Markov Constraint as Large Language Model Surrogate
This paper presents NgramMarkov, a variant of the Markov constraints. It is dedicated to text generation in constraint programming (CP). It involves a set of n-grams (i.e., sequence of n words) associated with probabilities given by a large language model (LLM). It limits the product of the probabilities of the n-gram of a sentence. The propagator of this constraint can be seen as an extension of the ElementaryMarkov constraint propagator, incorporating the LLM distribution instead of the maximum likelihood estimation of n-grams. It uses a gliding threshold, i.e., it rejects n-grams whose local probabilities are too low, to guarantee balanced solutions. It can also be combined with a "look-ahead" approach to remove n-grams that are very unlikely to lead to acceptable sentences for a fixed-length horizon. This idea is based on the MDDMarkovProcess constraint propagator, but without explicitly using an MDD (Multi-Valued Decision Diagram). The experimental results show that the generated text is valued in a similar way to the LLM perplexity function. Using this new constraint dramatically reduces the number of candidate sentences produced, improves computation times, and allows larger corpora or smaller n-grams to be used. A real-world problem has been solved for the first time using 4-grams instead of 5-grams.
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage ModellingLarge Language ModelmodelSentenceText GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models
Bayesian inference typically relies on a large number of model evaluations to estimate posterior distributions. Established methods like Markov Chain Monte Carlo (MCMC) and Amortized Bayesian Inference (ABI) can become c…
Bayesian InferenceNeural network surrogates with uncertainty quantification for inverse problems in partial differential equations
Inverse problems for differential equations arise throughout science and engineering, where one seeks to infer unknown model parameters from noisy or incomplete observations. Traditional numerical methods for these probl…
Bayesian InferenceScalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
We investigate how fairness relaxations scale to flexible classifiers like deep neural networks for images and text. We analyze an easy-to-use and robust way of imposing fairness constraints when training, and through th…
FairnessScalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
We investigate how fairness relaxations scale to flexible classifiers like deep neural networks for images and text. We analyze an easy-to-use and robust way of imposing fairness constraints when training, and through th…
FairnessEnhancing Inverse Problem Solutions with Accurate Surrogate Simulators and Promising Candidates
Deep-learning inverse techniques have attracted significant attention in recent years. Among them, the neural adjoint (NA) method, which employs a neural network surrogate simulator, has demonstrated impressive performan…