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yorwbayesterday at 10:32 PM2 repliesview on HN

"make it as dark as possible"

  {
    "function_calls": [
      {
        "name": "set_thermostat",
        "arguments": {
          "temperature": 72,
          "mode": "cool",
          "room": "living room"
        }
      }
    ],
    "reasoning": "'as dark as possible' -> set_thermostat to warm; 'dark' implies higher temperature; 'cool' mode for darkness.",
    "confidence": 0
  }
... maybe this counts as dark humor at least.

Since it seems limited to matching a few templates and otherwise falling flat on its face, I wonder how 14MB of regexes would fare in its stead. Normally you wouldn't want to parse arbitrary natural language input with regex because of how tedious and brittle it would be, but for the tedium we have LLMs and this alternative isn't exactly robust either.


Replies

HenryNdubuakutoday at 9:38 AM

SO when confused, it gives you a low confidence score as a signal, in which case its recommended to finetune using th python package, train on your mac/PC

silentbob7today at 5:59 AM

I also wonder how small a LLM trained on catching only subject (e.g. living room) and action (light on) from text input could be compared to needle - the json wrapping could be done afterwards using templates.

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