REVIEW 6 cited by
Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significant costs and time investment. Researchers have recently explored using large language models (LLMs) to generate synthetic datasets as an alternative approach. However, the effectiveness of the LLM-generated synthetic data in supporting model training is inconsistent across different classification tasks. To better understand factors that moderate the effectiveness of the LLM-generated synthetic data, in this study, we look into how the performance of models trained on these synthetic data may vary with the subjectivity of classification. Our results indicate that subjectivity, at both the task level and instance level, is negatively associated with the performance of the model trained on synthetic data. We conclude by discussing the implications of our work on the potential and limitations of leveraging LLM for synthetic data generation.
Forward citations
Cited by 6 Pith papers
-
Optimizing the Preconditioner: A Black-box Online-to-Nonconvex Conversion with Static Regret Minimization Oracles
An OCO algorithm with only O(√T) static regret, pluggable as a preconditioner selector, recovers the classical O(1/√T) stationarity rate on smooth stochastic nonconvex problems and the O(T^{-2/7}) rate on nonsmooth ones.
-
Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation
LLM-based tabular generators reproduce seed rows often enough that membership-inference attacks succeed more against them than against GAN, VAE, or diffusion baselines.
-
What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning
Moderately diverse LLM-generated data can improve fine-tuned model performance in low-data settings when distribution shift is minimal, while high diversity or large distribution shift hurts.
-
Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement
SiDyP improves classifiers trained on LLM-generated noisy labels by retrieving likely true labels from embedding-space neighbors and iteratively refining them with a simplex diffusion model, reporting average gains of...
-
Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration
The paper proposes a systematic classification and two roadmaps for using foundation models (LLMs and wireless foundation models) to design Synesthesia of Machines systems for 6G, with preliminary case-study evidence ...
-
Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model
A two-stage pipeline encodes daily home-activity text with MiniLM, clusters the embeddings, and applies PageRank to derive per-patient behavior vectors.
Discussion (0). Sign in to comment.