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Personalized Large Language Models
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Large language models (LLMs) have significantly advanced Natural Language Processing (NLP) tasks in recent years. However, their universal nature poses limitations in scenarios requiring personalized responses, such as recommendation systems and chatbots. This paper investigates methods to personalize LLMs, comparing fine-tuning and zero-shot reasoning approaches on subjective tasks. Results demonstrate that personalized fine-tuning improves model reasoning compared to non-personalized models. Experiments on datasets for emotion recognition and hate speech detection show consistent performance gains with personalized methods across different LLM architectures. These findings underscore the importance of personalization for enhancing LLM capabilities in subjective text perception tasks.
Forward citations
Cited by 4 Pith papers
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Backtranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classification
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An external 'superego' module that filters agentic AI plans against user-selected 'constitutions' plus a universal safety floor is reported to cut harmful outputs by up to 98% on safety benchmarks.
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Do Implicit Personalization and Explicit Styles Conflict? PsPLUG: A Lightweight Plug-in for Balancing Personalization and Style in Customized LLMs
PsPLUG, a soft-prompt plug-in trained with style-conditioned preference pairs, preserves user identity under explicit style instructions and lets users tune personalization strength via an α scalar.
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Personalised Explanations in Long-term Human-Robot Interactions
A two-stage LLM pipeline that first generates an explanation and then personalises it using a stored user knowledge memory reduces explanation length only when the user has related prior knowledge, in synthetic hospit...
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