arXiv：cs.AI· Nikolaos D. Tantaroudas, Ilias Karachalios, Andrew J. McCracken·· 5 小时前AI 评分39
工具调用检索 vs 向量 RAG：小型希腊语-英语知识库的准确率与用户输入希腊语方式的鲁棒性对比
Tool-calling retrieval versus vector RAG for a small Greek--English knowledge base: accuracy and robustness to how users type Greek
AI 导读
在 KyGround 基准（198 道题，来自希腊 Kythera 农业平台记录）上，以 Claude Haiku 4.5 作为路由与回答模型，向量 RAG 正确率 95.3%，工具调用智能体仅 71.6%，差 23.6 个百分点。
正文
Abstract:Assistants grounded in a small, frequently edited knowledge base can retrieve through tool calls to a live data interface or through vector retrieval-augmented generation (RAG). We compare the two on KyGround, a benchmark of 198 questions drawn from the published records of a Greek--English agricultural platform on Kythera, Greece, with answers verified automatically against the records and each question posed in up to nine forms, including Greek without accents, in capitals and in three Latin-script (Greeklish) schemes. With Claude Haiku 4.5 as router and answer model, a reconstruction of the platform's tool agent answered 71.6\% of canonical Greek questions correctly and vector RAG 95.3\% (difference $-23.6$ percentage points, 95\% CI $-33.1$ to $-15.1$). Letting the router write the vector query changed nothing, and placing the whole knowledge base of about 26,000 tokens in the prompt reached 99.3\%. The tool agent's losses arose in retrieval. Its literal searches returned nothing when the router's arguments did not occur verbatim in a record, for example when it transliterated Greek into Latin script or combined words that occur in a record but not as one phrase, and the agent then abstained. Unaccented and capitalised questions cost the tool agent about 20 points and vector RAG at most 2; accent-insensitive search removed this loss, and matching stemmed tokens raised the tool agent to 83.8\% on canonical Greek. Greeklish cost both designs about 21 to 32 points. Tool interfaces for community knowledge bases need search that tolerates how users type.
| Comments: | 13 pages; 2 figures; |
| Subjects: | Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08205 [cs.CE] |
| (or arXiv:2610.08205v1 [cs.CE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08205 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Nikolaos Tantaroudas Dr [view email]
[v1]
Tue, 6 Oct 2026 11:58:55 UTC (42 KB)
来源：arXiv：cs.AI · arxiv.org