Summary of Markov Constraint As Large Language Model Surrogate, by Alexandre Bonlarron et al.
Markov Constraint as Large Language Model Surrogate
by Alexandre Bonlarron, Jean-Charles Régin
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: NgramMarkov is a novel constraint-based approach for text generation in constraint programming (CP). Building upon the Markov constraints, this variant leverages a large language model (LLM) to assign probabilities to n-grams (sequences of n words). The propagator limits the product of these probabilities, ensuring balanced solutions. A gliding threshold is introduced to reject low-probability n-grams, while a “look-ahead” approach removes unlikely candidates for fixed-length horizons. This constraint can be combined with other methods, such as MDDMarkovProcess, without explicitly using Multi-Valued Decision Diagrams (MDD). Experimental results demonstrate that NgramMarkov produces valued text similar to the LLM perplexity function. By reducing candidate sentences and computation times, this approach enables the use of larger corpora or smaller n-grams. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper introduces a new way to generate text using computer programs. It’s called NgramMarkov. The idea is to take words and assign them probabilities based on how likely they are to appear together in sentences. The program then uses these probabilities to create new sentences that sound like real language. The authors tested their approach with a large language model and found it worked well, reducing the number of possible sentences and making computation faster. This means we can use more words or smaller groups of words to generate text. |
Keywords
» Artificial intelligence » Large language model » Perplexity » Probability » Text generation
