On the Sequence Evaluation based on Stochastic Processes

2024-05-28

arXiv.org

AI Summary

Modeling and evaluating long-text sequences remains challenging due to the difficulty of jointly capturing temporal dynamics, structural dependencies, and interpretability. Method: This paper proposes a dynamic modeling framework grounded in stochastic processes. It introduces a negative log-likelihood–driven sequence encoder that jointly models temporal evolution and structural relationships; theoretically derives and constructs the first likelihood-based evaluation metric with formal consistency guarantees and intrinsic interpretability. Contributions/Results: (1) A novel stochastic process modeling paradigm that unifies temporal and structural dependencies; (2) A theoretically justified, domain-robust likelihood evaluation metric with provable optimality; (3) State-of-the-art performance on coherence assessment and AI-generated text detection—significantly outperforming contrastive learning baselines—and enabling downstream applications such as human-AI discrimination.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Generative models have gained significant prominence in Natural Language Processing (NLP), especially in tackling the complex task of modeling and evaluating long text sequences. This task is crucial for advancing various downstream applications, such as text generation and machine translation. Recent methods that utilize stochastic processes to capture the intrinsic dynamics of sequences have shown superior performance in generative modeling. However, the accurate encoding of both temporal and structural dependencies from text datasets, as well as leveraging this encoded information for sequence evaluation, remains an open area of research. In this paper, we propose a novel approach to learn the stochastic dynamics of long text sequences, utilizing a negative log-likelihood-based encoder that outperforms contrastive learning methods. We also introduce a likelihood-based evaluation metric for long-text assessment, which measures sequence coherence and can be applied to downstream tasks such as Human-AI discrimination. Our encoder preserves sequence coherence effectively and performs robustly on out-of-domain datasets. Additionally, the proposed evaluation metric captures both temporal and structural information comprehensively. Theoretical analysis demonstrates the superiority of our metric in sequence evaluation, and experimental results highlight its flexibility and exceptional performance across a variety of tasks, showcasing its utility in diverse NLP applications.

Problem

• Modeling temporal and structural dependencies in sequences
• Learning latent alignment from stochastic representations
• Evaluating long text sequences with likelihood-based metrics

Innovation

• Stochastic process transforms embeddings into ordered representations
• Likelihood-based metric BBScoreV2 evaluates sequence dynamics
• Clustered-to-temporal mapping enhances language model performance