AI forecasting bots incoming
59habryka
36gwern
27gwern
24gwern
20Jan_Kulveit
12Vanessa Kosoy
3Vanessa Kosoy
2Raemon
3Raemon
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Update: After seeing a comment by AdamK on Manifold, I dug into the code and can confirm that the way the codebase queries for articles does at least check for meta tags that indicate when an article was last updated (which my guess is aren't reliable, but it does seem like they at least tried). I would be highly surprised if their code addresses all of the myriad data-contamination issues (including very tricky ones like news articles that predicted things accurately getting more traffic after a forecasted event happened and therefore coming up higher in search results, even if they were written before the resolution time). I am currently taking bets that on prospective forecasts this system will perform worse than advertised (and also separately think that the advertised performance does not meaningfully make this system "superhuman")
How did you handle issues of data contamination?
In your technical report you say you validated performance for this AI system using retrodiction:
Performance. To evaluate the performance of the model, we perform retrodiction, pioneered in Zou
et al. [3]. That is to say, we take questions about past events that resolve after the model’s pretraining
data cutoff date. We then compare the accuracy of the crowd with the accuracy of the model, both having access to the same amount of recent information. When we retrieve articles for the forecasting AI, we use the search engine’s date cutoff feature, so as not to leak the answer to the model.
I am quite concerned about search engines actually not being capable of filtering out data for recent events.As an example, I searched “Israel attack on Iran” as you mention that as a concrete example in this excerpt of the blog post:
Concretely, we asked the bot whether Israel would carry out an attack on Iran before May 1, 2024. .
The first result of searching for "Israel attack on Iran", if you set the date cutoff to October 1st 2023, is this:
As you can see, Google claims a publishing data of "Aug 11, 2022". However, when you click into this article, you will quickly find the following text:
The article actually includes updates from April 19, 2024! This is very common, as many articles get updated after they are published.
The technical report just says:
When we retrieve articles for the forecasting AI, we use the search engine’s date cutoff feature, so as not to leak the answer to the model.
But at least for Google this fails, unless you are using an unknown functionality for Google.
Looking into the source code, it appears that the first priority source you check is some meta tags:
date_meta_tags = ['article:published_time', 'datePublished', 'date', 'pubdate', 'og:published_time', 'publishdate' ]However, for the article I just linked, those meta tags do indeed say the article was published in 2022:
<meta property="article:published_time" content="2022-08-11T11:15:47-0300">This means this article, as far as I can tell from parsing the source code, would have its full text end up in the search results, even though it’s been updated in 2024 and includes the events that are supposed to be forecasted (it might be filtered out by something else, but I can’t seem to find any handling of modified articles).
Generally, data contamination is a huge issue for retrodiction, so I am assuming you have done something good here, otherwise it seems very likely your results are inflated because of those data contamination issues, and we should basically dismiss the results of your technical report.
To be clear, I did not do any cherry-picking of data here. The very first search query on any topic that I tried was the search I document above.
Yes, the Google 'search by date' is basically completely busted and has been for a while (even though Google possesses the capability to date content accurately by using their Internet-wide snapshot archive going back to the 1990s, whose existence was recently publicly confirmed by the ad API leak). For kicks, try searching things like "Xi Jinping" with date-ranges like 2013... It is most regrettable, as it used to be a useful tool for me in digging up old stuff. There also seem to be issues in the other direction, where Google is 'forgetting' old articles which aren't being indexed at all, apparently, in any publicly-searchable fashion (which might be contributing to the former, by a base rates screening-paradox effect - if all the real old articles have been forgotten by the index, then only erroneously timestamped hits will be available). I'm not aware of any search engine whose date cutoff is truly reliable. Even if they were, you would still have to manually check and clean to be sure that things like sidebars or recommendations were not causing data leakage.
I also agree that if this is really the only countermeasure to data leakage OP has taken, then the results seem dead on arrival. ML models infamously ruthlessly exploit far subtler forms of temporal data leakage than this...
It sounds like I'll be waiting for some actually out-of-sample forecasting numbers before I believe any claims about beating Metaculus etc.
(This is a surprising mistake for a benchmarking expert to make. Even if you knew nothing about the specific problems with date-range search, it should be obvious how even with completely unedited, static snapshots from the past, that there would be leakage - like results will rank higher or lower based on future events. If Israel attacked Iran, obviously all articles before arguing that Israel will/should/could attack Iran are going to benefit from being 'right' and ranked higher than articles arguing the opposite, many of which will outright quietly disappear & cease to be mentioned, and a LLM conditioned on those rather than the lower-ranking ones will automatically & correctly 'predict' more accurately. And countless other leakages like that, which are not fixed as easily as "just download a snapshot from the IA".)
4
1Danny Halawi says there is lower performance on a different set of more heldout predictions, and the claims about GPT-4 knowledge cutoff are probably wrong:
The results in "LLMs Are Superhuman Forecasters" don't hold when given another set of forecasting questions. I used their codebase (models, prompts, retrieval, etc.) to evaluate a new set of 324 questions—all opened after November 2023.
Findings:
- Their Brier score: 0.195
- Crowd Brier score: 0.141 [lower=better]
First issue:
The authors assumed that GPT-4o/GPT-4o-mini has a knowledge cut-off date of October 2023.
However, this is not correct.
For example, GPT-4o knows that Mike Johnson replaced Kevin McCarthy as speaker of the house.
- This event happened at the end of October.
- This also happens to be a question in the Metaculus dataset.
2
1Update: Bots are still beaten by human forecasting teams/superforecasters/centaurs on truly heldout Metaculus problems as of early 2025: https://www.metaculus.com/notebooks/38673/q1-ai-benchmarking-results/
A useful & readable discussion of various methodological problems (including the date-range search problems above) which render all forecasting backtesting dead on arrival (IMO) was recently compiled as "Pitfalls in Evaluating Language Model Forecasters", Paleka et al 2025, and is worth reading if you are at all interested in the topic.
Seems worth mentioning SOTA, which is https://futuresearch.ai/. Based on the competence & epistemics of Futuresearch team and their bot get very strong but not superhuman performance, roll to disbelieve this demo is actually way better and predicts future events at superhuman level.
Also I think it is a generally bad to not mention or compare to SOTA but just cite your own prior work. Shame.
2Can you explain what's your definition of "accuracy"? (the 87.7% figure)
Does it correspond to some proper scoring rule?
How much does it cost to run one instance of this right now?
One obvious idea is to build integrations for Manifold, Fatebook, Metaculus etc where you automatically have this bot give an answer. I'm curious how much that costs when you're just doing it all over the place.
(I don't know that I believe the current generation of bots are as good as implied here for reasons Habryka/Gwern are saying, but, regardless, I expect at least pretty decent bots of this form to be available within a year if they aren't already, and for it to be useful for various sites to have SOTA forecasting bots plugged into them if they're at least nontrivially better than chance)
Some additional thoughts:
A thing that is different from a forecasting bot, is an "operationalization" bot, that scans threads with disagreements for plausible operationalizations of the disagreement.
I think good discourse is partly bottlenecked on "it's pretty expensive and annoying to operationalize many disagreements, in ways that actually help get to the heart of the matter."
I'd be pretty interested in experimenting with operationalization-bot and forecasting-bot combos (once they get sufficiently good to not be annoying), that scan disagreements, suggest little cloud-bubbles of "is this a cruxy operationalization you could bet on?", which maybe disappear if nobody clicks "yep".
Then, people are encouraged to put their own probabilities on it, and then, afterwards (to avoid anchoring), a forecasting bot gives it's own guess.
