In-Dialogue Learning for Personalized Dialogue Generation

핵심 개념

In-Dialogue Learning (IDL) enhances personalized dialogue generation by leveraging dialogue history without pre-defined profiles, leading to significant improvements in response quality and alignment with persona information.

초록

The study introduces In-Dialogue Learning (IDL) for personalized dialogue generation without pre-defined profiles. IDL utilizes Mutual Supervised Learning (MSL) and Deep Personalized Alignment (DPA) stages to optimize large language models for persona-based responses. Experimental results show IDL outperforms existing methods in coherence, diversity, and persona alignment.
Personalized dialogue systems have gained attention for incorporating special characteristics into responses. Existing approaches rely on pre-defined personal profiles, which are time-consuming and lack flexibility. IDL proposes a fine-tuning framework to leverage dialogue history for personalized responses without pre-defined profiles.
Experiments demonstrate that IDL significantly improves BLEU and ROUGE scores by up to 200% and 247%, respectively. Human evaluations confirm the efficacy of IDL in simulating personality traits and tone accurately. Ablation studies highlight the importance of each component in the IDL framework.

통계

BLEU scores increasing by up to 200%
ROUGE scores increasing by up to 247%

인용구

"In-Dialogue Learning enhances personalized dialogue generation by leveraging dialogue history without pre-defined profiles."
"Experimental results show that IDL outperforms existing methods in coherence, diversity, and persona alignment."

핵심 통찰 요약

"In Dialogues We Learn"

by Chuanqi Chen... 게시일 arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.03102.pdf

더 깊은 질문

How can In-Dialogue Learning be applied beyond personalized dialogue systems?

In-Dialogue Learning (IDL) can be applied beyond personalized dialogue systems in various ways. One potential application is in customer service chatbots, where the system can learn from past interactions to provide more tailored and effective responses to user queries. IDL could also be utilized in educational platforms to create more engaging and interactive learning experiences for students by adapting the content based on their individual progress and preferences. Additionally, IDL could enhance virtual assistants by enabling them to better understand user needs and preferences over time, leading to more efficient and personalized assistance.

What are potential drawbacks or limitations of relying solely on large language models for generating personalized responses?

Relying solely on large language models for generating personalized responses may have several drawbacks and limitations. One limitation is the risk of bias amplification, as these models may inadvertently perpetuate stereotypes or biases present in the training data when generating responses tailored to specific personas. Another drawback is the lack of interpretability, as it can be challenging to understand how these models arrive at their decisions or responses without clear explanations. Moreover, large language models require significant computational resources and energy consumption, making them less sustainable compared to simpler approaches.

How might the concept of learning from dialogues be extended to other AI applications beyond conversation?

The concept of learning from dialogues can be extended to other AI applications beyond conversation by incorporating contextual information into various tasks. For example:

Content Generation: In content creation tools like writing assistants or code generators, AI systems could learn from dialogues between users and provide contextually relevant suggestions.
Healthcare: AI systems could learn from patient-doctor conversations to assist healthcare professionals with diagnosis recommendations or treatment plans.
Legal Services: By analyzing legal discussions between lawyers or clients, AI systems could offer insights into case strategies or legal document generation.
Collaborative Work Environments: In team collaboration tools, AI systems could analyze communication patterns within teams' dialogues for improving teamwork dynamics or project management processes.

By leveraging dialogue-based learning techniques across diverse domains, AI applications can become more adaptive, context-aware, and capable of providing tailored solutions based on real-time interactions with users or stakeholders.