Efficient argument classification with compact language models and ChatGPT-4 refinements

• URL: http://arxiv.org/abs/2403.15473v1
• Date: Wed, 20 Mar 2024 16:24:10 GMT
• Title: Efficient argument classification with compact language models and ChatGPT-4 refinements
• Authors: Marcin Pietron, Rafał Olszowski, Jakub Gomułka,
• Abstract summary: This paper presents comparative studies between a few deep learning-based models in argument mining. The main novelty of this paper is the ensemble model which is based on BERT architecture and ChatGPT-4 as fine tuning model. The presented results show that BERT+ChatGPT-4 outperforms the rest of the models including other Transformer-based and LSTM-based models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Argument mining (AM) is defined as the task of automatically identifying and extracting argumentative components (e.g. premises, claims, etc.) and detecting the existing relations among them (i.e., support, attack, no relations). Deep learning models enable us to analyze arguments more efficiently than traditional methods and extract their semantics. This paper presents comparative studies between a few deep learning-based models in argument mining. The work concentrates on argument classification. The research was done on a wide spectrum of datasets (Args.me, UKP, US2016). The main novelty of this paper is the ensemble model which is based on BERT architecture and ChatGPT-4 as fine tuning model. The presented results show that BERT+ChatGPT-4 outperforms the rest of the models including other Transformer-based and LSTM-based models. The observed improvement is, in most cases, greater than 10The presented analysis can provide crucial insights into how the models for argument classification should be further improved. Additionally, it can help develop a prompt-based algorithm to eliminate argument classification errors.

Related papers

• Explaining Datasets in Words: Statistical Models with Natural Language Parameters [66.69456696878842]
  We introduce a family of statistical models -- including clustering, time series, and classification models -- parameterized by natural language predicates. We apply our framework to a wide range of problems: taxonomizing user chat dialogues, characterizing how they evolve across time, finding categories where one language model is better than the other.
  arXiv  Detail & Related papers  (2024-09-13T01:40:20Z)
• Fine-Tuning with Divergent Chains of Thought Boosts Reasoning Through Self-Correction in Language Models [63.36637269634553]
  We present a novel method of further improving performance by requiring models to compare multiple reasoning chains. We find that instruction tuning on DCoT datasets boosts the performance of even smaller, and therefore more accessible, language models.
  arXiv  Detail & Related papers  (2024-07-03T15:01:18Z)
• Explaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings [32.03184402316848]
  We analyze attribution scores extracted from prompt-based models w.r.t. plausibility and faithfulness. We find that using the prompting paradigm yields more plausible explanations than fine-tuning the models in low-resource settings.
  arXiv  Detail & Related papers  (2024-03-08T14:14:37Z)
• Split and Rephrase with Large Language Models [2.499907423888049]
  Split and Rephrase (SPRP) task consists in splitting complex sentences into a sequence of shorter grammatical sentences. We evaluate large language models on the task, showing that they can provide large improvements over the state of the art on the main metrics.
  arXiv  Detail & Related papers  (2023-12-18T10:16:37Z)
• The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute [66.84421705029624]
  We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
  arXiv  Detail & Related papers  (2023-09-20T10:31:17Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• Rationale-Guided Few-Shot Classification to Detect Abusive Language [5.977278650516324]
  We propose RGFS (Rationale-Guided Few-Shot Classification) for abusive language detection. We introduce two rationale-integrated BERT-based architectures (the RGFS models) and evaluate our systems over five different abusive language datasets.
  arXiv  Detail & Related papers  (2022-11-30T14:47:14Z)
• EPIC TTS Models: Empirical Pruning Investigations Characterizing Text-To-Speech Models [26.462819114575172]
  This work compares sparsity paradigms in text-to-speech synthesis. It is the first work that compares sparsity paradigms in text-to-speech synthesis.
  arXiv  Detail & Related papers  (2022-09-22T09:47:25Z)
• Dynamically-Scaled Deep Canonical Correlation Analysis [77.34726150561087]
  Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. We introduce a novel dynamic scaling method for training an input-dependent canonical correlation model.
  arXiv  Detail & Related papers  (2022-03-23T12:52:49Z)
• Deep Learning Models for Knowledge Tracing: Review and Empirical Evaluation [2.423547527175807]
  We review and evaluate a body of deep learning knowledge tracing (DLKT) models with openly available and widely-used data sets. The evaluated DLKT models have been reimplemented for assessing and replicability of previously reported results.
  arXiv  Detail & Related papers  (2021-12-30T14:19:27Z)
• Explaining and Improving Model Behavior with k Nearest Neighbor Representations [107.24850861390196]
  We propose using k nearest neighbor representations to identify training examples responsible for a model's predictions. We show that kNN representations are effective at uncovering learned spurious associations. Our results indicate that the kNN approach makes the finetuned model more robust to adversarial inputs.
  arXiv  Detail & Related papers  (2020-10-18T16:55:25Z)

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.