Assessing Generative Language Models in Classification Tasks: Performance and Self-Evaluation Capabilities in the Environmental and Climate Change Domain

• URL: http://arxiv.org/abs/2408.17362v1
• Date: Fri, 30 Aug 2024 15:52:41 GMT
• Title: Assessing Generative Language Models in Classification Tasks: Performance and Self-Evaluation Capabilities in the Environmental and Climate Change Domain
• Authors: Francesca Grasso, Stefano Locci,
• Abstract summary: This paper examines the performance of two Large Language Models (LLMs), GPT3.5 and Llama2 and one Small Language Model (SLM) Gemma, across three different classification tasks within the climate change (CC) and environmental domain.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper examines the performance of two Large Language Models (LLMs), GPT3.5 and Llama2 and one Small Language Model (SLM) Gemma, across three different classification tasks within the climate change (CC) and environmental domain. Employing BERT-based models as a baseline, we compare their efficacy against these transformer-based models. Additionally, we assess the models' self-evaluation capabilities by analyzing the calibration of verbalized confidence scores in these text classification tasks. Our findings reveal that while BERT-based models generally outperform both the LLMs and SLM, the performance of the large generative models is still noteworthy. Furthermore, our calibration analysis reveals that although Gemma is well-calibrated in initial tasks, it thereafter produces inconsistent results; Llama is reasonably calibrated, and GPT consistently exhibits strong calibration. Through this research, we aim to contribute to the ongoing discussion on the utility and effectiveness of generative LMs in addressing some of the planet's most urgent issues, highlighting their strengths and limitations in the context of ecology and CC.

Related papers

• Unraveling the Capabilities of Language Models in News Summarization [0.0]
  This work provides a comprehensive benchmarking of 20 recent language models, focusing on smaller ones for the news summarization task. We focus in this study on zero-shot and few-shot learning settings and we apply a robust evaluation methodology. We highlight the exceptional performance of GPT-3.5-Turbo and GPT-4, which generally dominate due to their advanced capabilities.
  arXiv  Detail & Related papers  (2025-01-30T04:20:16Z)
• DSGram: Dynamic Weighting Sub-Metrics for Grammatical Error Correction in the Era of Large Language Models [39.493913608472404]
  Large language model (LLM)-based Grammatical Error Correction (GEC) models often produce corrections that diverge from provided gold references. This discrepancy undermines the reliability of traditional reference-based evaluation metrics. We propose a novel evaluation framework for GEC models, DSGram, integrating Semantic Coherence, Edit Level, and Fluency, and utilizing a dynamic weighting mechanism.
  arXiv  Detail & Related papers  (2024-12-17T11:54:16Z)
• Language Model Preference Evaluation with Multiple Weak Evaluators [78.53743237977677]
  GED (Preference Graph Ensemble and Denoise) is a novel approach that leverages multiple model-based evaluators to construct preference graphs. In particular, our method consists of two primary stages: aggregating evaluations into a unified graph and applying a denoising process. We provide theoretical guarantees for our framework, demonstrating its efficacy in recovering the ground truth preference structure.
  arXiv  Detail & Related papers  (2024-10-14T01:57:25Z)
• THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models [0.0]
  Hallucination, the generation of factually incorrect content, is a growing challenge in Large Language Models. This paper introduces THaMES, an integrated framework and library addressing this gap. THaMES offers an end-to-end solution for evaluating and mitigating hallucinations in LLMs.
  arXiv  Detail & Related papers  (2024-09-17T16:55:25Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• Calibrating Large Language Models with Sample Consistency [76.23956851098598]
  We explore the potential of deriving confidence from the distribution of multiple randomly sampled model generations, via three measures of consistency. Results show that consistency-based calibration methods outperform existing post-hoc approaches. We offer practical guidance on choosing suitable consistency metrics for calibration, tailored to the characteristics of various LMs.
  arXiv  Detail & Related papers  (2024-02-21T16:15:20Z)
• On the Calibration of Large Language Models and Alignment [63.605099174744865]
  Confidence calibration serves as a crucial tool for gauging the reliability of deep models. We conduct a systematic examination of the calibration of aligned language models throughout the entire construction process. Our work sheds light on whether popular LLMs are well-calibrated and how the training process influences model calibration.
  arXiv  Detail & Related papers  (2023-11-22T08:57:55Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Calibrating LLM-Based Evaluator [92.17397504834825]
  We propose AutoCalibrate, a multi-stage, gradient-free approach to calibrate and align an LLM-based evaluator toward human preference. Instead of explicitly modeling human preferences, we first implicitly encompass them within a set of human labels. Our experiments on multiple text quality evaluation datasets illustrate a significant improvement in correlation with expert evaluation through calibration.
  arXiv  Detail & Related papers  (2023-09-23T08:46:11Z)
• Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models [23.881825575095945]
  Large pre-trained language models (PLMs) have demonstrated strong performance on natural language understanding (NLU) tasks through fine-tuning. However, fine-tuned models still suffer from overconfident predictions, especially in out-of-domain settings. We demonstrate that the PLMs are well-calibrated on the masked language modeling task with robust predictive confidence under domain shift. We show that preserving pre-trained features can improve the calibration of fine-tuned language models.
  arXiv  Detail & Related papers  (2023-05-30T17:35:31Z)

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.