capnrefsmmat
Joined 15 years ago
Joined 15 years ago
Though there evidently was an attempt to make GPT-5 sound more neutral, but in a literal sense: the emotional content of its text was flattened out. Suddenly it wasn't nearly as sycophantic or enthusiastic, and wrote much more dry, neutral text.
I think it's more accurate to say the text is not regressing to a mean, but to an artificially constructed style defined by the training process. And the AI companies have only partial control over that style, so weird things emerge from it.
That is, the original training on massive text corpora seems to produce an average of human writing, but that average looks pretty average. It doesn't feel like LLM slop. But the instruction tuning on specific tasks (answer this prompt, summarize this text, do this math problem), which turns text predictors into chatbots, seems to create strange style features as a side effect. And because that tuning step depends on company-specific training data (rather than just a crawl of the entire Internet), each company's LLM has a different style.
It's not that difficult to measure this. Linguists have had measures of style variation for a long time, though NLP and AI researchers seem to have forgotten. We've generated parallel human and AI corpora to measure it, and can show specifically which writing features change: https://arxiv.org/abs/2410.16107
Currently we're working on tweaking the instruction tuning process to see if we can pin down which steps and what types of tasks tend to produce the weird style features.
I think it's not the raw training corpus, but the samples collected specifically for reinforcement learning, direct preference optimization, or the other instruction-tuning steps that get a base model to turn into a chat/reasoning model. (We're working on testing this with the Allen AI models that have open training data.)
Smith, Gordon C. S., and Jill P. Pell. 2003. “Parachute Use to Prevent Death and Major Trauma Related to Gravitational Challenge: Systematic Review of Randomised Controlled Trials.” BMJ 327 (7429): 1459–61. https://doi.org/10.1136/bmj.327.7429.1459.
Objectives: To determine whether parachutes are effective in preventing major trauma related to gravitational challenge.
Design: Systematic review of randomised controlled trials.
Data sources: Medline, Web of Science, Embase, and the Cochrane Library databases; appropriate internet sites and citation lists.
Study selection: Studies showing the effects of using a parachute during free fall.
Main outcome measure: Death or major trauma, defined as an injury severity score > 15.
Results: We were unable to identify any randomised controlled trials of parachute intervention.
Conclusions: As with many interventions intended to prevent ill health, the effectiveness of parachutes has not been subjected to rigorous evaluation by using randomised controlled trials. Advocates of evidence based medicine have criticised the adoption of interventions evaluated by using only observational data. We think that everyone might benefit if the most radical protagonists of evidence based medicine organised and participated in a double blind, randomised, placebo controlled, crossover trial of the parachute.
> On average, students spent about 14 HPW preparing for class, 6 HPW working for pay, 12 HPW relaxing and socializing, 7 HPW participating in co-curricular activities and community service, 2 HPW caring for dependents, and 3 HPW commuting to campus. These activities accounted for a total weekly average of 45 HPW.
https://doi.org/10.1007/s11162-018-9497-z
I've looked for other data and have not found anything to indicate the trend has reversed.
Empirically, student free time grew for much of the 20th century, and students spend much less time on school work than they used to. The decline is not explained by students spending more time on work outside of school; much of it has become leisure time.
https://doi.org/10.1162/REST_a_00093 https://doi.org/10.1177/016146811011200908
Now perhaps there has been a sharp change in the years since those two studies (there certainly has been a sharp change in UK tuition costs), but the point is that our intuitions about how hard students must have to work these days are usually wrong.
I realize this isn't entirely serious, but I can't resist pointing out that this doesn't seem to be a good explanation for why LLMs write the way they do. When we've experimented with LLM writing style on open-weights models where you can get a base model (pretraining on text only) and an instruction-tuned variant (pretraining + post-training with RLHF and whatever other human-evaluated tasks), it's the instruction-tuned variant that shows the weird writing quirks. That is, the writing style is not because of the training texts, but because of whatever tasks the LLM companies do in instruction tuning. https://arxiv.org/abs/2410.16107
I'd speculate that this is partly impressed human preferences (the human raters unintentionally reward a particular writing style) and partly because of the chosen tasks: they're training the LLM to be good at, say, summarizing text, so it develops a style that's good at being informationally dense.
At any rate I've seen this same phenomenon with Llama and Gemma, and will be trying soon with Qwen. Unfortunately none of the commercial models lets you access the base model, as far as I know.
> TerraPower must still complete construction, submit an operating license application, and satisfy all applicable safety and regulatory requirements before loading fuel and beginning operations.
Paragraph 4.3.3:
> While data indicated that portable electronic devices were more often the cause of fire in aircraft cabins than power banks were, the latter were a significant concern due to their increased use and a prevalence of lower-quality products with defects or vulnerabilities that were more likely to lead to thermal events. Power banks were also not offered the same level of protection that batteries installed in portable electronic devices were provided. The amendments therefore focused on power banks.
1. The post mainly reiterates a single idea (Capsicum enumerates what the process can do, seccomp provides a configurable filter) in many different ways. There is not much actual depth, code samples notwithstanding. Nothing on why different designs were chosen, how easy each is to use, outcomes besides the Chrome example, etc.
2. There are a lot of AI writing tells, like staccato sentences, parallelism ("Same browser. Same threat model. Same problem."), pointless summary tables, "it's not X, it's Y" contradiction ("This is not a bug. It is the original Unix security model"), etc.
3. The author has roughly a blog post a day, all with similar style and on widely varied topics, and in the same writing style. Unless the author has deep expertise on a remarkably wide range of topics and spends all their time writing, these can't reflect deep insight or experience, but minimal editing of AI output.
So yes, it's pretty sloppy.
Associate teaching professor of Statistics & Data Science, Carnegie Mellon University
Author of Statistics Done Wrong (https://www.statisticsdonewrong.com), the woefully complete guide to statistical errors
email: alex at refsmmat dot com