# Verifiable Format Control for Large Language Model Generations

arXiv: https://arxiv.org/abs/2502.04498 · HTML: https://mlsanity.com/paper/2502.04498/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://mlsanity.com/paper/2405.14734/) (2024)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023)
- [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://mlsanity.com/paper/2104.08773/) (2021)
- [RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment](https://mlsanity.com/paper/2304.06767/) (2023)
- [Large Language Models Can Self-Improve](https://mlsanity.com/paper/2210.11610/) (2022)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [ORPO: Monolithic Preference Optimization without Reference Model](https://mlsanity.com/paper/2403.07691/) (2024)
- [PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization](https://mlsanity.com/paper/2306.05087/) (2023)
- [FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability](https://mlsanity.com/paper/2402.18667/) (2024)
- [Preference as Reward, Maximum Preference Optimization with Importance Sampling](https://mlsanity.com/paper/2312.16430/) (2023)

## Built on by (most cited first, 17 of 17)

- [Generalizing Verifiable Instruction Following](https://mlsanity.com/paper/2507.02833/) (2025)
- [Learning to Generate Structured Output with Schema Reinforcement Learning](https://mlsanity.com/paper/2502.18878/) (2025)
- [Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models](https://mlsanity.com/paper/2506.01413/) (2025)
- [Revisiting the Reliability of Language Models in Instruction-Following](https://mlsanity.com/paper/2512.14754/) (2025)
- [AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance](https://mlsanity.com/paper/2510.01474/) (2025)
- [STED and Consistency Scoring: A Framework for Evaluating LLM Structured Output Reliability](https://mlsanity.com/paper/2512.23712/) (2025)
- [FeedEval: Pedagogically Aligned Evaluation of LLM-Generated Essay Feedback](https://mlsanity.com/paper/2601.04574/) (2026)
- [Large-Scale Constraint Generation -- Can LLMs Parse Hundreds of Constraints?](https://mlsanity.com/paper/2509.24090/) (2025)
- [DLM-SWAI: Steering Diffusion Language Models Before They Unmask](https://mlsanity.com/paper/2605.29626/) (2026)
- [Precision over Diversity: High-Precision Reward Generalizes to Robust Instruction Following](https://mlsanity.com/paper/2601.04954/) (2026)
- [PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation](https://mlsanity.com/paper/2601.09771/) (2026)
- [HoWToBench: Holistic Evaluation for LLM's Capability in Human-level Writing using Tree of Writing](https://mlsanity.com/paper/2604.19071/) (2026)
- [When Correct Isn't Usable: Improving Structured Output Reliability in Small Language Models](https://mlsanity.com/paper/2605.02363/) (2026)
- [TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles](https://mlsanity.com/paper/2606.07520/) (2026)
- [Translating Natural Language to Strategic Temporal Specifications via LLMs](https://mlsanity.com/paper/2606.30441/) (2026)
- [ScopeIF: Improving Scope-Aware Precise Instruction-Following in Large Language Models via Graded Reward Modeling](https://mlsanity.com/paper/2609.32189/) (2026)
- [ReFormBench: Benchmarking the Stability of Response-Conditioned Format Correction in Large Language Models](https://mlsanity.com/paper/2610.19693/) (2026)

## Abstract (arXiv)

Recent Large Language Models (LLMs) have demonstrated satisfying general instruction following ability. However, small LLMs with about 7B parameters still struggle fine-grained format following (e.g., JSON format), which seriously hinder the advancements of their applications. Most existing methods focus on benchmarking general instruction following while overlook how to improve the specific format following ability for small LLMs. Besides, these methods often rely on evaluations based on advanced LLMs (e.g., GPT-4), which can introduce the intrinsic bias of LLMs and be costly due to the API calls. In this paper, we first curate a fully verifiable format following dataset VFF. In contrast to existing works often adopting external LLMs for instruction-following validations, every sample of VFF can be easily validated with a Python function. Further, we propose to leverage this verifiable feature to synthesize massive data for progressively training small LLMs, in order to improve their format following abilities. Experimental results highlight the prevalent limitations in the format following capabilities of 7B level open-source LLMs and demonstrate the effectiveness of our method in enhancing this essential ability.