| > automatically generated data graphs Are you saying there are data graphs that don't have humans in the chain? If so, what came up with the data and the tools to generate those graphs? And how do you decide which data and graph to trust? What exactly makes them "good sources"? > If I had to choose between a map from Google maps I would too. But Google Maps relies on local 3d party survey companies that use people, manual GIS tools, and image recognition AI. How do you know they don't have any mistakes in them? In fact, I live in a country where local area names are frequently misspelled on Google Maps, and reverse geocoding gives misleading addresses. I feel my point that all these "reputable sources" or "good sources" have biases (and mistakes) still stands. I must also point out that the 3 concrete examples given against my replies all involved visual content like graphs, maps, Peanuts cartoons, etc. But my comments were written with the typical text-based usage for QA in mind. I don't know if LLMs can fact-check map imagery or data graphs (probably not, but I've never tried). It's just not the kind of thing I'd ever use LLMs for, to begin with. |
That does not contend with the premise. The premise is not that reputable sources are perfect or form objective truth.
The premise is that when you do not have the time to investigate something closer reputable sources are much more likely to be close to the truth than LLMs that are trained on these reputable sources as well as unreputable sources and just mimick these training source material thereby obscuring the source of information and introducing hallucinations.
When you have the time to investigate information, primary sources and reputable secondary sources allow you to more easily trace information and judge its validity. LLMs by their nonpredictable nature hinder this.
Thesis: "A is better than B"
Argument: "A is flawed"
The argument does not engage with or weakens the thesis.