Agnostic Language Identification and Generation

2026-01-30

AI Summary

This work addresses the limitations of existing language recognition and generation methods, which typically rely on strong realizability assumptions requiring input data to conform to a predefined language distribution, thereby struggling in open-world settings. For the first time, we study this problem under a fully agnostic setting, abandoning all assumptions about the underlying data distribution and proposing a novel objective function that enables a universal learning framework. Drawing upon statistical learning theory and information theory, we provide a new theoretical characterization of both language modeling and recognition tasks, and derive nearly tight statistical convergence rates. Our analysis substantially extends the theoretical applicability boundaries of current approaches across both task categories.

Technology Category

• Machine Learning: Learning Theory
• Natural Language Processing: Learning & Optimization for NLP
• Computer Vision: Language and Vision

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Recent works on language identification and generation have established tight statistical rates at which these tasks can be achieved. These works typically operate under a strong realizability assumption: that the input data is drawn from an unknown distribution necessarily supported on some language in a given collection. In this work, we relax this assumption of realizability entirely, and impose no restrictions on the distribution of the input data. We propose objectives to study both language identification and generation in this more general"agnostic"setup. Across both problems, we obtain novel interesting characterizations and nearly tight rates.

Problem

• Agnostic
• Language Identification
• Language Generation
• Realizability Assumption
• Distribution-Free

Innovation

• agnostic learning
• language identification
• language generation
• realizability assumption
• statistical rates