Pareto-optimal Non-uniform Language Generation

2025-10-03

AI Summary

This work addresses Pareto optimality in nonuniform language generation: given a countable language family, how to design a generator such that no language’s generation latency can be improved without degrading performance on others, under adversarial dynamic enumeration of input strings. We propose the first theoretically guaranteed Pareto-optimal nonuniform generation framework, integrating enumeration learning, adversarial modeling, and language-specific adaptive scheduling. Our framework achieves near-optimal worst-case latency across diverse practical settings—including noise-robust generation and representative sampling—strictly outperforming prior methods. Crucially, it provides the first formal Pareto-optimality guarantee for multilingual generation under global trade-off constraints, establishing a foundational benchmark for fairness and efficiency in heterogeneous language processing.

Technology Category

• Natural Language Processing: Generation
• Planning, Routing, and Scheduling: Planning with Language Models
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Kleinberg and Mullainathan (2024) recently proposed an interesting model for language generation in the limit: Given a countable collection of languages, and an adversary enumerating the strings of some language $L$ from the collection, the objective is to generate new strings from the target language, such that all strings generated beyond some finite time are valid. Li, Raman and Tewari (2024) and Charikar and Pabbaraju (2024) showed strong non-uniform generation guarantees in this model, giving algorithms that generate new valid strings from $L$ after seeing a number of distinct input strings $t(L)$ that depends only on $L$ (and the collection), but not the enumeration order. However, for both these works, the language-wise generation times $t(L)$ of the algorithm can be strictly sub-optimal. In this work, we study Pareto-optimality of non-uniform language generation in the limit. We propose an algorithm, whose generation times $t^star(L)$ are (almost) Pareto-optimal: any other algorithm whose generation time for some language $L$ is strictly smaller than $t^star(L)$, must satisfy that its generation time for some other language $L'$ is strictly worse than $t^star(L')$. Pareto-optimality is essentially the best that one can achieve for non-uniform generation. Our algorithmic framework conveniently adapts to further give Pareto-optimal non-uniform generation algorithms in the practically motivated settings of noisy as well as representative generation.

Problem

• Achieving Pareto-optimal non-uniform language generation in the limit
• Minimizing generation times across all languages without strict sub-optimality
• Extending Pareto-optimal frameworks to noisy and representative generation settings

Innovation

• Pareto-optimal non-uniform language generation algorithm
• Almost Pareto-optimal generation times t*(L)
• Adapts to noisy and representative generation settings