arXiv:2606.12003v1 Announce Type: new Abstract: Self-consistency improves LLM reasoning by sampling multiple outputs and selecting the most consistent answer, but existing formulations largely rely on exact matching and therefore remain limited to tasks with categorical outputs. In this work, we study self-consistency in open-ended generation tasks such as code synthesis and text summarization. We hypothesize that consistency can be understood as a geometric property of the generation space, where semantically compatible generations concentrate in similar regions of representation space. To st
This research builds on existing self-consistency methods for LLMs, indicating a continued push to enhance their reasoning capabilities, particularly for open-ended tasks as they become more ubiquitous.
Improving self-consistency for open-ended generation tasks like code synthesis and summarization can significantly enhance the reliability and applicability of large language models across diverse industries.
The shift from exact matching to representation space agreement for self-consistency allows LLMs to evaluate semantic compatibility, opening doors for more nuanced and accurate outputs in complex generative tasks.
- · AI model developers
- · Software development industry
- · Content generation industry
- · AI research institutions
- · Manual code reviewers
- · Basic summarization services
More robust and reliable AI-generated content and code.
Accelerated development cycles and reduced human oversight in creative and technical fields.
Increased automation of complex white-collar tasks, potentially redefining roles in programming and content creation.
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Read at arXiv cs.CL