Summary of Selective Prompting Tuning For Personalized Conversations with Llms, by Qiushi Huang et al.
Selective Prompting Tuning for Personalized Conversations with LLMs
by Qiushi Huang, Xubo Liu, Tom Ko, Bo Wu, Wenwu Wang, Yu Zhang, Lilian Tang
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Selective Prompt Tuning (SPT) method aims to personalize large language models (LLMs) for conversational AI. Despite LLMs’ improved response coherence, effective persona integration remains a challenge. The study first analyzes two common approaches: textual prompting and direct fine-tuning. Textual prompting often fails to produce responses similar to ground truths, while direct fine-tuning leads to repetitive or generic replies. To address these issues, SPT initializes soft prompts and uses a trainable dense retriever to adaptively select suitable prompts for LLMs based on input contexts. The model also incorporates context-prompt contrastive learning and prompt fusion learning to enhance response diversity. Experimental results on the CONVAI2 dataset demonstrate significant enhancements in response diversity (up to 90%) and other critical performance indicators, highlighting SPT’s efficacy in generating engaging and personalized dialogues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SPT is a new method for personalizing conversations with large language models. Right now, these models are good at answering questions, but not great at having a conversation that feels like it’s with a real person. To make them better, researchers tried two different approaches: giving the model hints about what to say next (called textual prompting) and training the model specifically for a certain kind of conversation. But both of these methods had problems – the prompts didn’t always work well, and the trained models just repeated themselves or said generic things. SPT is a new way to do things that tries to fix these issues by giving the model hints about what to say next in a more flexible way. It also uses special learning techniques to make sure the conversations feel diverse and interesting. In tests, SPT worked really well – it made conversations that were up to 90% more personalized and engaging than before. |
Keywords
» Artificial intelligence » Fine tuning » Prompt » Prompting
