This paper introduces a systematic framework for categorizing and understanding different types of human feedback in reinforcement learning systems, helping bridge machine learning and human-computer interaction.
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https://arxiv.org/abs/2411.11761
Original Problem 🤔:
Current RLHF systems use limited feedback types like binary preferences, ignoring the rich variety of human communication. This restricts expressiveness and disregards human factors in the learning process.
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Solution in this Paper 💡:
→ The paper develops a taxonomy with 9 key dimensions across human-centered, interface-centered and model-centered perspectives.
→ It identifies 7 quality metrics to evaluate feedback effectiveness including expressiveness, ease of use, precision and informativeness.
→ The framework unifies different feedback types like demonstrations, corrections, preferences and implicit signals.
→ It provides concrete guidelines for designing RLHF systems that can handle diverse feedback types.
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Key Insights from this Paper 🔍:
→ Human feedback exists on a spectrum from explicit to implicit, proactive to reactive
→ Context and uncertainty need to be carefully tracked and modeled
→ Different feedback types have varying levels of precision and informativeness
→ System design must balance human effort with feedback quality
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Results 📊:
→ Framework successfully classifies 14 established feedback types from literature
→ Validates across multiple use cases including LLMs and robotics
→ Provides unified approach for measuring 7 key feedback qualities
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