Leveraging Large Language Models to Efficiently Collect Personalized Multi-Session Conversational Data

Core Concepts

The authors propose LAPS, an LLM-Augmented Personalized Self-Dialogue method, to efficiently collect large-scale, human-written, multi-session, and multi-domain conversational data with extracted user preferences.

Abstract

The authors introduce LAPS, a novel method for collecting personalized conversational data. LAPS leverages large language models (LLMs) to guide human workers in generating diverse and high-quality multi-session dialogues, while also extracting user preferences.
Key highlights:

LAPS uses LLMs to provide personalized guidance to human workers, enabling them to compose engaging and diverse conversational responses.
The method collects 1,406 multi-domain, multi-session dialogues, paired with 11,215 extracted user preferences.
Compared to existing datasets, LAPS-generated dialogues exhibit higher lexical diversity and overall quality, addressing the limitations of fully synthetic dialogue generation.
The authors demonstrate the benefits of storing user preferences in a semi-structured format (preference memory) for generating personalized recommendations, outperforming the baseline method that relies solely on dialogue history.
The collected LAPS dataset is suitable for training preference extraction and personalized response generation models.

Stats

LAPS collected 1,406 multi-domain, multi-session dialogues.
The dataset contains 11,215 extracted user preferences.

Quotes

"LAPS can collect large-scale, human-written, multi-session, and multi-domain conversations, including extracting user preferences."
"When compared to existing datasets, LAPS-produced conversations are as natural and diverse as expert-created ones, which stays in contrast with fully synthetic methods."
"By incorporating preference memory, the model can more accurately utilize the users' disclosed preferences for recommendations than the baseline method."

Key Insights Distilled From

Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search

by Hideaki Joko... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03480.pdf

Deeper Inquiries

How can the LAPS method be extended to collect personalized dialogues in other domains beyond recipes and movies?

The LAPS method can be extended to collect personalized dialogues in other domains by adapting the dialogue act classification, guidance generation, utterance composition, and preference extraction components to suit the specific characteristics of the new domain. Here are some key steps to extend the LAPS method:

Domain-specific Dialogue Act Classification: Define domain-specific dialogue acts that are relevant to the new domain. For example, if the new domain is fashion, dialogue acts could include browsing preferences, style preferences, brand preferences, etc.

Customized Guidance Generation: Develop personalized guidance prompts tailored to the new domain. The guidance should focus on eliciting user preferences specific to that domain. For instance, in a travel domain, the guidance could prompt the assistant to ask about preferred destinations, travel dates, accommodation preferences, etc.

Adapted Utterance Composition: Modify the utterance composition process to reflect the language and context of the new domain. Ensure that the human agents can effectively communicate user preferences and recommendations related to the specific domain.

Domain-specific Preference Extraction: Fine-tune the preference extraction model to recognize and extract preferences unique to the new domain. This may involve training the model on domain-specific data to improve accuracy.

Dataset Collection in the New Domain: Recruit crowd workers or human agents familiar with the new domain to participate in the dialogue collection process. Provide clear instructions and training specific to the domain to ensure high-quality data collection.

By customizing the LAPS method to the characteristics and requirements of the new domain, it can effectively collect personalized dialogues across a wide range of domains beyond recipes and movies.

How can the potential biases introduced by the LLM-generated guidance be mitigated?

Potential biases introduced by LLM-generated guidance in the LAPS method can be mitigated through several strategies:

Diverse Training Data: Ensure that the LLM is trained on a diverse dataset that represents a wide range of user preferences and conversational styles. This can help reduce bias towards specific patterns or responses.

Human Oversight: Incorporate human oversight in the guidance generation process. Human agents can review and adjust the LLM-generated guidance to ensure it aligns with the context of the conversation and avoids biased or inappropriate suggestions.

Regular Model Evaluation: Continuously evaluate the performance of the LLM in generating guidance. Monitor for any biases or inconsistencies in the generated prompts and make adjustments as needed.

Bias Detection Algorithms: Implement bias detection algorithms to identify and flag any biased language or suggestions in the LLM-generated guidance. This can help in proactively addressing potential biases before they impact the dialogue collection process.

Feedback Mechanism: Encourage feedback from human agents and participants on the quality and fairness of the LLM-generated guidance. Incorporate this feedback to refine the guidance generation process and minimize biases.

By implementing these strategies, the potential biases introduced by LLM-generated guidance in the LAPS method can be effectively identified and mitigated, ensuring the collection of high-quality and unbiased personalized dialogues.

How can the preference extraction and recommendation models be further improved to provide more explainable and trustworthy personalized responses?

To enhance the preference extraction and recommendation models for more explainable and trustworthy personalized responses, the following approaches can be considered:

Interpretability Techniques: Incorporate interpretability techniques such as attention mechanisms or saliency maps in the preference extraction model to highlight which parts of the dialogue contribute most to the extracted preferences. This can make the extraction process more transparent and explainable.

Preference Validation: Implement a validation mechanism where extracted preferences are presented to the user for confirmation or correction. This feedback loop can improve the accuracy and trustworthiness of the extracted preferences.

Contextual Understanding: Enhance the recommendation model's ability to understand the context of the conversation and user preferences. Consider incorporating contextual embeddings or memory networks to capture the nuances of multi-session dialogues.

Ethical Considerations: Integrate ethical considerations into the models to ensure that the personalized responses are fair, unbiased, and respectful of user privacy. Implement mechanisms to prevent the amplification of harmful biases in the recommendations.

User Feedback Integration: Allow users to provide feedback on the recommendations they receive. Use this feedback to continuously refine and improve the recommendation model, making it more aligned with user preferences and expectations.

By implementing these strategies, the preference extraction and recommendation models can be enhanced to provide more explainable and trustworthy personalized responses, fostering user trust and satisfaction in the conversational system.