Enterprise AI Analysis
GROUNDED IN REALITY: LEARNING AND DEPLOYING PROACTIVE LLM FROM OFFLINE LOGS
This paper introduces Learn-to-Ask, a novel simulator-free framework for training proactive, goal-oriented Large Language Models (LLMs) directly from offline expert dialogue data. By reframing the long-horizon Reinforcement Learning problem into a series of supervised tasks and leveraging the 'observed future' of expert trajectories, Learn-to-Ask infers a dense, turn-by-turn reward signal. It allows LLMs to learn both 'what to ask' and 'when to stop,' a critical capability for high-stakes domains like healthcare. The framework includes an Automated Grader Calibration pipeline to ensure reward fidelity with minimal human supervision. Empirical results on a real-world medical dataset demonstrate significant improvements in questioning quality and termination accuracy across LLM sizes up to 32B. Crucially, the approach led to successful deployment in a large-scale online AI service, achieving performance superior to human experts and validating its ability to bridge the 'reality gap' and deliver tangible real-world impact.
Transforming Enterprise Operations with Proactive LLMs
Learn-to-Ask delivers measurable improvements in efficiency, accuracy, and business outcomes.
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Good Hit Rate (WA-GH) Improvement (7B Model)
Termination Accuracy (WS) (7B Model)
Lift in Dialog-to-Purchase Conversion (Production)
Deep Analysis & Enterprise Applications
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Learn-to-Ask is a novel framework that learns a complete, sequential questioning policy—including a stopping condition—directly from offline expert logs. This provides a grounded, data-driven, and economically viable alternative to brittle user simulators, completely bypassing the 'reality gap' inherent in simulation-based approaches.
We introduce a method to infer dense, turn-by-turn rewards by using the observed future of expert trajectories. This is coupled with an Automated Grader Calibration pipeline that ensures reward fidelity with minimal human oversight, systematically mitigating oracle noise and enabling the decomposition of complex long-horizon problems into tractable supervised learning tasks.
The framework's efficacy is demonstrated not only via offline experiments but also by its successful deployment into a live, large-scale medical AI service. The deployed agent achieved super-human performance on key business metrics, proving the framework's ability to translate offline data into tangible, real-world impact and offering a practical blueprint for transforming passive LLMs into proactive applications.
Information Completeness Rate Achieved in Live Deployment
Learn-to-Ask Framework Overview
Offline Expert Logs
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Hindsight-driven Reward Modeling
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Automated Prompt Calibration
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Grounded Reward Formulation
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Tractable Policy Optimization
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Proactive LLM Deployment
| Feature | Traditional LLMs (SFT/DPO) | Learn-to-Ask |
|---|---|---|
| Proactivity | Passive responders, single-turn focus |
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| Policy Learning | Myopic, attribute-based, no stopping condition |
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| Data Source | Synthetic preference data or direct imitation |
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| Reality Gap | Prone to simulator-reality gap or generalization issues |
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| Reward System | Sparse, binary preferences, unstable value estimation |
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Real-World Medical AI Service Deployment
Learn-to-Ask was successfully deployed in a large-scale online AI service, 'Medication AI Assistant,' handling thousands of users daily. Operating on a dataset 100x larger and covering 10x more medical conditions than the academic benchmark, the 32B model, trained with Learn-to-Ask, significantly outperformed the 7B model. This production success confirmed its ability to transcend academic performance and meet complex business demands. The system achieved a 93% information completeness rate and an 88% good-question rate online, translating to a 1.87x lift in dialog-to-purchase conversion compared to human-based services.
- Deployed into live, large-scale medical AI service.
- Achieved performance superior to human experts.
- 1.87x lift in dialog-to-purchase conversion.
- Proactive engagement to gather symptoms for OTC medication recommendations.
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Your Proactive LLM Implementation Roadmap
Our phased approach ensures a smooth and effective integration of proactive LLMs into your operations.
Phase 1: Discovery & Strategy (2-4 weeks)
Understand your specific goals, existing data, and define target proactive LLM behaviors. This includes data audit and initial prompt calibration.
Phase 2: Data Preparation & Model Training (6-10 weeks)
Utilize your offline expert logs with our Learn-to-Ask framework. Automated Grader Calibration and RFT training of base LLMs (e.g., Qwen2.5-32B Instruct) for targeted proactivity.
Phase 3: Pilot Deployment & Refinement (4-8 weeks)
Deploy a pilot version in a controlled environment. Gather initial feedback, analyze performance metrics, and iteratively refine the model and prompts based on real-world interactions.
Phase 4: Full-Scale Rollout & Continuous Optimization (Ongoing)
Expand deployment across your enterprise. Leverage the Auto-Prompt pipeline for continuous learning and adaptation, ensuring long-term performance and alignment with evolving business needs.
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