arXiv:2601.21500v2 Announce Type: replace Abstract: In many applications of LLMs, natural language responses often have an underlying structure such as representing discrete labels, numerical values, or graphs. Yet, existing decoding and uncertainty estimation methods operate only in language space and largely disregard structural information. We address this by modeling LLM outputs directly in a task-dependent latent structure. By equipping this structure with a dissimilarity measure, we can compute Bayes-optimal responses. These are not selected from sampled generations but are newly synthes
Rapid advancements in LLM capabilities are increasingly bottlenecked by the accuracy and reliability of their outputs, particularly in structured applications.
This development offers a pathway to significantly enhance the reliability and application specificity of large language models, making them more suitable for high-stakes enterprise use cases.
LLMs can now generate and validate outputs based on underlying structural information, moving beyond mere language space and enabling more precise, task-aware responses.
- · AI developers
- · Enterprise AI adopters
- · Automation software vendors
- · Data scientists
- · LLM applications without structural validation
- · Generative AI solutions lacking precision
Improved accuracy and reduced uncertainty in LLM-generated content for specific tasks.
Accelerated adoption of LLMs in fields requiring structured outputs, such as data analysis, code generation, and scientific research.
Enhanced trust in AI systems, leading to broader integration into critical decision-making processes.
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Read at arXiv cs.LG