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The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs

Paper recorded by Signals 4 on 2026-09-09 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-09 on arXiv · recorded by Signals 4 on 2026-09-10

Category: cs.CL · 自然语言处理 · first seen 2026-09-10

Abstract

A graph retrieval-augmented generation pipeline chooses which triples to put in the prompt, a syntax to write them in, an order to write them in, and a sentence telling the model what to do with them. We vary all four over six large language models and two knowledge-graph question answering benchmarks. Two of the four choices move the answer and the other two are flat. The first is whether the ans

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#89 most recent of 186 cs.CL papers we have recorded · ↑ newer: LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation · ↓ older: $Φ$-Bench: Can Large Language Models Engineer the Infrastructure That
Cite this page: The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs: the #89 most recent of 186 cs.CL papers we have recorded (as of 2026-09-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/the-answer-path-and-the-grounding-instruction-in-llm-question-answering-over-kno.html
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