| Challenge: | Structured outputs are essential for large language models (LLMs) but often deviate from predefined schemas hampering reliable application development. |
| Approach: | They propose a model-agnostic approach that transforms unstructured LLM outputs into precise structured formats. |
| Outcome: | The proposed model-agnostic approach transforms unstructured LLM outputs into precise structured formats. |
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| Challenge: | Large Language Models (LLMs) have advanced capabilities but produce complex structured data. |
| Approach: | They propose a structure-aware fine-tuning method to bolster LLMs' performance by crafting format-specific instructions from the intended outputs. |
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Let Me Speak Freely? A Study On The Impact Of Format Restrictions On Large Language Model Performance. (2024.emnlp-industry)
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| Challenge: | Structured generation is used to extract key output information from large language models (LLMs). |
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A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction (2024.findings-emnlp)
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| Challenge: | Large language models have impressive abilities in generating unstructured natural language . performance inconsistent when tasked with producing text that adheres to structured formats . |
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Learning to Generate Structured Output with Schema Reinforcement Learning (2025.acl-long)
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Yaxi Lu, Haolun Li, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Zhiyuan Liu, Fangming Liu, Maosong Sun
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| Challenge: | General-purpose Large Language Models (LLMs) like GPT-4 have exhibited strong translation abilities. |
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CodecLM: Aligning Language Models with Tailored Synthetic Data (2024.findings-naacl)
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Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
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LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit (2024.emnlp-industry)
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| Challenge: | Large language models (LLMs) often prioritize reasoning over adherence to detailed instructions due to high computational costs and limited parameter access. |
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ULTRABENCH: Benchmarking LLMs under Extreme Fine-grained Text Generation (2025.findings-emnlp)
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| Challenge: | Existing benchmarks evaluate models on only a few attributes, typically fewer than five . a new benchmark evaluates large language models under dense, multi-attribute constraints . |
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Structured Object Language Modeling (SO-LM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising (2024.emnlp-industry)
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| Challenge: | Structured objects generation is a challenging problem for existing Large Language Models. |
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