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Increased personalisation in future llms

one of the things I personally find frustrating about LLMs is their tendency to offer unsolicited advice. in the future will users be able to better modify their experiences with LLMs

Based on the search results, there is a significant movement towards greater personalization and user control of LLM interactions:

Future Personalization Capabilities

The technology is evolving to give users more control over their LLM experiences in several ways:

Customization Options:

  • Future LLMs will enable hyper-personalization, where interactions are tailored at a granular level[1]
  • Systems will learn to adapt their response style and level of guidance based on individual user preferences[1]

Proactive vs Reactive:\ Interestingly, there's a tension in the development approach:

  • Some platforms are moving towards more proactive, anticipatory responses[1]
  • Others recognize that unsolicited guidance can be problematic, noting that LLMs should primarily respond to direct prompts rather than offering unprompted advice[6]

Custom Solutions

The rise of custom LLMs offers promising solutions:

  • Organizations can develop specialized models aligned with specific needs and communication styles[5]
  • Users may eventually have access to personal LLM instances that learn their preferences over time[5]
  • Fine-tuning techniques allow for adjusting model behavior to match desired interaction patterns[5]

Current Limitations

It's worth noting that current LLMs have inherent constraints:

  • They can only respond to explicit prompts and lack true initiative[6]
  • Their responses are based on pattern matching rather than genuine understanding[6]
  • They have limited ability to maintain consistent personalization over long interactions[6]

The trend suggests that future LLM interactions will become increasingly customizable, allowing users to better control the level and style of engagement they prefer.

Citations:\ [1] The Role of LLMs in Personalizing User Experiences - RapidCanvas https://www.rapidcanvas.ai/blogs/the-role-of-llms-in-personalizing-user-experiences\ [2] An Analysis of Chinese LLM Censorship and Bias with Qwen 2 Instruct https://huggingface.co/blog/leonardlin/chinese-llm-censorship-analysis\ [3] Emerging Reliance Behaviors in Human-AI Text Generation - arXiv http://arxiv.org/html/2409.08937\ [4] Explicit and Implicit LLM User Feedback: A Quick Guide - Nebuly https://www.nebuly.com/blog/explicit-implicit-llm-user-feedback-quick-guide\ [5] How Custom LLMs Can Transform Your Industry? - Signity Solutions https://www.signitysolutions.com/blog/how-custom-llms-can-transform-your-industry\ [6] Your Large Language Model - it's as Dumb as a Rock - LinkedIn https://www.linkedin.com/pulse/your-large-language-model-its-dumb-rock-jason-flaks