Jul 3, 2023 · Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models. Authors:Jinhao Duan, Hao ...

Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models. In Proceedings of the 62nd Annual Meeting ...

[ACL 2024] Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models - jinhaoduan/SAR.

Aug 11, 2024 · To correct this, we propose Shifting. Attention to more Relevant (SAR) components at both token- and sentence-levels for better. UQ. We conduct ...

May 28, 2024 · To correct this, we propose Shifting Attention to more Relevant (SAR) components at both token- and sentence-levels for better UQ. We conduct ...

This paper studies uncertainty quantification in long-form generation in language models. While there are many methods for this now, one common method is ...

Sep 23, 2024 · Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models. January 2024. DOI ...

Aug 10, 2024 · Alex Zavalny's Post · Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models · More ...

This research is derived from the heuristic facts that tokens are created unequally in reflecting the meaning of generations by auto-regressive LLMs, ...

To tackle these biases posed by generative inequalities, we propose to jointly Shifting Attention to more Relevant (SAR) components from both the token level ...