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Found 4 papers, 3 papers with code
Make Your Decision Convincing! A Unified Two-Stage Framework: Self-Attribution and Decision-Making
Through extensive experiments on five reasoning datasets from the ERASER benchmark, we demonstrate that our framework not only establishes a more reliable link between the generated rationale and model decision but also achieves competitive results in task performance and the quality of rationale.
Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese
To address this challenge, we propose knowledge-tuning, which leverages structured medical knowledge bases for the LLMs to grasp domain knowledge efficiently and facilitate reliable response generation.
Manifold-based Verbalizer Space Re-embedding for Tuning-free Prompt-based Classification
Experimental results indicate that even without tuning any parameters, our LLE-INC is on par with automated verbalizers with parameter tuning.
Don't Ignore Dual Logic Ability of LLMs while Privatizing: A Data-Intensive Analysis in Medical Domain
We conduct several experiments to analyze the dual logic ability of LLMs by examining the consistency of the stance in responses to paired questions about the same fact.
