arxiv_ml 90% Match Research Paper NLP Researchers,Machine Learning Engineers,AI Safety Researchers 1 week ago

Incremental Sequence Classification with Temporal Consistency

large-language-models › evaluation

📄 Abstract

Abstract: We address the problem of incremental sequence classification, where predictions are updated as new elements in the sequence are revealed. Drawing on temporal-difference learning from reinforcement learning, we identify a temporal-consistency condition that successive predictions should satisfy. We leverage this condition to develop a novel loss function for training incremental sequence classifiers. Through a concrete example, we demonstrate that optimizing this loss can offer substantial gains in data efficiency. We apply our method to text classification tasks and show that it improves predictive accuracy over competing approaches on several benchmark datasets. We further evaluate our approach on the task of verifying large language model generations for correctness in grade-school math problems. Our results show that models trained with our method are better able to distinguish promising generations from unpromising ones after observing only a few tokens.

Authors (8)

Lucas Maystre

Gabriel Barello

Tudor Berariu

Aleix Cambray

Rares Dolga

Alvaro Ortega Gonzalez

+2 more

Submitted

May 22, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

This paper introduces a novel approach for incremental sequence classification by leveraging temporal-consistency conditions inspired by reinforcement learning. A new loss function is developed that optimizes this condition, leading to significant gains in data efficiency for training sequence classifiers. The method is shown to improve accuracy on text classification benchmarks and effectively evaluate large language model generations for correctness in math problems.

Business Value

Enables more efficient and accurate real-time analysis of sequential data, such as text streams or user interactions. It can also improve the reliability of LLM outputs by providing a more robust evaluation mechanism.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

High. The method focuses on improving training efficiency and evaluation, making it readily applicable to existing NLP and LLM pipelines. Requires integration of the novel loss function.

Limitations Addressed

Inefficiency in training incremental sequence classifiers,Difficulty in evaluating LLM generations incrementally,Need for improved data efficiency in sequence modeling

Performance Gains

substantial gains in data efficiency,improves predictive accuracy over competing approaches

Technical Tags

incremental sequence classificationtemporal consistencytemporal-difference learningreinforcement learningloss functiondata efficiencytext classificationlarge language modelsgrade-school math problemspredictive accuracy

Research Topics

Sequence ModelingOnline LearningReinforcement LearningNatural Language ProcessingModel Evaluation

Methods & Architectures

Temporal-difference learningTemporal-consistency conditionNovel loss functionOptimization of loss function Sequence ClassifiersLarge Language Models

Applications & Tasks

Natural Language Processing Education Technology AI Safety Incremental predictionSequence classificationEvaluating LLM generations Incremental Sequence ClassificationText ClassificationLLM Generation Verification

Datasets & Benchmarks

Datasets

benchmark datasets

predictive accuracydata efficiency

Related Fields

Online LearningSequential Data AnalysisNatural Language ProcessingReinforcement Learning

Keywords

Academic Context

#Sequence Modeling#Online Learning#Reinforcement Learning#Natural Language Processing#Model Evaluation

Commercial Potential

Potential Products

Real-time text analysis toolsLLM evaluation platformsAutomated content moderation systems

Target Industries

TechnologyMediaEducationCustomer Service

Use Case Examples

Real-time sentiment analysis of social media feedsVerifying the correctness of AI-generated summariesMonitoring and filtering streaming data for specific events

Competitive Edge

Offers a more data-efficient and accurate approach to incremental sequence classification and LLM evaluation compared to existing methods.

Market Opportunity

Growing demand for efficient NLP and LLM evaluation tools.

Revenue Models

Licensing of the algorithmintegration into SaaS platforms.

Resource Requirements

Compute Needs

Moderate (depends on model size and dataset)

Data Requirements

Sequential data for classification tasks

Deployment Constraints

Requires integration into existing sequence processing pipelines.

Scalability

Scales with the length of sequences and the complexity of the classification task.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years

Patent Potential

Moderate

View Full Paper Back to Papers