在大型语言模型中增强类人响应

Enhancing Human-Like Responses in Large Language Models

January 9, 2025

作者: Ethem Yağız Çalık, Talha Rüzgar Akkuş

cs.AI

摘要

本文探讨了如何使大型语言模型（LLMs）更加接近人类的进展。我们关注增强人工智能系统中自然语言理解、对话连贯性和情感智能的技术。研究评估了各种方法，包括利用多样化数据集进行微调、融入心理学原理，以及设计更好模拟人类推理模式的模型。我们的研究结果表明，这些改进不仅提高了用户交互体验，还为人工智能在不同领域的应用开辟了新的可能性。未来的工作将解决这些人类化特征引入的伦理影响和潜在偏见。

English

This paper explores the advancements in making large language models (LLMs) more human-like. We focus on techniques that enhance natural language understanding, conversational coherence, and emotional intelligence in AI systems. The study evaluates various approaches, including fine-tuning with diverse datasets, incorporating psychological principles, and designing models that better mimic human reasoning patterns. Our findings demonstrate that these enhancements not only improve user interactions but also open new possibilities for AI applications across different domains. Future work will address the ethical implications and potential biases introduced by these human-like attributes.