在大型語言模型中增強類人回應

Enhancing Human-Like Responses in Large Language Models

January 9, 2025

作者: Ethem Yağız Çalık, Talha Rüzgar Akkuş

cs.AI

摘要

本文探討了如何使大型語言模型（LLMs）更具人類化的進展。我們專注於增強自然語言理解、對話連貫性和情感智能的技術。該研究評估了各種方法，包括使用多樣化數據集進行微調、納入心理學原則，以及設計更好地模擬人類推理模式的模型。我們的研究結果表明，這些改進不僅提高了用戶互動，還為不同領域的人工智能應用開辟了新的可能性。未來的工作將解決這些人類化特徵引入的道德問題和潛在偏見。

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.