Mapping the Course for Prompt-based Structured Prediction
Researchers propose combining large language models with combinatorial inference to improve structured prediction and address limitations of autoregressive generation
- Combine large language models with combinatorial inference to improve structured prediction
- Address limitations of autoregressive generation by incorporating combinatorial methods
- Evaluate the performance of the proposed approach on various structured prediction tasks
NLP researchers and AI engineers on a team can benefit from this research as it provides a new approach to improving the performance of large language models in structured prediction tasks, which can be applied to various applications such as text generation and language understanding
💡 Combining large language models with combinatorial inference can improve structured prediction and address limitations of autoregressive generation
💡 Combining LLMs with combinatorial inference to improve structured prediction #LLMs #NLP
Key Takeaways
Researchers propose combining large language models with combinatorial inference to improve structured prediction and address limitations of autoregressive generation
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Abstract:
arXiv:2508.15090v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated strong performance in a wide-range of language tasks without requiring task-specific fine-tuning. However, they remain prone to hallucinations and inconsistencies, and often struggle with complex reasoning, in part due to the limitations of autoregressive generation. We propose to address some of these issues, particularly for structured prediction, by combining LLMs with combinatorial infere
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