GENERATIVE AI RESEARCH ANALYSIS
FollowGPT: Revolutionizing Follow-up Question Generation with Real-world LLM Interaction Logs
FollowGPT introduces a novel framework that leverages real-world user-LLM conversation logs to generate more authentic and relevant follow-up questions, enhancing user engagement and information exploration.
Executive Impact: Enhanced User Engagement and Operational Efficiency
FollowGPT's innovative approach significantly improves the quality and relevance of follow-up questions, directly translating into higher user satisfaction and more efficient information retrieval processes for enterprise LLM applications.
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BLEU-1 Score (ShareFQG)
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BERTScore (ShareFQG)
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Avg. Follow-up Question Length
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High-Quality Training Samples
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Challenge of Proactive LLMs
Current Large Language Models (LLMs) passively respond to user questions, limiting their ability to proactively guide conversations. The Follow-up Question Generation (FQG) task aims to bridge this gap by automatically generating insightful follow-up question candidates, enhancing user engagement and facilitating deeper exploration of related topics. FollowGPT addresses the critical need to align these generated questions with real-world user intents.
Hierarchical Filtering for Data Quality
Directly using raw conversation logs for FQG leads to significant noise and sparsity. FollowGPT addresses this with a hierarchical filtering strategy. Simple noise types (extreme length, topic shifts, surface-level repetitions) are handled with text-matching rules and semantic similarity filtering. Hard noise types (keyword mismatches, logical conflicts, excessive specialization) leverage powerful LLMs with carefully designed prompt templates for nuanced judgment, ensuring the reliability and relevance of the training data.
Multi-Dimensional Data Synthesis
To mitigate data sparsity and enhance model generalization, FollowGPT employs data synthesis methods across three dimensions: Topic Diversity, Intent Transition Diversity, and Negative Sample Diversity. This process involves identifying underrepresented topics using external knowledge bases (ODP), categorizing user intent transitions (Refinement, Comparison, Extension), and constructing diverse hard negative follow-up questions for preference data.
Two-Stage Training Framework
FollowGPT utilizes a robust two-stage training framework. The first stage involves Supervised Fine-Tuning (SFT) on high-quality filtered and synthesized data, enabling the model to master FQG tasks and learn diverse generation patterns. The second stage employs Direct Preference Optimization (DPO), refining the model's outputs to align more closely with human preferences by optimizing the probability ratio between positive and negative samples, thereby improving overall question quality and user acceptance.
Outperforming Baselines
Experimental evaluations on both the ShareFQG test set (in-domain) and FollowupQG (out-of-domain) datasets demonstrate FollowGPT's superior performance. The model consistently outperforms existing baselines across various metrics including lexical similarity (BLEU, ROUGE) and semantic similarity (BERTScore), validating the effectiveness of its data filtering, synthesis, and two-stage training methodology. GPT-40-based comparative evaluations further confirm its strength.
Advancing Conversational AI
FollowGPT represents a significant advancement in generating authentic and relevant follow-up questions for LLMs. By effectively mining real-world user interaction logs and employing sophisticated data processing and training, it enhances user experience and guides deeper information exploration. Future directions include extending the framework to multi-modal settings and refining intent modeling for even more nuanced question generation.
Enterprise Process Flow: FollowGPT's Training Pipeline
Data Extraction & Filtering
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Data Synthesis
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Supervised Fine-Tuning (SFT)
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Direct Preference Optimization (DPO)
Key Performance Indicator
33.04 Percentage Increase in BLEU-1 Score on ShareFQG (vs. T5)| Feature | FollowGPT | Qwen2.5-3B (SFT) | Mistral-7B (In-Context) | BART (Small-scale PLM) |
|---|---|---|---|---|
| BLEU-1 (Lexical Similarity) | 33.04 | 30.43 | 23.42 | 28.88 |
| BERTScore (Semantic Similarity) | 67.03 | 64.34 | 61.13 | 60.85 |
| Generalization (FollowupQG BLEU-1) | 9.64 | 9.10 | 8.42 | 8.00 |
| Diversity (ShareFQG Dist-2) | 62.55 | 59.97 | 63.98 | 60.43 |
Case Study: Enhanced Contextual Relevance in Orienteering Race Scenario
This case study illustrates FollowGPT's superior ability to generate contextually relevant and user-intent-aligned follow-up questions compared to baselines.
Initial Question (Q1): In a middle-distance orienteering race, what strategies can athletes use to optimize their route choice and pace?
System Answer (R): To optimize their route choice and pace in a middle-distance orienteering race, athletes can use strategies such as reading the map ahead, breaking the course into smaller sections, and using handrails like trails or streams. They should also consider physical condition and adjust pace, factoring terrain difficulty and navigation complexity.
FollowGPT Output: How do athletes determine the optimal handrails to use in a middle-distance orienteering race, considering factors like terrain difficulty and personal navigation skills?
Baseline Models (e.g., BART): How can athletes specifically use the map ahead to anticipate upcoming challenges and adjust pace accordingly in a middle-distance orienteering race? (Lacks focus on 'handrails', less aligned with user intent.)
Impact: FollowGPT's question not only tightly centers on the "handrails" theme but also incorporates additional elements like "terrain difficulty" and "personal navigation skills," making it more aligned with user intent and situational needs, thereby generating a higher-quality follow-up query.
Real-World Data Leverage
97.4 Percentage of Real-world Logs Retained after FilteringCalculate Your Potential ROI with FollowGPT
Estimate the efficiency gains and cost savings your enterprise could achieve by integrating advanced FQG capabilities.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Transformation Roadmap
A typical phased approach to integrating FollowGPT and similar advanced LLM capabilities into your enterprise.
Phase 01: Discovery & Strategy
Initial consultation to understand your specific needs, assess current LLM usage, and define clear objectives for FQG integration.
Phase 02: Data Preparation & Model Training
Clean and synthesize your proprietary conversation logs, fine-tune FollowGPT with SFT and DPO, ensuring alignment with your enterprise context.
Phase 03: Pilot Deployment & Evaluation
Deploy FollowGPT in a pilot program with a select user group, collect feedback, and rigorously evaluate performance against defined KPIs.
Phase 04: Full-Scale Integration & Optimization
Roll out FollowGPT across your organization, provide ongoing support, and continuously optimize the model based on evolving user interactions.
Ready to Elevate Your LLM Interactions?
Harness the power of real-world user intent to generate more intelligent and engaging follow-up questions with FollowGPT.
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