title: Enhancing Human-Like Responses in Large Language Models
summary: 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.
paper_authors._id: 6780bd677a8cd9c2c5053b7d
paper_authors.name: Ethem Yağız Çalık
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-01-10T06:28:04.751Z
paper_authors._id: 6468ce47e134d050a58aa89c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6468ce47e134d050a58aa89c/ApFcPlOzgI6Cjr0SYPpk6.png
paper_authors.fullname: Yağız Çalık
paper_authors.type: user
paper_authors.user: Weyaxi
paper_authors._id: 6780bd677a8cd9c2c5053b7e
paper_authors.name: Talha Rüzgar Akkuş
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-01-10T06:28:32.323Z
paper_authors._id: 63da3d7ae697e5898cb86854
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1675246771355-noauth.jpeg
paper_authors.fullname: Talha Rüzgar Akkuş
paper_authors.type: user
paper_authors.user: Q-bert
paper_title: Enhancing Human-Like Responses in Large Language Models
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) natural language understanding conversational coherence emotional intelligence fine-tuning diverse datasets psychological principles human reasoning patterns