Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification
- URL: http://arxiv.org/abs/2406.17790v1
- Date: Tue, 28 May 2024 12:47:43 GMT
- Title: Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification
- Authors: Areeg Fahad Rasheed, M. Zarkoosh,
- Abstract summary: We employ the chi-square test to identify high-quality samples and compare the results with those obtained using low-quality samples.
Our findings demonstrate that utilizing high-quality samples leads to improved performance with respect to all evaluated metrics.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Within few-shot learning, in-context learning (ICL) has become a potential method for leveraging contextual information to improve model performance on small amounts of data or in resource-constrained environments where training models on large datasets is prohibitive. However, the quality of the selected sample in a few shots severely limits the usefulness of ICL. The primary goal of this paper is to enhance the performance of evaluation metrics for in-context learning by selecting high-quality samples in few-shot learning scenarios. We employ the chi-square test to identify high-quality samples and compare the results with those obtained using low-quality samples. Our findings demonstrate that utilizing high-quality samples leads to improved performance with respect to all evaluated metrics.
Related papers
- Clear Preferences Leave Traces: Reference Model-Guided Sampling for Preference Learning [59.11519451499754]
Direct Preference Optimization (DPO) has emerged as a de-facto approach for aligning language models with human preferences.
Recent work has shown DPO's effectiveness relies on training data quality.
We discover that reference model probability space naturally detects high-quality training samples.
arXiv Detail & Related papers (2025-01-25T07:21:50Z) - Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs [11.24476329991465]
Training large language models (LLMs) for external tool usage is a rapidly expanding field.
The absence of systematic data quality checks poses complications for properly training and testing models.
We propose two approaches for assessing the reliability of data for training LLMs to use external tools.
arXiv Detail & Related papers (2024-09-24T17:20:02Z) - Auto Cherry-Picker: Learning from High-quality Generative Data Driven by Language [41.40908753726324]
Diffusion models can generate realistic and diverse images, potentially facilitating data availability for data-intensive perception tasks.
We present textbfAuto textbfCherry-textbfPicker (ACP), a novel framework that generates high-quality cross-modality training samples.
arXiv Detail & Related papers (2024-06-28T17:53:18Z) - Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios.
We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples.
Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
arXiv Detail & Related papers (2024-06-07T11:37:45Z) - Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral.
This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
arXiv Detail & Related papers (2024-02-16T13:53:26Z) - QuRating: Selecting High-Quality Data for Training Language Models [64.83332850645074]
We introduce QuRating, a method for selecting pre-training data that can capture human intuitions about data quality.
In this paper, we investigate four qualities - writing style, required expertise, facts & trivia, and educational value.
We train a Qur model to learn scalar ratings from pairwise judgments, and use it to annotate a 260B training corpus with quality ratings for each of the four criteria.
arXiv Detail & Related papers (2024-02-15T06:36:07Z) - One-Shot Learning as Instruction Data Prospector for Large Language Models [108.81681547472138]
textscNuggets uses one-shot learning to select high-quality instruction data from extensive datasets.
We show that instruction tuning with the top 1% of examples curated by textscNuggets substantially outperforms conventional methods employing the entire dataset.
arXiv Detail & Related papers (2023-12-16T03:33:12Z) - A Novel Metric for Measuring Data Quality in Classification Applications
(extended version) [0.0]
We introduce and explain a novel metric to measure data quality.
This metric is based on the correlated evolution between the classification performance and the deterioration of data.
We provide an interpretation of each criterion and examples of assessment levels.
arXiv Detail & Related papers (2023-12-13T11:20:09Z) - Test Time Adaptation for Blind Image Quality Assessment [20.50795362928567]
We introduce two novel quality-relevant auxiliary tasks at the batch and sample levels to enable TTA for blind IQA.
Our experiments reveal that even using a small batch of images from the test distribution helps achieve significant improvement in performance.
arXiv Detail & Related papers (2023-07-27T09:43:06Z) - A Quality Aware Sample-to-Sample Comparison for Face Recognition [13.96448286983864]
This work integrates a quality-aware learning process at the sample level into the classification training paradigm (QAFace)
Our method adaptively finds and assigns more attention to the recognizable low-quality samples in the training datasets.
arXiv Detail & Related papers (2023-06-06T20:28:04Z) - Temporal Output Discrepancy for Loss Estimation-based Active Learning [65.93767110342502]
We present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss.
Our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks.
arXiv Detail & Related papers (2022-12-20T19:29:37Z)
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.