capnrefsmmat

Joined 15 years ago

Karma
2,165
Submissions
409
Member since
Apr 4
https://www.refsmmat.com

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

View on Hacker News

Recent submissions

Comment · 2 days ago
But it isn't neutral average language; it's extremely idiosyncratic. GPT-4o used present participles something like 5 times more often than the average human, for instance.

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.

Comment · 4 days ago
Comment · 9 days ago
This theory doesn't hold up when you use base models (i.e., models with only pretraining, no RLHF or instruction tuning). They sound much more human. I've tested pairs (like Llama 3 base vs. instruct), and the base model can match human style much better than the instruct version can: https://www.pnas.org/doi/10.1073/pnas.2422455122

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.)

Comment · 1 month ago
This is the most memorable abstract I've ever read.

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.

Comment · 1 month ago
It's not the training data. When we've tested the writing style of base models that have not gone through instruction tuning, they're much more human-like (https://arxiv.org/abs/2410.16107). The style shift seems to come from something in the instruction-tuning process, so our current research problem is figuring out what in the process is doing it.
Comment · 2 months ago
Comment · 2 months ago
Comment · 2 months ago
Comment · 4 months ago
Being invited to conference talks around the world is a completely normal part of being an active researcher in almost any academic field, so it doesn't register as pompous to other academics.
Comment · 4 months ago
From your link,

> TerraPower must still complete construction, submit an operating license application, and satisfy all applicable safety and regulatory requirements before loading fuel and beginning operations.

Comment · 7 months ago
Discussion is included in the Dangerous Goods Panel report, agenda item 4.3 (pages 39-41) and Appendix E (beginning page 89). https://www.icao.int/sites/default/files/DangerousGoods/DGP%...

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.

Comment · 7 months ago
Several reasons:

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.

Comment · 7 months ago
Probably. One common feature of LLM output is grammatical features that indicate information density, like nominalizations, longer words, participial clauses, and so on. Perhaps training tasks that involve asking the LLMs for concise explanations or summaries encourage the use of these features to give denser answers.
Comment · 7 months ago
Comment · 7 months ago
I've heard the Kenya and Nigeria story, but has anyone backed it up with quantitative evidence that the vocabulary LLMs overuse coincides with the vocabulary that is more common in Kenyan and Nigerian English than in American English?

Live from the official Hacker News API

A modern reader for Hacker News · SvelteKit + Skeleton