Temporal Evidence-Parameter Co-Alignment for LLM Reasoning over Temporal Knowledge Graphs
To address these limitations, we propose Temporal Evidence-Parameter Co-Alignment (TEPCA), a framework for LLM reasoning over TKGs.
ProofPaper ↗
Key points
- Large Language Models (LLMs) often struggle with temporally grounded reasoning because their static parametric memory is misaligned with the dynamic evolution of real-world knowledge.
- TEPCA aligns LLM reasoning with temporal knowledge at both the evidence and parameter levels.
- At the evidence level, it introduces time-constrained Dynamic Relevance-Guided Path (DRGP) pruning to preserve globally coherent temporal paths, together with Mutual-Information (MI)-based pruning to retain query-informative local facts under temporal constraints.
- This design enables LLMs to better capture fine-grained entity evolution and time-dependent relation semantics while preserving parameter efficiency.
Sources (1)
- [1]Temporal Evidence-Parameter Co-Alignment for LLM Reasoning over Temporal Knowledge GraphsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 01:16 PM
To address these limitations, we propose Temporal Evidence-Parameter Co-Alignment (TEPCA), a framework for LLM reasoning over TKGs.
Large Language Models (LLMs) often struggle with temporally grounded reasoning because their static parametric memory is misaligned with the dynamic evolution of real-world knowledge.
Extractive summary: sentences quoted from the sources.
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