Query language for blending SQL and LLMs across structured + unstructured data, with type constraints.
Reproduction Package for the paper "Type-Constrained Code Generation with Language Models" [PLDI 2025]
Constrained Decoding of Diffusion LLMs with Context-Free Grammars.
Code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2021 and "Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation" arXiv 2020
For our ICRA 2025 paper "SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models" by Yi Wu, Zikang Xiong, Yiran Hu, Shreyash Iyengar, Nan Jiang, Aniket Bera, Lin Tan, and Suresh Jagannathan.
A guide to structured generation using constrained decoding
Speculative grammar backtracking algorithm for LLM decoding conforming to some lark context-free grammar (CFG)
Context-Free Grammar-guided Generation of FHIR Resources Using Large Language Models
MAZE is an adaptive constraint-based code generation system that combines Large Language Models (LLMs) with formal constraint enforcement to produce more accurate and contextually appropriate code.
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