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Got it. Thanks. Any data on how LLMs like this format? Are they able to make the associations etc?
by throw03172019 10mo ago
Got it. Thanks.
Any data on how LLMs like this format? Are they able to make the associations etc?
- maheshvaikri99 10mo agoYes - I ran a 300 Questions benchmark comparing ISON vs JSON vs JSON-COMPACT etc on the same tasks. ISON: 88.3% accuracy JSON: lower (can share exact numbers if interested) Tested across Claude, GPT-4, DeepSeek, and Llama 3. The key finding: LLMs handle tabular formats natively because they've seen billions of markdown tables and CSVs in training. No special prompting needed. For associations, I tested with multi-table ISON docs like: table.users id name 1 Alice 2 Bob table.orders id user_id product 101 :1 Widget 102 :2 Gadget Prompt: "What did Alice order?" All models correctly resolved :1 → Alice → Widget without explicit instructions about the reference syntax. The 30-70% token savings come from removing JSON's structural overhead (braces, quotes, colons, commas) while keeping the same semantic density. Haven't published formal benchmarks on this yet - that's good feedback. I should.