Efficient KG Reasoning for Small LLMs
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LightPROF is a lightweight framework that enables small-scale language models to perform complex reasoning over knowledge graphs (KGs) using structured prompts. Key highlights:
Retrieve-Embed-Reason pipeline – LightPROF introduces a three-stage architecture:
Plug-and-play & parameter-efficient – LightPROF trains only the adapter and projection modules, allowing seamless integration with any open-source LLM (e.g., LLaMa2-7B, LLaMa3-8B) without expensive fine-tuning.
Outperforms larger models – Despite using small LLMs, LightPROF beats baselines like StructGPT (ChatGPT) and ToG (LLaMa2-70B) on KGQA tasks: 83.8% (vs. 72.6%) on WebQSP and 59.3% (vs. 57.6%)on CWQ.
Extreme efficiency – Compared to StructGPT, LightPROF reduces token input by 98% and runtime by 30%, while maintaining accuracy and stable output even in complex multi-hop questions.
Ablation insights – Removing structural signals or training steps severely degrades performance, confirming the critical role of the Knowledge Adapter and retrieval strategy.
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