Summary of Grammar-aligned Decoding, by Kanghee Park et al.
Grammar-Aligned Decoding
by Kanghee Park, Jiayu Wang, Taylor Berg-Kirkpatrick, Nadia Polikarpova, Loris D’Antoni
First submitted to arxiv on: 31 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the limitations of Large Language Models (LLMs) in generating structured outputs like program code or mathematical formulas. Constrained decoding approaches, such as grammar-constrained decoding (GCD), aim to restrict the LLM’s output tokens to ensure grammatical correctness. However, this can lead to distorted output distributions, resulting in low-quality outputs that are grammatically correct but not proportional to the original LLM distribution. The authors propose adaptive sampling with approximate expected futures (ASAp), a decoding algorithm that guarantees grammaticality while aligning with the LLM’s conditional probability given the grammar constraint. ASAp uses prior sample outputs to overapproximate future grammaticality, producing outputs with higher likelihood according to the LLM’s distribution compared to existing GCD techniques. The authors evaluate their approach on code generation and structured NLP tasks, demonstrating improved results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about how big language models struggle to create organized text, like computer code or math problems, that follow specific rules. To fix this problem, the authors suggest using “grammar-constrained decoding”, which helps ensure the generated text follows these rules. However, they found that this approach can actually make things worse by producing low-quality text that is technically correct but not very good. The researchers then propose a new method called “adaptive sampling with approximate expected futures” (ASAp) to solve this problem. This algorithm uses previous attempts to generate text to predict whether the generated text will be grammatically correct or not, and it works better than existing methods in generating high-quality text that follows specific rules. |
Keywords
» Artificial intelligence » Likelihood » Nlp » Probability
