✨Reasoning with Confidence: Efficient Verification of LLM Reasoning Steps via Uncertainty Heads
📝 Summary:
This paper introduces lightweight UHeads, transformer-based uncertainty quantification heads, to efficiently verify LLM reasoning steps. UHeads estimate uncertainty from the LLM's internal states, outperforming larger verification models while being scalable and effective across various domains.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06209
• PDF: https://arxiv.org/pdf/2511.06209
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #AI #MachineLearning #UncertaintyQuantification #ModelVerification
📝 Summary:
This paper introduces lightweight UHeads, transformer-based uncertainty quantification heads, to efficiently verify LLM reasoning steps. UHeads estimate uncertainty from the LLM's internal states, outperforming larger verification models while being scalable and effective across various domains.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06209
• PDF: https://arxiv.org/pdf/2511.06209
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #AI #MachineLearning #UncertaintyQuantification #ModelVerification