigns weights based on the training dynamics of the classifiers to the distantly supervised labels. By assigning importance weights instead of filtering out examples based on an arbitrary threshold on the predicted confidence, we maximize the usage of automatically labeled data. The proposed method obtains an improvement of 1.5% in Macro F1 over the distant supervision baseline, and substantial improvements over several other strong SSL baselines.
  arXiv  Detail & Related papers  (2024-06-20T18:35:47Z)
• Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation [2.0411082897313984]
  This study introduces a novel methodology that integrates human annotators and Large Language Models. The proposed framework integrates human annotation with the output of LLMs, depending on the model uncertainty levels. The empirical results show a substantial decrease in the costs associated with data annotation while either maintaining or improving model accuracy.
  arXiv  Detail & Related papers  (2024-06-17T21:45:48Z)
• LLMEmbed: Rethinking Lightweight LLM's Genuine Function in Text Classification [13.319594321038926]
  We propose a simple and effective transfer learning strategy, namely LLMEmbed, to address this classical but challenging task. We perform extensive experiments on publicly available datasets, and the results show that LLMEmbed achieves strong performance while enjoys low training overhead.
  arXiv  Detail & Related papers  (2024-06-06T03:46:59Z)
• RepEval: Effective Text Evaluation with LLM Representation [55.26340302485898]
  RepEval is a metric that leverages the projection of Large Language Models (LLMs) representations for evaluation. Our work underscores the richness of information regarding text quality embedded within LLM representations, offering insights for the development of new metrics.
  arXiv  Detail & Related papers  (2024-04-30T13:50:55Z)
• Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data [36.09359953556684]
  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.
  arXiv  Detail & Related papers  (2024-04-03T03:24:19Z)
• Large Language Models Are Zero-Shot Text Classifiers [3.617781755808837]
  Large language models (LLMs) have become extensively used across various sub-disciplines of natural language processing (NLP) In NLP, text classification problems have garnered considerable focus, but still faced with some limitations related to expensive computational cost, time consumption, and robust performance to unseen classes. With the proposal of chain of thought prompting (CoT), LLMs can be implemented using zero-shot learning (ZSL) with the step by step reasoning prompts.
  arXiv  Detail & Related papers  (2023-12-02T06:33:23Z)
• Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs [56.526095828316386]
  We propose a novel framework for adaptation with self-evaluation to improve the selective prediction performance of large language models (LLMs) We evaluate our method on a variety of question-answering (QA) datasets and show that it outperforms state-of-the-art selective prediction methods.
  arXiv  Detail & Related papers  (2023-10-18T03:34:59Z)
• Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning [37.22349652230841]
  Large language models (LLMs) enable in-context learning (ICL) by conditioning on a few labeled training examples as a text-based prompt. In this paper, we demonstrate that factual knowledge is imperative for the performance of ICL in three core facets. We introduce a novel Knowledgeable In-Context Tuning (KICT) framework to further improve the performance of ICL.
  arXiv  Detail & Related papers  (2023-09-26T09:06:39Z)
• OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning [49.38867353135258]
  We propose OverPrompt, leveraging the in-context learning capability of LLMs to handle multiple task inputs. Our experiments show that OverPrompt can achieve cost-efficient zero-shot classification without causing significant detriment to task performance.
  arXiv  Detail & Related papers  (2023-05-24T10:08:04Z)
• MC-BERT: Efficient Language Pre-Training via a Meta Controller [96.68140474547602]
  Large-scale pre-training is computationally expensive. ELECTRA, an early attempt to accelerate pre-training, trains a discriminative model that predicts whether each input token was replaced by a generator. We propose a novel meta-learning framework, MC-BERT, to achieve better efficiency and effectiveness.
  arXiv  Detail & Related papers  (2020-06-10T09:22:19Z)

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.