| Challenge: | Large pretrained language models are successful at generating fluent text but are notoriously hard to controllably sample from. |
| Approach: | They propose a sampling procedure that combines the log-likelihood of the language model with arbitrary constraints in a single energy function and then generates samples in . non-autoregressive manner. |
| Outcome: | The proposed method improves on text generation with soft and hard constraints and keyword-guided generation. |
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| Challenge: | Generating natural language under complex constraints is a principled formulation towards controllable text generation. |
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| Challenge: | Large language models (LLMs) improve with more training data, but practical limitations on data collection constrain further scaling. |
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| Challenge: | Large language models (LM) based on transformers generate plausible long texts . a discriminator-guided approach allows to apply constraints more finely and dynamically. |
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| Challenge: | a fundamental question in psycholinguistics is how comprehenders form interpretations of utterances that they hear or see. |
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| Challenge: | Existing guidance methods for text generation are prone to decoding errors and degrade performance. |
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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
| Challenge: | Recent advances in large language models have facilitated the development of intelligent applications like automatic web search (Qin et al., 2023) Several methods exist for generating JSON strings from LLMs, including Prompting but often miss certain schemas. |
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Continuous Context Sampling Allows Extending Diversity Boundaries of Large Language Models (2026.acl-srw)
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| Challenge: | Large language models exhibit a persistent limitation: repeated generations from the same prompt tend to be semantically similar. |
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Scaling LLM Inference Efficiently with Optimized Sample Compute Allocation (2025.naacl-long)
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A Plug-and-Play Method for Controlled Text Generation (2021.findings-emnlp)
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LETI: Learning to Generate from Textual Interactions (2024.findings-naacl)
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| Challenge: | Existing techniques fine-tune on input-output pairs or with numerical rewards that gauge the output quality are not effective. |
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