Enhancing Human-Like Responses in Large Language Models
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.
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Enhancing Human-Like Responses in Large Language Models
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.
Advancements in enhancing natural language understanding, conversational coherence, and emotional intelligence in large language models improve user interactions and expand AI applications, while future research will address ethical implications and biases.
large language models fine-tuning psychological principles human reasoning patterns