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аналитика - Artificial Intelligence, Natural Language Processing
Leveraging Large Language Models' Mistakes to Enhance Reasoning Capabilities
Large language models can benefit from learning from their previous mistakes to improve their reasoning capabilities.
Evaluating Reasoning Capabilities of Language Models through Symbolic Verification
Language models can be prompted with logical demonstrations to generate plausible explanations for reasoning tasks over knowledge bases, and constraining outputs and ensuring intermediate reasoning correctness are important for improving reasoning performance.
Measuring and Mitigating Political Biases in Large Language Models
This study introduces a comprehensive framework to measure and analyze political biases inherent in large language models (LLMs), going beyond traditional political orientation-level analyses. The framework examines both the content and style of LLM-generated content to provide fine-grained, topic-specific insights into political biases.
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