From GPT-4 to GPT-5: Measuring progress through MedHELM [pdf]

https://www.fertrevino.com/docs/gpt5_medhelm.pdf

by fertrevino • 11 months ago

127 96 11 months ago

I recently worked on running a thorough healthcare eval on GPT-5. The results show a (slight) regression in GPT-5 performance compared to GPT-4 era models.

I found this to be an interesting finding. Here are the detailed results: https://www.fertrevino.com/docs/gpt5_medhelm.pdf

woeirua2• 11 months ago

Interesting topic, but I'm not opening a PDF from some random website. Post a summary of the paper or the key findings here first.

BoredPositron• 11 months ago

It's hacker news. You can handle a PDF.

jeffbee• 11 months ago

I approve of this level of paranoia, but I would just like to know why PDFs are dangerous (reasonable) but HTML is not (inconsistent).

hypoxia• 11 months ago

Did you try it with high reasoning effort?

ares6234• 11 months ago

Sorry, not directed at you specifically. But every time I see questions like this I can’t help but rephrase in my head:

“Did you try running it over and over until you got the results you wanted?”

SequoiaHope• 11 months ago

What you describe is a person selecting the best results, but if you can get better results one shot with that option enabled, it’s worth testing and reporting results.

dcre3• 11 months ago

This is not a good analogy because reasoning models are not choosing the best from a set of attempts based on knowledge of the correct answer. It really is more like what it sounds like: “did you think about it longer until you ruled out various doubts and became more confident?” Of course nobody knows quite why directing more computation in this way makes them better, and nobody seems to take the reasoning trace too seriously as a record of what is happening. But it is clear that it works!

chairmansteve• 11 months ago

Or...

"Did you try a room full of chimpanzees with typewriters?"

xnx• 11 months ago

Have you looked at comparing to Google's foundation models or specialty medical models like MedGemma (https://developers.google.com/health-ai-developer-foundation...)?

fertrevino4• 11 months ago

That would be an interesting extension. MedGemma isn't part of the original benchmark either [1]. Since Gemini 2.0 Flash is on 6th place, expectations are for MedGemma to achieve higher than that :)

[1]https://crfm.stanford.edu/helm/medhelm/latest/#/leaderboard

username135• 11 months ago

I wonder what changed with the models that created regression?

teaearlgraycold• 11 months ago

Not sure but with each release it feels like they’re just wiping the dirt around and not actually cleaning.

degamad• 11 months ago

Obligxkcd: https://xkcd.com/1838/

oezi• 11 months ago

There is some speculation that GPT-5 uses a router to decide which expert model to deploy (e.g. to mini vs o/thinking models). So the router might decide that the query can be solved by a cheaper model and this model gives worse results.

aresant• 11 months ago

Feels like a mixed bag vs regression?

eg - GPT-5 beats GPT-4 on factual recall + reasoning (HeadQA, Medbullets, MedCalc).

But then slips on structured queries (EHRSQL), fairness (RaceBias), evidence QA (PubMedQA).

Hallucination resistance better but only modestly.

Latency seems uneven (maybe more testing?) faster on long tasks, slower on short ones.

woeirua2• 11 months ago

Definitely seems like GPT5 is a very incremental improvement. Not what you’d expect if AGI were imminent.

p1esk2• 11 months ago

What would you expect?

TrainedMonkey2• 11 months ago

GPT-5 feels like cost engineering. The model is incrementally better, but they are optimizing for least amount of compute. I am guessing investors love that.

narrator• 11 months ago

I agree. I have found GPT-5 significantly worse on medical queries. It feels like it skips important details and is much worse than o3, IMHO. I have heard good things about GPT-5 Pro, but that's not cheap.

I wonder if part of the degraded performance is where they think you're going into a dangerous area and they get more and more vague, for example like they demoed on launch day with the fireworks example. It gets very vague when talking about non-abusable prescription drugs for example. I wonder if that sort of nerfing gradient is affecting medical queries.

After seeing some painfully bad results, I'm currently using Grok4 for medical queries with a lot of success.

yieldcrv• 11 months ago

Yeah look at their open source models and how you get such high parameters in such low vram

Its impressive but a regression for now, in direct comparison to just high parameter model

RestartKernel• 11 months ago

I wonder how that math works out. GPT-5 keeps triggering a thinking flow even for relatively simple queries, so each token must be a magnitude cheaper to make this worth the trade-off in performance.

JimDabell• 11 months ago

I’ve found that it’s super likely to get stuck repeating the exact same incorrect response over and over. It used to happen occasionally with older models, but it happens frequently now.

Things like:

Me: Is this thing you claim documented? Where in the documentation does it say this?

GPT: Here’s a long-winded assertion that what I said before was correct, plus a link to an unofficial source that doesn’t back me up.

Me: That’s not official documentation and it doesn’t say what you claim. Find me the official word on the matter.

GPT: Exact same response, word-for-word.

Me: You are repeating yourself. Do not repeat what you said before. Here’s the official documentation: [link]. Find me the part where it says this. Do not consider any other source.

GPT: Exact same response, word-for-word.

Me: Here are some random words to test if you are listening to me: foo, bar, baz.

GPT: Exact same response, word-for-word.

It’s so repetitive I wonder if it’s an engineering fault, because it’s weird that the model would be so consistent in its responses regardless of the input. Once it gets stuck, it doesn’t matter what I enter, it just keeps saying the same thing over and over.

UltraSane• 11 months ago

Since the routing is opaque they can dynamically route queries to cheaper models when demand is high.

fertrevino4• 11 months ago

Mixed results indeed. While it leads the benchmark in two question types, it falls short in others which results in the overall slight regression.

credit_guy• 11 months ago

Here's my experience: for some coding tasks where GPT 4.1, Claude Sonnet 4, Gemini 2.5 Pro were just spinning for hours and hours and getting nowhere, GPT 5 just did the job without a fuss. So, I switched immediately to GPT 5, and never looked back. Or at least I never looked back until I found out that my company has some Copilot limits for premium models and I blew through the limit. So now I keep my context small, use GPT 5 mini when possible, and when it's not working I move to the full GPT 5. Strangely, it feels like GPT 5 mini can corrupt the full GPT 5, so sometimes I need to go back to Sonnet 4 to get unstuck. To each their own, but I consider GPT 5 a fairly bit move forward in the space of coding assistants.

czk• 11 months ago

its possible to use gpt-5-high on the plus plan with codex-cli, its a whole different beast! i dont think theres any other way for plus users to leverage gpt-5 with high reasoning.

codex -m gpt-5 model_reasoning_effort="high"

benlc• 11 months ago

Interestingly I'm experiencing the opposite as you. Was mostly using Claude Sonnet 4 and GPT 4.1 through copilot for a few months and was overall fairly satisfied with it. First task I threw at GPT 5, it excelled in a fraction of the time Sonnet 4 normally takes, but after a few iterations, it all went downhill. GPT 5 almost systematically does things I didn't ask it to do. After failing to solve an issue for almost an hour, I switched back to Claude which fixed it in the first try. YMMV

AndyNemmity• 11 months ago

Yeah, GPT 5 got into death loops faster than any other LLM, and I stopped using it for anything more than UI prototypes.

agos• 11 months ago

any thread on HN about AI (there's constantly at least one in homepage nowadays) goes like this:

"in my experience [x model] one shots everything and [y model] stumbles and fumbles like a drunkard", for _any_ combination of X and Y.

I get the idea of sharing what's working and what's not, but at this point it's clear that there are more factors to using these with success and it's hard to replicate other people's successful workflows.

kumarvvr• 11 months ago

I have an issue with the words "understanding", "reasoning", etc when talking about LLMs.

Are they really understanding, or putting out a stream of probabilities?

sema4hacker3• 11 months ago

The latter. When "understand", "reason", "think", "feel", "believe", and any of a long list of similar words are in any title, it immediately makes me think the author already drank the kool aid.

manveerc• 11 months ago

In the context of coding agents, they do simulate “reasoning” when you feed them the output and it is able to correct itself.

qwertytyyuu• 11 months ago

I agree with “feel” and “believe” but what words would you suggest instead of “understand” and “reason’?

vexna• 11 months ago

kool aid or not -- "reasoning" is already part of the LLM verbiage (e.g `reasoning` models having `reasoningBudget`). The meaning might not be 1:1 to human reasoning, but when the LLM shows its "reasoning" it does look _appear_ like a train of thought. If I had to give what it's doing a name (like I'm naming a function), I'd be hard pressed to not go with something like `reason`/`think`.

jmpeax• 11 months ago

Do you yourself really understand, or are you just depolarizing neurons that have reached their threshold?

dmead• 11 months ago

He doesn't know the answer to that and neither do you.

lucisferre2• 11 months ago

[flagged]

octomind• 11 months ago

It can be simultaneously true that human understanding is just a firing of neurons but that the architecture and function of those neural structures is vastly different than what an LLM is doing internally such that they are not really the same. Encourage you to read Apple’s recent paper on thinking models; I think it’s pretty clear that the way LLMs encode the world is drastically inferior to what the human brain does. I also believe that could be fixed with the right technical improvements, but it just isn’t the case today.

hodgehog114• 11 months ago

What does understanding mean? Is there a sensible model for it? If not, we can only judge in the same way that we judge humans: by conducting examinations and determining whether the correct conclusions were reached.

Probabilities have nothing to do with it; by any appropriate definition, there exist statistical models that exhibit "understanding" and "reasoning".

Workaccount22• 11 months ago

https://ai.vixra.org/pdf/2506.0065v1.pdf

Lays out pretty well what our current knowledge on understanding is

munchler• 11 months ago

Does it matter from a practical point of view? It's either true understanding or it's something else that's similar enough to share the same name.

axdsk• 11 months ago

The polygraph is a good example.

The "lie detector" is used to misguide people, the polygraph is used to measure autonomic arousal.

I think these misnomers can cause real issues like thinking the LLM is "reasoning".

dang3• 11 months ago

OK, we've removed all understanding from the title above.

fragmede2• 11 months ago

Care to provide reasoning as to why?

causality0• 11 months ago

I've definitely seen some unexpected behavior from gpt5. For example, it will tell me my query is banned and then give me a full answer anyway.

ancorevard• 11 months ago

so since reasoning_effort is not discussed anywhere, I assume you used the default which is "medium"?

energy123• 11 months ago

Also, were tool calls allowed? The point of reasoning models is to delete the facts so finite capacity goes towards the dense reasoning engine rather than recall, with the facts sitting elsewhere.

CuriouslyC• 11 months ago

GPT-5 is like an autistic savant

mattwad• 11 months ago

i thought cursor was getting really bad, then i found out i was on a gpt 5 trial. gonna stick with claude :)

0xDEAFBEAD• 11 months ago

So which of these benchmarks are most relevant for an ordinary user who wants to talk to AI about their health issues?

I'm guessing HeadQA, Medbullets, MedHallu, and perhaps PubMedQA? (Seems to me that "unsupported speculation" could be a good thing for a patient who has yet to receive a diagnosis...)

Maybe in practice it's better to look at RAG benchmarks, since a lot of AI tools will search online for information before giving you an answer anyways? (Memorization of info would matter less in that scenario)

andai• 11 months ago

Did this use reasoning or not? GPT-5 with Minimal reasoning does roughly the same as 4o on benchmarks.

From GPT-4 to GPT-5: Measuring progress through MedHELM [pdf]

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