# SLOT: Structuring the Output of Large Language Models

arXiv: https://arxiv.org/abs/2505.04016 · HTML: https://mlsanity.com/paper/2505.04016/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 17 of 17)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [StructGPT: A General Framework for Large Language Model to Reason over Structured Data](https://mlsanity.com/paper/2305.09645/) (2023)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation](https://mlsanity.com/paper/2403.06988/) (2024)
- [Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models](https://mlsanity.com/paper/2408.02442/) (2024)
- [FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability](https://mlsanity.com/paper/2402.18667/) (2024)
- [JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models](https://mlsanity.com/paper/2501.10868/) (2025)
- ["We Need Structured Output": Towards User-centered Constraints on Large Language Model Output](https://mlsanity.com/paper/2404.07362/) (2024)
- [Learning to Generate Structured Output with Schema Reinforcement Learning](https://mlsanity.com/paper/2502.18878/) (2025)
- [Think Inside the JSON: Reinforcement Strategy for Strict LLM Schema Adherence](https://mlsanity.com/paper/2502.14905/) (2025)
- [Sketch: A Toolkit for Streamlining LLM Operations](https://mlsanity.com/paper/2409.03346/) (2024)

## Built on by (most cited first, 18 of 18)

- [Unveiling the Potential of Diffusion Large Language Model in Controllable Generation](https://mlsanity.com/paper/2507.04504/) (2025)
- [STED and Consistency Scoring: A Framework for Evaluating LLM Structured Output Reliability](https://mlsanity.com/paper/2512.23712/) (2025)
- [Natural Language Tools: A Natural Language Approach to Tool Calling In Large Language Agents](https://mlsanity.com/paper/2510.14453/) (2025)
- [Automatic Prompt Optimization for Dataset-Level Feature Discovery](https://mlsanity.com/paper/2601.13922/) (2026)
- [Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents](https://mlsanity.com/paper/2604.00555/) (2026)
- [KM-GPT: An Automated Pipeline for Reconstructing Individual Patient Data from Kaplan-Meier Plots](https://mlsanity.com/paper/2509.18141/) (2025)
- [You May Speak Freely: Improving the Fine-Grained Visual Recognition Capabilities of Multimodal Large Language Models with Answer Extraction](https://mlsanity.com/paper/2510.14885/) (2025)
- [FMBench: Adaptive Large Language Model Output Formatting](https://mlsanity.com/paper/2602.06384/) (2026)
- [ADEMA: A Knowledge-State Orchestration Architecture for Long-Horizon Knowledge Synthesis with LLMAgents](https://mlsanity.com/paper/2604.25849/) (2026)
- [The Architecture of Errors: From Universal Impossibility to Patch-Local LLM Reliability](https://mlsanity.com/paper/2605.30628/) (2026)
- [The Unreliable Progress Bar: Can LLM Agents Reliably Report Task Progress Throughout Execution?](https://mlsanity.com/paper/2609.08589/) (2026)
- [CienaLLM: Generative Climate-Impact Extraction from News Articles with Autoregressive LLMs](https://mlsanity.com/paper/2512.19305/) (2025)
- [ScrapeGraphAI-100k: Dataset for Schema-Constrained LLM Generation](https://mlsanity.com/paper/2602.15189/) (2026)
- [Runtime Burden Allocation for Structured LLM Routing in Agentic Expert Systems: A Full-Factorial Cross-Backend Methodology](https://mlsanity.com/paper/2604.01235/) (2026)
- [BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection](https://mlsanity.com/paper/2604.10389/) (2026)
- [A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2607.29530/) (2026)
- [State-Grounded Conditioning: Wrapping User-Facing LLM Agents Where Direction Depends on Live State](https://mlsanity.com/paper/2609.27606/) (2026)
- [ContractRL: Shielded Group-Relative Policy Optimization for Auditable Tool-Call Repair](https://mlsanity.com/paper/2610.00328/) (2026)

## Abstract (arXiv)

Structured outputs are essential for large language models (LLMs) in critical applications like agents and information extraction. Despite their capabilities, LLMs often generate outputs that deviate from predefined schemas, significantly hampering reliable application development. We present SLOT (Structured LLM Output Transformer), a model-agnostic approach that transforms unstructured LLM outputs into precise structured formats. While existing solutions predominantly rely on constrained decoding techniques or are tightly coupled with specific models, SLOT employs a fine-tuned lightweight language model as a post-processing layer, achieving flexibility across various LLMs and schema specifications. We introduce a systematic pipeline for data curation and synthesis alongside a formal evaluation methodology that quantifies both schema accuracy and content fidelity. Our results demonstrate that fine-tuned Mistral-7B model with constrained decoding achieves near perfect schema accuracy (99.5%) and content similarity (94.0%), outperforming Claude-3.5-Sonnet by substantial margins (+25 and +20 percentage points, respectively). Notably, even compact models like Llama-3.2-1B can match or exceed the structured output capabilities of much larger proprietary models when equipped with SLOT, enabling reliable structured generation in resource-constrained environments.