Editing LLM Conversational Level (section)

=== Word Salad 1 ===

{{Tweet
| name = Emmett Shear
| username = eshear
| text = The incredibly clever trick that all frontier AI labs use which makes LLMs so fabulously powerful: they barely regularize at all, resulting in models which are massively overfit. This would be a problem, except they’re overfit to the domain of all human cultural knowledge.
| date = Aug 30, 2025
| ID = 1961975432238223675
| ref-name = Tweet_1961975432238223675
| block = true
}}

{{Tweet
| name = rizz or bust
| username = rizzorbust
| replyto = eshear
| text = what a word salad

emmett also reminding us he was ceo of OAI for 24hrs
The incredibly clever trick that all frontier AI labs use which makes LLMs so fabulously powerful: they barely regularize at all, resulting in models which are massively overfit. This would be a problem, except they’re overfit to the domain of all human cultural knowledge.
| date = Aug 30, 2025
| ID = 1962255199470321841
| ref-name = Tweet_1962255199470321841
| block = true
}}

Gemma 3n 4b flawlessly comprehends the message, so the LCL is bounded above by 4B.

[[File:{{#setmainimage:Emmett Shear Tweet Evaluation by Gemma 3n 4b.png}}|frame|center]]

I couldn't get a smaller Gemma model at the time since OpenRouter's Gemma 2b doesn't generate and just returns 400. It is interesting to observe what happens when you use smaller models, though. And Llama 3.2 conveniently has a few available. The comparison rapidly illustrates the difference.

[[File:Emmett Shear Tweet Evaluation by Llama 3.2.png|frame|center]]

Repeating the experiment has the smaller model more often describe the tweet as word salad, while the larger model concludes that more rarely, though it does not often explain the tweet correctly. The largest models like Opus 4.1 understand it every time and never make a mistake. So Twitter user rizzorbust likely exceeds LCL 1B but is lower than 4B and perhaps lower than 3B.