Jiapu Wang · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28176 · open-access PDF
Model Bahasa Besar (LLM) sering berjuang dengan alasan berbasis waktu karena memori parameter statik mereka tidak sejalan dengan evolusi dinamis pengetahuan dunia nyata. Meskipun RAG dan Parameter-Efficient Fine-Tuning (PEFT) menyediakan cara praktis untuk meningkatkan LLM, mereka tetap tidak mencukupi untuk alasan waktu. Metode retrieval yang ada biasanya memprioritaskan relevansi semantik sambil mengabaikan validitas waktu, urutan peristiwa, dan ketergantungan evolusi, menghasilkan bukti yang relevan dalam isolasi tetapi terfragmentasi secara temporal.
Machine translation (Meta M2M100); the English original follows.
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. Although RAG and Parameter-Efficient Fine-Tuning (PEFT) provide practical ways to enhance LLMs, they remain insufficient for temporal reasoning. Existing retrieval methods typically prioritize semantic relevance while overlooking temporal validity, event order, and evolutionary dependencies, resulting in evidence that is relevant in isolation but temporally fragmented. Meanwhile, conventional PEFT methods usually
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