Pitfalls in Evaluating Language Model Forecasters

## Abstract

Abstract Large language models (LLMs) have recently been applied to forecasting tasks, with some works claiming these systems match or exceed human performance. In this paper, we argue that, as a community, we should be careful about such conclusions as evaluating LLM forecasters presents unique challenges.
We identify two broad categories of issues: (1) difficulty in trusting evaluation results due to many forms of temporal leakage, and (2) difficulty in extrapolating from evaluation performance to real-world forecasting. Through systematic analysis and concrete examples from prior work, we demonstrate how evaluation flaws can raise concerns about current and future performance claims. We argue that more rigorous evaluation methodologies are needed to confidently assess the forecasting abilities of LLMs.

## 1 Introduction

Forecasting, the task of assigning probabilities to future events, represents a critical capability for decision-making across various domains.
Several recent studies have explored the potential of
Large Language Models (LLMs) as forecasting systems, sometimes even suggesting that LLMs can already rival human performance (Halawi et al., 2024; Phan et al., 2024; Schoenegger et al., 2024).

However, this paper identifies significant concerns in the trustworthiness of reported results for both existing and future LLM forecasting systems. We expand on several issues that are partially known in the forecasting community (e.g., (Bosse et al., 2024; Sempere and Lawsen, 2021; Arnott et al., 2018)), but have not been comprehensively analyzed for LLM forecasters.
Through concrete examples, we illustrate subtle challenges in forecasting evaluation, and discuss how these issues may have led to overly optimistic assessments of LLM forecasting abilities in prior work.
The challenges we identify fall into two broad categories:

- 1.
*Trusting evaluation results*: Various forms of data leakage make it difficult to ensure that models truly predict future events rather than using information after the forecast date.
- 2.
*Extrapolating from evaluation results to real-world performance*: Good performance on forecasting benchmarks may not necessarily correspond to good forecasting abilities due to issues like benchmark gaming and distributional biases.

We provide an overview of these challenges below, and analyze them in more depth in Sections [2](https://arxiv.org/html/2506.00723v1#S2)-[3](https://arxiv.org/html/2506.00723v1#S3).

#### Challenge 1: Establishing trustworthy evaluation results.

The gold standard for evaluating a forecaster involves running it on unresolved questions, waiting until the questions resolve, and then scoring the predictions. However, this approach is impractical for rapid model evaluation. Thus, researchers typically resort to *backtesting* or *retrodiction* (Zou et al., 2022), where the forecasting system is given knowledge as of some past time $T$ and asked to forecast events between time $T$ and the present. Although appealing in principle, backtesting introduces several issues:

- •
*Logical leakage:*
The very nature of backtesting can logically constrain possible answers. Consider a time-traveler analogy: If someone from 2035 asks you to predict if we will find alien life before 2040, you can deduce that the answer must be "yes"–otherwise, the time traveler would not yet have definitive evidence to grade your prediction. We show that a significant percentage of questions in prior forecasting benchmarks permit similar logical deductions of the correct answer.
- •
*Unreliable date-restricted retrieval*:
Many forecasting systems incorporate retrieval components (e.g., search engines) that are restricted to data available at time $T$. Yet, date metadata on documents is often inaccurate, allowing future data to leak into the system. More subtly, the retrieval model itself has typically been trained on future data, causing leakage through learned associations. For example, searching for "January 6th" with a date restriction to 2020 returns documents with an abnormally strong association to U.S. politics (by 2020 standards).
- •
*Over-reliance on model cutoff dates*: Researchers often assume that models have no knowledge beyond their reported training cutoff dates. However, cutoffs are more of a guideline than a guarantee, and evidence suggests that models possess knowledge of some events beyond these dates.

#### Challenge 2: Extrapolating from benchmark performance.

Even with a sound evaluation, translating results into real-world forecasting ability faces additional issues:

- •
*Piggybacking on human forecasts*:
Many forecasting datasets originate from human prediction platforms. Thus, human forecasts are likely available to the LLM (in its training data or through its retrieval system).
Claims that LLMs “match human performance” may then be circular: the models might simply be copying human forecasts rather than demonstrating independent ability.
- •
*Gaming benchmarks through betting*:
Unlike many AI tasks, forecasting benchmarks can reward strategic gambling over accurate uncertainty estimation. For example, consider forecasting U.S. politics in 2025, from a vantage point in 2023 with a 50/50 prior on the presidential election. The benchmark-optimal strategy might be to commit to one outcome and condition all predictions on that guess. With 50% chance, this yields excellent performance; with 50% chance, it fails completely. This has a much higher chance of topping the benchmark than a calibrated strategy.
- •
*Skewed data distribution*:
Questions on forecasting platforms often focus on topics that competitive forecasters find interesting, creating potential distribution biases. When curating benchmarks for backtesting, these biases can be exacerbated by constraints on which questions can be resolved within the evaluation timeframe.
Although data biases exist in many ML benchmarks (e.g., ImageNet’s focus on dog breeds still produces transferable visual features), there is little evidence that performance on current forecasting benchmarks yields generalizable forecasting capabilities.
- •
*Metrics are counterintuitive*: Typical metrics used to evaluate forecasters can fail to incentivize good forecasting. For example, calibration can penalize useful forecasting, while Brier scores over-emphasize performance on
questions with a high base rate.

#### Looking ahead: Challenges in optimizing better forecasters.

Current LLM forecasters primarily leverage existing models’ knowledge and reasoning capabilities. As the field advances, a natural next step will be applying optimization pressure specifically to improve forecasting performance.
However, temporal correlations in data make this optimization challenging. Naively training on question-answer pairs over a time period creates temporal leakage, as early samples in training can leak information relevant to later ones (e.g., "Who will win the election?" followed by "Who will win the primaries?").
Temporally sorting training samples still fails to properly simulate the task of predicting events further in the future.

By developing these arguments, we argue that the monitoring of forecasting capabilities is appreciably harder than the evaluation of knowledge about the past or present,
as it has unique issues on top of all the existing issues with machine learning evaluation (Leech et al., 2024).
Forecasting future events is a fascinating and challenging task for LLMs, with wide-ranging implications, and we hope to convince the community to devote more attention to evaluating it carefully.

## 2 Challenge 1: Establishing Trustworthy Evaluation Results

The ideal way to evaluate a forecasting system is to ask questions about future events, collect predictions, and then score the model’s answers as the events resolve over time. However, such an evaluation can last months or years. Instead, researchers rely on *backtests*, which constrains a system to only access knowledge at some past time $T$, and then ask it questions that resolved between time $T$ and the present (Tashman, 2000). This allows for immediate feedback, which is essential for rapid iteration and to quickly compare new releases. However, the assumption that the system does not have information after the chosen time $T$ is often violated in subtle ways.

### 2.1 Issue 1: Logical Leakage of Outcomes in Backtesting

When backtesting forecasters, we select (or generate) forecasting questions at some past time $T$ *with knowledge of what the future holds.*
Natural strategies for selecting forecasting questions can thus implicitly leak information about the future, if the forecaster knows that it is being backtested.

Knowledge of the backtesting date can leak outcomes.
It may seem reasonable to collect questions from human forecasting platforms, with the restriction that (1) the question was formulated before time $T$; and (2) the question has resolved in the present.
Yet, the latter condition can implicitly leak the answer: Suppose in 2021 we asked “Will Queen Elizabeth live to be 100 years old?”. If we want to backtest a forecaster today in 2025, this seems like a great sample, since we now know that the true answer is “no”. But if the forecaster knows that it is being evaluated in 2025, then it can deduce that the correct answer cannot be “yes”, as Queen Elizabeth would have turned 100 in 2026.

Back-generated questions exhibit biases.
This issue is exacerbated when questions for backtesting are generated after the fact; (say, in 2025 we aim to generate forecasting questions that *could have been asked* in 2024, together with correct answers.), as done in Dai et al. (2025) and Paleka et al. (2024). These works use news reports to generate questions about “future” events from the perspective of the backtested model.
However, the news is biased towards events that occur and rarely reports uninteresting events that unsurprisingly did not occur. The interesting incidents it reports can sometimes be too specific to be a reasonable forecasting question to ask before the incident occurred. This is also related to *survivorship bias*, popularly studied in financial trading (Gruber and Blake, 1996).
Consider a company that shuts down in Q1 2025. Post-shutdown, fewer news articles will be written about this company, reducing the likelihood that question-generation procedures will create backtesting questions about its 2025 outcomes. This creates a systematic difference between backtest and live-test question distributions: in 2024, forecasters could reasonably have been asked numerous questions about the company’s 2025 performance.
However, a question like “What will the company’s Q3 2025 revenue be?” (correct answer: $0) represents a valid forecasting target from the 2024 perspective, but is unlikely to appear in datasets generated from news data in 2025.

Empirical evidence.
We analyze forecasting benchmarks from Dai et al. (2025); Paleka et al. (2024); Halawi et al. (2024); Tao et al. (2025),
finding that logical leakage is a practical concern.

Halawi et al. (2024) select questions that resolved during the time window from June 2023 to
January 2024, but do not filter out questions that
may not have resolved in that window (such as “Will Sudan experience a civil war before 2036?”). We find that at least 3.8% of their dataset consists of questions for events that resolved “early” and where hence no forecasting is needed.
Similarly, the dataset in Tao et al. (2025) consists of questions that resolved by August 2024, with at least 10% of questions
being trivial to forecast given that they resolve at that time.
Dai et al. (2025) and Paleka et al. (2024) curate news articles to retroactively generate potential forecasting questions. We find that such questions often contain shortcuts, allowing even weak classifiers to obtain high accuracies over 80% on binary questions created by  Dai et al. (2025) (see details in [Appendix C](https://arxiv.org/html/2506.00723v1#A3)).

Possible solutions.
Forecasting questions collected for backtesting should be restricted to those where *every possible resolution* of the question can be validated at the time of evaluation. Of course, this may limit the number of available questions, which is already an issue (see Appendix [B](https://arxiv.org/html/2506.00723v1#A2)).

When generating questions retroactively, great care should be taken to ensure that the questions *reflect the type of forecasts that could plausibly have been asked in the past*.
A partial fix is presented in (Paleka et al., 2024; Dai et al., 2025), where their news-generated dataset is augmented with slight modifications to the questions to create similar-looking questions that resolve to the opposite outcome.
Even then, we find that this process creates overly specific questions that would never be asked as a forecasting question before the event occurred.
An example from Dai et al. (2025) is “Will a body be found inside a trash can on the 20400 block of Omira Street in Detroit in early November 2024?”
We estimate over 90% of their dataset consists of such questions, though this judgment is inherently subjective, and better quantitative measures of this effect are needed.

### 2.2 Issue 2: Unreliable Date-restricted Retrieval

The ability to retrieve relevant and up-to-date information
is of critical importance for building a performant forecasting system (Bosse et al., 2024).
As a result, LLM forecasting systems are typically designed with access to some retrieval system (Phan et al., 2024; Halawi et al., 2024).

Many search engines do not robustly implement date restrictions.
When backtesting a forecaster, we have to ensure that the retrieval system is properly restricted to information available at the backtested time $T$. Unfortunately, this is challenging to do reliably with modern search engines.
While multiple search engines (such as Google, DuckDuckGo, and Bing) support restricting search results to a specific time period, this feature is highly unreliable (as we evidence below), due to: (1) web pages being updated over time without changing their reported publication date; (2) retrieved pages containing adjacent elements like comments, ads, or sidebars that reflect present knowledge; or (3) the search engine simply not having reliable information about when a page was first published.

Figure: (a) Search results for “jan 6” with date restriction before 2020. The first result is incorrectly dated, leaking future information. The second result shows more subtle temporal leakage: the article discusses the mechanics of the Electoral College, which is only relevant for this query due to the events on Jan 6th, 2021.
Refer to caption: extracted/6497810/imgs/jan6_google_clean.png

Retrieval systems may rely on future knowledge to select and rank past data.
A more subtle issue is that the retrieval engine may rely on algorithms or knowledge developed after the evaluation time $T$, and this can bias the retrieval results based on “future” information. For example, when we ask Google to return results from 2022 only, it does not use the search engine *algorithm and models* from 2022. As a result, as hypothesized by Branwen (2024), articles that became significant after the evaluation time $T$ may be ranked higher than they would have been at the time $T$.

Empirical evidence.
We collect a number of examples of possible information leakage through date-restricted retrieval systems.
While some of these examples are clear-cut (e.g., the search engine returns a page that contains data from after the restricted data range), others we can only (strongly) hypothesize to be evidence of leakage (e.g., biases in search results based on future knowledge).

Figure [3](https://arxiv.org/html/2506.00723v1#A1.F3) in Appendix [A](https://arxiv.org/html/2506.00723v1#A1) shows an example where a search for the Nobel Peace Prize winner “Nihon Hidankyo” restricted to January-September 2024 returns a result with a claimed publication date of 14 January 2024, yet highlighting the Nobel Prize (which was announced in October 2024).

Examples of more subtle leakage through retrieval biases are in Figure [1](https://arxiv.org/html/2506.00723v1#S2.F1).
Here, a search for “jan 6” restricted to articles before 2020 returns highly ranked search results related to U.S. politics, even though the strong association of that date with U.S. politics only emerged in January 2021. Similarly, a search for “wuhan” restricted to articles from before the COVID-19 pandemic features prominent search results about Wuhan’s Institute of Virology which only later gained international fame.
Additional examples of such leakages are in Figure [4](https://arxiv.org/html/2506.00723v1#A1.F4) in the appendix.

A related well-known issue in backtests for financial trading is how historic fundamentals data might be contaminated from an update made at a later point. Breitschwerdt (2015) highlight how Enron drastically changed its 1998-2001 earnings in 2002, and many data repositories do not report the original values anymore.

Possible solutions.
A simple heuristic, implemented in (Phan et al., 2024), is to apply a filter on top of the retrieval
process to discard articles that obviously contain information past the specified retrieval date. However, this is imperfect, not only because of false negatives seeping through the filter, but also because the article might have reported contradictory information to the actual resolution before the update, which could have affected a forecaster’s prediction negatively, but now just gets filtered out leading to its capability being overestimated.
One robust way to resolve the issue of incorrect date restrictions is to implement a more restricted retrieval system (e.g., which only returns results from Wikipedia and news sources with clear and reliable dates).
Another option could be to maintain an index of high-quality web information for different time periods and, search over this corpus for the forecaster retrieval process.

The issue of leakage through retrieval biases appears difficult to solve with filters.
Here, a robust solution would require either a retrieval system that does *not* incorporate knowledge, such as a simple TF-IDF system; or a search algorithm using *older embedding models*.
Of course, this may result in much worse retrieval performance.

### 2.3 Issue 3: Over-reliance on Model Cutoff Dates

Model creators generally report a “knowledge cutoff date” for their model (see Figure [7](https://arxiv.org/html/2506.00723v1#A2.F7)), after which the model’s knowledge is not updated. This is useful for backtesting, as it allows us to test the model’s performance on a held-out dataset of events that occurred after the cutoff date.

Knowledge cutoff dates are unreliable.
Model creators do not report a knowledge cutoff date for purposes of test/train separation in forecasting evaluation.
Rather, it is to inform users about the date after which the model outputs can be unreliable.
Thus, the knowledge cutoff date is not to be taken as a guarantee that the model will not have access to information after the date (Halawi, 2024).

The meaning of the data cutoff can also vary by model developer, and may refer only to the pretraining data, with later-collected user preference data leaking a small amount of information about the future. Some model families (e.g., Mistral) do not report any explicit cutoff date (Wang, 2025).

Empirical evidence.
The GPT-4o model from August 2024 (when asked) says it has a knowledge cutoff of October 2023 (see Figure [2(a)](https://arxiv.org/html/2506.00723v1#S2.F2.sf1)); this aligns with OpenAI official documentation.
Furthermore, it behaves consistently with this cutoff: it denies knowledge of any events from November 2023 onward.
However, in Figure [2(b)](https://arxiv.org/html/2506.00723v1#S2.F2.sf2), we see that system prompting the model with the hint that its knowledge cutoff is in November 2023 manages to elicit information about the Biden-Xi meeting on November 15th, 2023, which was not announced until November 8th, 2023 (Times, 2023).
(^1^11We cannot, however, confirm this as a leakage, because a potential visit to the US by Xi Jinping in November 2023 was already discussed in October 2023 (Reuters, 2023) and the model could have guessed the correct date (Nov 15) as the most likely date for the meeting.)

Figure: (a) Standard response consistent with the knowledge cutoff of October 2023.

Even in cases where a training cutoff date is correct for the main model, system prompts and other scaffolding of the model can leak information.
For instance the system prompt of Anthropic’s Claude.AI (knowledge cutoff without search: November 2024) reportedly contains the snippets “Donald Trump is the current president of the United States and was inaugurated on January 20, 2025.” and “semiconductor export restrictions 2025” (Johnson, 2025).

Possible solutions.
The *release date* of a model is a strict upper bound on the knowledge cutoff date; except in cases of system prompt updates as described above.
The vast majority of model providers version their models in a way where the weights do not change after the release date; and in open-weight models, this is trivial.

As the example in Figure [2(b)](https://arxiv.org/html/2506.00723v1#S2.F2.sf2) shows, the actual knowledge cutoff may be up to a few months after the stated cutoff date. Thus, having a few-month buffer is prudent and might resolve this issue in practice.
However, due to knowledge cutoff dates getting closer to the release dates (Figure [7](https://arxiv.org/html/2506.00723v1#A2.F7)), this might reduce to the release date solution above.

## 3 Challenge 2: Extrapolating From Benchmark Performance

The purpose of benchmarks is to demonstrate improvements in the system being evaluated. Ideally, a higher ranking on the benchmark should translate to improved capabilities in real-world forecasts. We discuss subtle ways in which this property may not hold on forecasting benchmarks.

### 3.1 Issue 1: Piggybacking on Human Forecasts

Human forecasts may be contained in training data or retrieval system responses.
When comparing LLM forecasting predictions with human performance, it is important to check whether the human baseline predictions are available to the model.
Since many benchmarks consist of questions scraped from prediction market websites, this data could easily leak into a model’s training set, or into results of a retrieval system.

Empirical evidence.
Many questions that resolve in a certain period were already being forecast by people for a long time before that. For example, the question
“Will AI get at least bronze on the International Math Olympiad by end of 2025?” on Manifold Markets has had many people bet and explain their reasoning since May 2023.
This makes an "LLM vs market of human forecasters" comparison circular, because the LLM can just copy the market probabilities.
Even when comparing across LLMs with similar knowledge cutoff dates, this can be an issue as one LLM might have access to more knowledge on existing human forecasts than another.

This issue can affect interpretation of scores on ForecastBench (Karger et al., 2024a), which uses human-crowd prediction as the gold standard for measuring LLM capability on unresolved questions. If an LLM forecasting system retrieves the relevant prediction market and recent crowd aggregates, it can trivially achieve gold standard performance.

Possible solutions.
Human forecasters can also see the current crowd aggregate forecast on a market before making their own predictions.
The crucial difference is that the incentives for human predictors are to be *better* than the crowd, on average.
We thus propose that (ambitious) forecasting benchmarks should measure the edge that the system has over the human crowd; then, past market data can even be made explicitly available to the LLM when backtesting.

### 3.2 Issue 2: Gaming Benchmarks Through Betting

Real-world prediction contests such as the ACX/Metaculus Prediction Contest (Metaculus, 2025; Hanania, 2022) are often accompanied with monetary prizes for the best performers.
Similarly, forecasting models and scaffolds that perform the best are likely to be selected to be used further.
When designing machine learning benchmarks, models with the best score should ideally be ones that maximize general capability on the task. This can break down in forecasting evaluation due to the large degree of correlated stochasticity of the real world.

Maximizing chance of being the best predictor does not elicit the best forecasting system.
It is known that, in a theoretical prediction market contest, maximizing the chance of being the best predictor encourages taking correlated risks over betting based on one’s actual honest beliefs (Sempere and Lawsen, 2021).
To illustrate, the winner of a forecasting contest in 2022 said: “I tried to deliberately structure my answers to maximize my probability of winning, rather than maximize the probability of each individual answer being correct.” (Alexander, 2023)

A similar dynamic might occur in benchmarking LLM forecasters.
Consider a forecasting system set in September 2024, predicting political and economic events resolving in 2025, such as
“Will the US government resume collecting student loan payments in 2025?”.
There is a key latent variable that correlates with the outcome of many of these questions: the outcome of the 2024 US presidential election.
A good LLM forecaster reporting its true probabilities would likely estimate $P(\cdot\mid\text{Republican win})$ and $P(\cdot\mid\text{Democrat win})$ for all questions, and average out its predictions over the two possible outcomes.
On the contrary, a forecaster that wants to maximize its chance of performing very well on this dataset should just assume that the outcome of the 2024 election is certain.(^2^22They could either bet on the outcome they consider most likely a priori, or consider which of the two outcomes (Republican win or Democrat win) *may lead to less stochasticity*, thereby enabling better predictions.)
This creates a winner’s curse problem: when benchmarking many LLMs with different biases, the top performer is likely overestimated, having achieved its ranking through systematic overconfidence rather than superior forecasting ability.

Empirical evidence.
Sempere and Lawsen (2021) show that this issue appears in human forecasting contests even when the question outcomes are not correlated. Forecasters can increase their chance of performing well either by betting confidently on key latent variables, or by trying to place bets contradicting other forecasters(^3^33 The winner in Alexander (2023) again reports: “…my model of win probability was (probability of predictions being accurate) / (local density of competitors with similar predictions to me)…”). In [Appendix E](https://arxiv.org/html/2506.00723v1#A5), we formalize the tradeoff between win probability and honest predictions on a toy example of a dataset of questions with a shared latent variable.
It remains unclear if current LLM forecasters resort to such “consistent confidence” strategies, as the models are not very consistent (Paleka et al., 2024). However, similar effects have been highlighted before in the financial trading literature, where high variance strategies can perform strongly in a fixed period or back-test, but eventually revert to the mean later (Sharpe, 1964).

Possible solutions.
Ultimately, this problem is due to the questions about the world in a given period being correlated and hence the performance of a forecaster resting on few correct guesses.
While prediction market datasets are limited ([Appendix B](https://arxiv.org/html/2506.00723v1#A2)), we can in principle generate larger synthetic datasets of questions.
However, some key events such as the COVID-19 pandemic are likely to affect virtually all questions with real-world relevance.

In financial trading, a popular approach is to report risk-adjusted returns (Sharpe, 1964), where one estimates both the mean performance and variance (Simons, 1998). Ideally one should evaluate forecasters on multiple disjoint backtesting periods (Bailey et al., 2015). A forecaster that places high-variance bets will have a lower chance of performing well in multiple evaluations.
Note that it is not enough to change the period in which the questions resolve; we also need to change the backtesting date, so that, for example, the 2024 US presidential election is a relevant latent variable for only one of the backtesting periods.

### 3.3 Issue 3: Skewed Data Distributions

If the data distribution used for benchmarking forecasters has a specific skew, it is unclear if benchmark performance would be predictive of general real-world forecasting.
Note that this issue generally affects any benchmark: e.g., a model that performs well on ImageNet does not imply the model has good vision abilities in general (and certainly not that the model matches human vision abilities). But we argue that distribution skews may be particularly problematic for current forecasting benchmarks.

Table: Table 1: Distribution of ForecastBench questions across domains and data sources, table borrowed from the Appendix of Karger et al. (2024a). Users on each market favor specific categories, over-weighing them when market questions are used for benchmarking. Further, ForecastBench questions from non-market sources all follow highly specific templates akin to time series prediction.

Benchmarks are skewed towards narrow topics.
The use of LLMs was originally motivated for *judgmental forecasting* about discrete events (Zou et al., 2022), where classical time series models without language understanding cannot be applied directly.
However, benchmarks sourced from prediction markets tend to exhibit domain-specific skews that reflect the interests of their user base.
Polymarket, for example, is disproportionately focused on cryptocurrency price movements and sports results,
while Manifold includes a large number of personal questions such as “Will I go to the gym today?”.
More generally, markets tend to overrepresent US-centric political, economic, and sports events.

Empirical evidence.
Table [1](https://arxiv.org/html/2506.00723v1#S3.T1) shows the distribution of questions across categories and data sources in ForecastBench (Karger et al., 2024a),
which is heavily skewed towards topics in Security & Defense.
Moreover, we find that questions from non-market sources follow only a few basic templates, listed in Figure [8](https://arxiv.org/html/2506.00723v1#A6.F8), which heavily focus on time-series predictions.
For example, a large number of questions are sourced from a database of global conflict statistics (ACLED), and require
predicting increases in conflicts in particular regions and time periods.

Possible solutions. Rather than scrape questions from prediction markets, some recent works (e.g., (Dai et al., 2025; Paleka et al., 2024))
generate synthetic backtesting questions based on contemporary news articles. This can allow more control over the data distribution while sampling a large number of questions for forecasting. Of course, one is then limited to the types of events that are reported in the news, which can miss important developments, and introduces potential leakage (see Section [2.1](https://arxiv.org/html/2506.00723v1#S2.SS1)). We discuss further issues with datasets created in this way in Appendix [C](https://arxiv.org/html/2506.00723v1#A3).

### 3.4 Issue 4: Commonly used Forecasting Metrics are Imperfect

Evaluations of AI forecasters usually report up to four different metrics:

- •
*Brier score*: if $p$ is the predicted probability that the question resolves "Yes", and $y$ is 1 if the outcome is "Yes" and 0 otherwise, the Brier score is $(p-y)^{2}$ (a lower score is better).
- •
*logarithmic score*: with $p$ and $y$ as above, the logarithmic score is $y\log p+(1-y)\log(1-p)$.
- •
*accuracy*: let $q$ be $1$ if $p\geq 0.5$ and 0 0 otherwise; report the mean accuracy where $q$ are the predictions and $y$ are the true outcomes.
- •
*calibration*: consider all questions where the forecaster predicts a probability close to $p$; the forecaster has good calibration if the proportion of questions where the outcome is "Yes" is close to $p$. It is usually measured over “bins” of questions based on the predicted probability.

Calibration can penalize useful forecasting.
As mentioned in Rischel (2023), on a dataset of questions with low prior probability, predicting the base rate results in better calibration than actually trying to predict the correct model.
For example, imagine a conclave with 100 living cardinals, only one of which will actually become Pope, and a dataset of 100 questions asking ‘Will Cardinal X become Pope?’.
A base rate forecaster who simply assigns every cardinal the 1 percent chance of becoming Pope is perfectly calibrated on this tiny dataset: the sole nonempty bin is the 1-percent bin, and it contains one success out of 100 tries, for an empirical rate of 1 percent.

By contrast, a more discerning forecaster who spreads the probability mass more realistically – e.g. giving 10 percent to five plausible “front-runners” (including the eventual Pope!) and about 0.5 percent to the remaining 95 – has much worse calibration:
the 10-percent bin contains one success out of five (for an empirical rate of 20 percent), and the 0.5-percent bin contains 0 percent success.
But, the latter forecast was clearly more useful!
A similar phenomenon occurs even on an uncorrelated dataset, as in the example in (Rischel, 2023).

Accuracy is not a strictly proper scoring rule.
Some papers (Zou et al., 2022; Halawi et al., 2024; Hsieh et al., 2024) report accuracy among other metrics.
This metric does not incentivize reporting honest probabilities
(Savage, 1971);
and does not measure forecasting performance on events that have low or high reference class probability.
Accuracy of forecasting may be useful as a sanity check, but never as a primary metric on binary questions.

Brier scores are not comparable across different base rates.
Forecasting datasets typically mix questions with very different base rates, e.g., questions that are “coin tosses” such as “Who will win the U.S. presidential election”, and questions such as “Will an earthquake of magnitude >9.3 occur in 2025” (3 in the last century).

For a forecaster who always predicts the (correct) base rate $b$ of a question, the expected Brier score is $b(1-b)$.
The peculiarities of this score mean that no amount of skill in forecasting very rare events can make up for a deficiency in discriminating 40% chance events from 60% chance events.
Consider a concrete example: in a dataset with 50 questions with 50% base rate and 50 questions about rare events with 5% base rate, a forecaster who achieves perfect forecasting on all rare events (reducing the Brier score from 0.0475 to 0 on those questions) but performs at baseline for coin-flip questions would earn a total Brier score of about 0.125. Meanwhile, a forecaster who is clueless about rare events but improves coin-flip questions from baseline 0.25 to 0.15 would earn a total Brier score of about 0.1—appearing significantly better.
This means that a benchmark reporting an average Brier score will select for the latter much more than the former.

Label noise makes scoring rules not strictly proper.
Forecasting datasets—either collected from prediction markets or synthetically generated—are likely to have some *label noise* (i.e., incorrect or ambiguous resolutions).
In Appendix [D](https://arxiv.org/html/2506.00723v1#A4), we show that constant label noise can make “clamping” probability estimates give a better logarithmic score than reporting the true probability.

Label noise is an issue in practice. For example, the real-money prediction platform Polymarket has repeatedly had mistaken or controversial resolutions (Gerlacher, 2024).
In synthetically generated questions, resolution is often verified with retrieval systems, which can make mistakes:
Paleka et al. (2024) report a 1-5% error rate when resolving questions using Perplexity-based retrieval.

Possible solutions.
No commonly used metric is without issues, and they weigh different aspects of forecasting performance differently; we recommend computing at least Brier score, calibration, and logarithmic score. If a forecasting system outperforms on all of those, its information advantage over other forecasting systems (including humans) should be reported, such as market profits.

## 4 Looking Ahead: Challenges in Optimizing Better Forecasters

It is natural to ask whether we can turn backtesting (if implemented without the issues in [Section 2](https://arxiv.org/html/2506.00723v1#S2))
into a learning task that improves models’ forecasting ability.
Yet, the best-performing LLM forecasters so far have mostly just relied on base models without any forecasting-specific learning (Halawi et al., 2024; Karger et al., 2024a; Phan et al., 2024).
In this section, we discuss
a key issue that makes it challenging to use backtesting as a training objective.

Optimization confounds the training objective during training.
In standard machine learning, we split data into training and test sets in no particular order.
In backtesting, we have to split data *temporally*, where all points in the training set are before all points in the test set.
This way, we ensure the predictions on the test set are clean and use no future information.

However, optimizing on a backtesting dataset creates a subtle leakage problem. If we optimize the model by updating on an ordered set of events $e_{1},\ldots,e_{n}$, then when predicting event $e_{i+1}$, the model parameters already encode information about events $e_{1},\ldots,e_{i}$.
This means we are no longer testing the model’s ability to predict $e_{i+1}$ from the original cutoff date, but rather its ability to predict $e_{i+1}$ given some learned information about earlier events in the sequence.

Possible solutions.
Sorting the events by date might look like a solution, as the model will only ever remember earlier events when predicting later events. However, this only teaches the model to predict on shorter time horizons.
Ideally, we want a way to penalize memorization, forcing the model to learn forecasting without learning what specific events happened.

## 5 Discussion and Conclusion

The primary purpose of public benchmarks is to rank models for users (Hardt, 2025). After all, as we discussed in [Section 3.4](https://arxiv.org/html/2506.00723v1#S3.SS4), absolute scores are hard to interpret as they depend on the data distribution. Some issues we highlighted, such as backtest questions being trivially answerable in  [Section 2.1](https://arxiv.org/html/2506.00723v1#S2.SS1), might not affect relative comparisons. However, the extent to which different systems exploit benchmark shortcomings can vary, and thus rankings could still be affected.

We also do not have proof that the benchmark issues we uncover would lower the performance claims of LLM forecasters. But we argue it is challenging to trust these evaluations because LLM forecasters *could* have gamed the evaluation through various shortcuts.
Such exploitation is not necessarily adversarial, it can be an unintended result of trying to improve benchmark performance.

Better evaluations of forecasting that mitigate some of the issues described here are possible, and progress is already being made. For example, ForecastBench does show fewer obvious temporal and logical leakage issues compared to other attempts, as we discuss in [Section 2.1](https://arxiv.org/html/2506.00723v1#S2.SS1) and [Appendix C](https://arxiv.org/html/2506.00723v1#A3).
We recommend future evaluations to follow recommendations made throughout this paper, and to collect their own questions about events that are as recent as possible.
Much can also be learned from the financial trading literature, where related issues have been discovered and mitigated over the last few decades (Arnott et al., 2018).
Ideally, live evaluations on prediction markets with the goal of making profits should be performed for the final forecasting system or research claims, with detailed performance reports across topics and forecast time-horizons.

Conclusion.   In this work, we analyzed unique issues that arise when evaluating the capabilities of language models used for forecasting future events. Through a series of concrete examples, we argue that existing data collection and evaluation practices may produce misleading results, either due to shortcuts that simplify the forecasting task, or data biases that put in doubt the general capabilities of LLM forecasters.
We hope that the potential countermeasures provided throughout this paper can inform the design of more principled evaluations for LLM forecasters.

## Acknowledgments

We would like to thank Rubi Hudson, Vineeth Bhat, Lennart Finke, Davis Brown, Gavin Leech, and Gwern Branwen for feedback on the paper.

## References

- Alexander [2023]
Scott Alexander.
Who predicted 2022?, 2023.
URL [https://www.astralcodexten.com/p/who-predicted-2022](https://www.astralcodexten.com/p/who-predicted-2022).
Accessed on 12-May-2025.
- Arnott et al. [2018]
Robert D Arnott, Campbell R Harvey, and Harry Markowitz.
A backtesting protocol in the era of machine learning.
*Available at SSRN 3275654*, 2018.
- Bailey et al. [2015]
David H Bailey, Jonathan Borwein, Marcos Lopez de Prado, and Qiji Jim Zhu.
The probability of backtest overfitting.
*Journal of Computational Finance (Risk Journals)*, 2015.
- Bosse et al. [2024]
Nikos Bosse, Peter Mühlbacher, Lawrence Phillips, and Dan Schwarz.
Contra papers claiming superhuman AI forecasting, 2024.
URL [https://www.lesswrong.com/posts/uGkRcHqatmPkvpGLq/contra-papers-claiming-superhuman-ai-forecasting](https://www.lesswrong.com/posts/uGkRcHqatmPkvpGLq/contra-papers-claiming-superhuman-ai-forecasting).
- Boudoukh et al. [2019]
Jacob Boudoukh, Ronen Israel, and Matthew Richardson.
Long-horizon predictability: a cautionary tale.
*Financial Analysts Journal*, 2019.
- Branwen [2024]
Gwern Branwen.
AI forecasting bots incoming: comment section, 2024.
URL [https://www.lesswrong.com/posts/4kuXNhPf9FBwok7tK/ai-forecasting-bots-incoming?commentId=MirX9bPg232BuzMBq](https://www.lesswrong.com/posts/4kuXNhPf9FBwok7tK/ai-forecasting-bots-incoming?commentId=MirX9bPg232BuzMBq).
Accessed 12 May 2025.
- Breitschwerdt [2015]
Ernest Breitschwerdt.
Point-in-time vs. lagged fundamentals: This time i(t)’s different?
Research report, S&P Capital IQ, Quantamental Research, 2015.
URL [https://www.spglobal.com/marketintelligence/en/documents/sp-capitaliq-quantamental-point-in-time-vs-lagged-fundamentals.pdf](https://www.spglobal.com/marketintelligence/en/documents/sp-capitaliq-quantamental-point-in-time-vs-lagged-fundamentals.pdf).
Accessed: 2025-05-27.
- Dai et al. [2025]
Hui Dai, Ryan Teehan, and Mengye Ren.
Are LLMs prescient? A continuous evaluation using daily news as the oracle.
In *ICML*, 2025.
URL [https://arxiv.org/abs/2411.08324](https://arxiv.org/abs/2411.08324).
- Gerlacher [2024]
Chris Gerlacher.
Polymarket settles a market incorrectly – again, 2024.
URL [https://predictionnews.com/news/polymarket-settles-a-market-incorrectly-again](https://predictionnews.com/news/polymarket-settles-a-market-incorrectly-again).
Accessed on 12-May-2025.
- Gruber and Blake [1996]
Elton M Gruber and C Blake.
Survivorship bias and mutual fund performance.
*Review of Financial Sttidies*, 9:1097–1120, 1996.
- Halawi [2024]
Danny Halawi.
Knowledge cutoff issues of GPT-4o regarding Phan et al. (2024), 2024.
URL [https://x.com/dannyhalawi15/status/1833295067764953397](https://x.com/dannyhalawi15/status/1833295067764953397).
- Halawi et al. [2024]
Danny Halawi, Fred Zhang, Chen Yueh-Han, and Jacob Steinhardt.
Approaching human-level forecasting with language models, 2024.
- Hanania [2022]
Richard Hanania.
Introducing the SalemCSPi forecasting tournament, 2022.
URL [https://www.cspicenter.com/p/introducing-the-salemcspi-forecasting](https://www.cspicenter.com/p/introducing-the-salemcspi-forecasting).
Accessed on 12-May-2025.
- Hardt [2025]
Moritz Hardt.
The emerging science of machine learning benchmarks.
Online at [https://mlbenchmarks.org](https://mlbenchmarks.org), 2025.
Manuscript.
- Hsieh et al. [2024]
Elvis Hsieh, Preston Fu, and Jonathan Chen.
Reasoning and tools for human-level forecasting.
*arXiv preprint arXiv:2408.12036*, 2024.
- Johnson [2025]
Asgeir Thor Johnson.
asgeirtj/system_prompts_leaks/claude.txt, 2025.
URL [https://github.com/asgeirtj/system_prompts_leaks/blob/f7d92dec4a9a9f5e4d11e4384f8239fb7ca3be05/claude.txt](https://github.com/asgeirtj/system_prompts_leaks/blob/f7d92dec4a9a9f5e4d11e4384f8239fb7ca3be05/claude.txt).
Accessed 12 May 2025.
- Karger et al. [2024a]
Ezra Karger, Houtan Bastani, Chen Yueh-Han, Zachary Jacobs, Danny Halawi, Fred Zhang, and Philip E. Tetlock.
ForecastBench: A dynamic benchmark of AI forecasting capabilities, 2024a.
URL [https://arxiv.org/abs/2409.19839](https://arxiv.org/abs/2409.19839).
- Karger et al. [2024b]
Ezra Karger, Houtan Bastani, Chen Yueh-Han, Zachary Jacobs, Danny Halawi, Fred Zhang, and Philip E. Tetlock.
forecastingresearch/forecastbench/, 2024b.
URL [https://github.com/forecastingresearch/forecastbench/tree/8e236823e3683584330fc31bf41196a81ce28626/src/helpers](https://github.com/forecastingresearch/forecastbench/tree/8e236823e3683584330fc31bf41196a81ce28626/src/helpers).
- Leech et al. [2024]
Gavin Leech, Juan J Vazquez, Niclas Kupper, Misha Yagudin, and Laurence Aitchison.
Questionable practices in machine learning.
*arXiv preprint arXiv:2407.12220*, 2024.
- Metaculus [2025]
Metaculus.
Acx2025 tournament, 2025.
URL [https://www.metaculus.com/tournament/ACX2025/](https://www.metaculus.com/tournament/ACX2025/).
Accessed on 12-May-2025.
- Paleka et al. [2024]
Daniel Paleka, Abhimanyu Pallavi Sudhir, Alejandro Alvarez, Vineeth Bhat, Adam Shen, Evan Wang, and Florian Tramèr.
Consistency checks for language model forecasters.
*arXiv preprint arXiv:2412.18544*, 2024.
- Phan et al. [2024]
Long Phan, Adam Khoja, Mantas Mazeika, and Dan Hendrycks.
LLMs are superhuman forecasters, 2024.
URL [https://drive.google.com/file/d/1Tc_xY1NM-US4mZ4OpzxrpTudyo1W4KsE](https://drive.google.com/file/d/1Tc_xY1NM-US4mZ4OpzxrpTudyo1W4KsE).
Center for AI Safety, UC Berkeley.
- Reuters [2023]
Reuters.
White House planning face-to-face meeting with Biden, Xi, 2023.
URL [https://www.reuters.com/world/white-house-planning-face-to-face-meeting-with-biden-xi-washington-post-2023-10-05/](https://www.reuters.com/world/white-house-planning-face-to-face-meeting-with-biden-xi-washington-post-2023-10-05/).
- Rischel [2023]
Eigil Fjeldgren Rischel.
Against calibration, 2023.
URL [https://erischel.com/againstcalibration/](https://erischel.com/againstcalibration/).
Accessed on 12-May-2025.
- Savage [1971]
Leonard J Savage.
Elicitation of personal probabilities and expectations.
*Journal of the American Statistical Association*, 66(336):783–801, 1971.
- Schoenegger et al. [2024]
Philipp Schoenegger, Indre Tuminauskaite, Peter S Park, Rafael Valdece Sousa Bastos, and Philip E Tetlock.
Wisdom of the silicon crowd: LLM ensemble prediction capabilities rival human crowd accuracy.
*Science Advances*, 10(45):eadp1528, 2024.
- Sempere and Lawsen [2021]
Nuño Sempere and Alex Lawsen.
Alignment problems with current forecasting platforms.
*arXiv preprint arXiv:2106.11248*, 2021.
- Sharpe [1964]
William F Sharpe.
Capital asset prices: A theory of market equilibrium under conditions of risk.
*The journal of finance*, 19(3):425–442, 1964.
- Simons [1998]
Katerina Simons.
Risk-adjusted performance of mutual funds.
*New England Economic Review*, 9:33–48, 1998.
- Tao et al. [2025]
Zhengwei Tao, Zhi Jin, Bincheng Li, Xiaoying Bai, Haiyan Zhao, Chengfeng Dou, Xiancai Chen, Jia Li, Linyu Li, and Chongyang Tao.
PROPHET: An inferable future forecasting benchmark with causal intervened likelihood estimation.
*arXiv preprint arXiv:2504.01509*, 2025.
- Tashman [2000]
Leonard J. Tashman.
Out-of-sample tests of forecasting accuracy: An analysis and review.
*International Journal of Forecasting*, 16(4):437–450, 2000.
doi: 10.1016/S0169-2070(00)00065-0.
- Times [2023]
Japan Times.
Biden, Xi talks in san francisco, 2023.
URL [https://www.japantimes.co.jp/news/2023/11/08/world/politics/biden-xi-talks-san-francisco/](https://www.japantimes.co.jp/news/2023/11/08/world/politics/biden-xi-talks-san-francisco/).
- Wang [2025]
Hao Wang.
Haooowang/llm-knowledge-cutoff-dates, 2025.
URL [https://github.com/HaoooWang/llm-knowledge-cutoff-dates/blob/f2ea76a47437c2787cc651838b5c7af4720d1c0a/README.md](https://github.com/HaoooWang/llm-knowledge-cutoff-dates/blob/f2ea76a47437c2787cc651838b5c7af4720d1c0a/README.md).
Accessed 12 May 2025.
- Wu et al. [2024]
Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, and Gholamreza Haffari.
Continual learning for large language models: A survey, 2024.
URL [https://arxiv.org/abs/2402.01364](https://arxiv.org/abs/2402.01364).
- Zou et al. [2022]
Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, and Dan Hendrycks.
Forecasting future world events with neural networks.
*arXiv preprint arXiv:2206.15474*, 2022.

## Appendix A Additional Examples of Retrieval Bias

Figure: Figure 3: Search results with date restriction showing an article with January 2024 publication date containing information about the October 2024 Nobel Peace Prize announcement. This type of temporal leakage can artificially improve forecasting performance.
Refer to caption: extracted/6497810/imgs/nihonhidankyo_google_clean.png

Here we provide additional examples of how search engines leak information when using date restrictions, expanding on the examples shown in Section [2.2](https://arxiv.org/html/2506.00723v1#S2.SS2). Even when filtering out content published after a certain date, the selection of which articles are deemed most relevant appears influenced by knowledge of future events.

Figure: (a) Search results for "October 7" with date restriction before 2022. Note the prominence of articles about conflict in the Middle East.
Refer to caption: extracted/6497810/imgs/oct7_ddg_clean.png

These examples further demonstrate how search engines with date restrictions can leak information through selection bias. The top results for searches like "October 7" (Figure [4(a)](https://arxiv.org/html/2506.00723v1#A1.F4.sf1)) feature content that would likely not be prominent before this date became associated with the Israel-Hamas conflict starting in 2023. Similarly, Figure [4(d)](https://arxiv.org/html/2506.00723v1#A1.F4.sf4) and Figure [4(c)](https://arxiv.org/html/2506.00723v1#A1.F4.sf3) shows bias toward names that would later become culturally significant.

## Appendix B Few Backtesting Questions are Available

The most popular data source for forecasting benchmarks are prediction market questions [Karger et al., 2024a, Halawi et al., 2024, Phan et al., 2024, Zou et al., 2022, Paleka et al., 2024, Tao et al., 2025].
On multiple such platforms, users can ask questions about anything they want. Hence, many questions, especially on play-money platforms like Manifold, are irrelevant personal questions (see Figure [5](https://arxiv.org/html/2506.00723v1#A2.F5)). Forecasting benchmarks should filter out such questions, either using a single cleaning prompt [Halawi et al., 2024], or using multi-step question verification [Paleka et al., 2024].

Figure: Figure 5: Manifold emphasizes Personal use-cases over News and current events, whereas the latter is more relevant when benchmarking language model forecasting.
Refer to caption: extracted/6497810/imgs/manifold_usecases.png

Figure [6](https://arxiv.org/html/2506.00723v1#A2.F6) shows the breakdown of monthly resolved Manifold questions starting July 2024 by the number of forecasters, which is a common metric to filter irrelevant questions [Halawi et al., 2024]. Manifold produced 1000-6000 questions in the second half of 2024. Some of these questions include under-specified or irrelevant questions like “Will I lift weights today?” (id: uPdSLhP0dn).
Over 50% of the questions in each month had less than 12 forecasters.
We find such prediction volume filters also lead to a large number of false negatives. Many filtered questions are perfectly reasonable for forecasting, but just happen to not attract predictions. For example, this filter systematically reduces short-horizon questions that resolve fast.

Figure: Figure 6: Resolved questions from Manifold Markets by month, with colors representing the number of forecasters that made a prediction on the question, a commonly used proxy for whether people care about the question for forecasting.
Refer to caption: extracted/6497810/imgs/resolve_month_distribution.png

Overall, these issues lead to a lower number of questions being available for forecasting evaluations each month. Next, we show how this issue is exacerbated by a recent decreasing trend in the number of months available for backtesting for frontier models.

### B.1 The Period Available for Backtesting is Narrowing

Figure: Figure 7: The gap between the knowledge cutoff and when the model is relevant is getting smaller.
Refer to caption: extracted/6497810/imgs/llm_knowledge_lag.png

For backtesting frontier models, we need questions that resolved between the model’s knowledge cutoff and today. As Figure [7](https://arxiv.org/html/2506.00723v1#A2.F7) shows, this evaluation window is shrinking: knowledge cutoffs are getting closer to release dates, and accelerated model development further reduces the time between release and today.

This narrowing backtest period creates several problems. It limits the number of available questions, which increases variance in performance estimates and reduces evaluation reliability. It also restricts testing to increasingly short-horizon predictions, but short-horizon forecasting success may not correlate with longer-horizon performance [Boudoukh et al., 2019], limiting real-world generalization. If API models adopt continuous knowledge updates [Wu et al., 2024], backtesting may become impossible entirely.

## Appendix C Issues with LLM Generated Questions from News Articles

Recent works [Dai et al., 2025, Paleka et al., 2024] have used LLMs to create forecasting questions for backtesting, using news articles as a reference. Specifically, both papers take news articles between the models existing knowledge cutoff and today, and use LLMs to generate questions for each article.
This overcomes the issue on having to rely on prediction market questions and significantly expands the distribution of topics, but comes with its own issues.

Generating binary questions from news biases the dataset towards things that happen.
Dai et al. [2025] back-generated questions from news articles, resulting in a dataset where reference class forecasting performs extremely poorly.
Concretely, here are some questions from their dataset that resolve “Yes”:

- •
Will a co-worker of Ronald Silver II share details about the unsafe working conditions of Baltimore DPW during the 2024-11-25 news conference?
- •
Will a body be found inside a trash can on the 20400 block of Omira Street in Detroit in early November 2024?
- •
Will the recall of apple juice due to high levels of inorganic arsenic expand to include multiple brands totaling 133,500 cases by September 2024?

A forecaster that knows the dataset is generated from news articles will have a much higher chance of forecasting correctly, as these questions are overly specific, to the point that any reasonable forecaster would have a high prior for saying “No” for these exact events (conjunction of many uncertain outcomes) occurring.
In general, the news tends to highlight interesting events like “schools closing in Nevada this week”, and is much less likely to mention the default state (high prior) such as “schools not closing in Washington this week”.
An incomplete fix to this issue is presented in [Paleka et al., 2024], where they augment their news-generated dataset with slight modifications to the questions to create similar-looking questions that resolve to the opposite outcome.

To what extent can forecasting questions be solved with shortcuts? Many of the issues we mentioned with LLM generated, or resolved question phrasing, can essentially be considered shortcuts that can be exploited to solve the forecasting question without any reasoning about the future. One way to quantify the extent of how much a given dataset can be solved with shortcuts is by finetuning a weak classifier on these questions. We finetune a DeBERTa model released in 2021, that has definitely not seen the test set we predict on in its training, and give it no retrieved documents. For binary Yes/No questions, we train a two class classifier after balancing the data to ensure that the constant baseline (all Yes, or all No) accuracy is 50%. For multiple choice questions with four options, we train the classifier to predict the option ID (A, B, C, or D) given the question and options in the prompt. We temporally split the data to avoid any leakage. We find this leads to high accuracies (up to 80%) on even the four choice MCQ dataset released by Dai et al. [2025], where the chance baseline is 25%. Even when we reproduce their pipeline with newer models (DeepSeek v3 0324) and improved prompts, we still achieve a four choice MCQ accuracy of 55%. The accuracy is non-trivial, but much lower on Metaculus (55%) and Manifold (59%). We believe this DeBERTa classifier only catches on easy shortcuts and does not actually engage in meaningful forecasting.

## Appendix D Clamping Improves Logarithmic Scoring Under Label Noise: A Toy Example

Here we present a mathematical analysis showing that, under label noise, logarithmic scoring does not strictly incentivize reporting the true probability.
Intuitively, if there are many questions where the true probability is very close to 0 or 1, then the logarithmic score is dominated by the noise
and will blow up as soon as some question with very high or low probability is mislabeled.

A natural counterargument to this is that, in many datasets, the questions with true probability close to 0 or 1 are rare or not particularly vulnerable to label noise.
We consider a simple model of a dataset where the true probabilities are themselves uniformly distributed on $[0,1]$ (and hence the low probability questions are very rare), and show that the logarithmic scoring rule still does not incentivize reporting the true probability.

Imagine a stream of binary-resolution questions coming from a process with aleatoric uncertainty:
the *true* probabilities are themselves uniformly distributed on $[0,1]$.

For simplicity, we assume that label noise is *symmetric and constant*: each realized label $Y^{\prime}$ is
flipped from the true label $Y$ with probability $\eta\in(0,\frac{1}{2})$, independent of the question.

$$ $\Pr(Y^{\prime}=1\mid Y)=\begin{cases}1-\eta&\text{if }Y=1\\ \eta&\text{if }Y=0\end{cases}$ $$

Consider a well-calibrated forecaster, which outputs $p$ for a question with true probability $p$.
The log-score for one forecast is

$$ $\ell(p,Y^{\prime})=-[Y^{\prime}\log p+(1-Y^{\prime})\log(1-p)].$ $$

Expected score for a fixed $p$.
Conditioning on $(Y,Y^{\prime})$ and using the noise model,

$$ $\displaystyle L_{\eta}(p)$ $\displaystyle:=\mathbb{E}_{Y,Y^{\prime}\mid p}[\ell(p,Y^{\prime})]$ $\displaystyle=-[(1-\eta)p+\eta(1-p)]\log p-[(1-\eta)(1-p)+\eta p]\log(1-p).$ $$

Define $A(p)=\eta+(1-2\eta)p$ and $B(p)=1-A(p)$, so that

$$ $L_{\eta}(p)=-[A(p)\log p+B(p)\log(1-p)].$ $$

Because $p$ is uniform on $[0,1]$, the expected log-score is

$$ $\mathbb{E}[\ell]:=\mathbb{E}[L_{\eta}(p)]=\int_{0}^{1}L_{\eta}(p)\,dp.$ $$

We can compute the integral exactly:

$$ $\displaystyle\mathbb{E}[\ell]$ $\displaystyle=\int_{0}^{1}L_{\eta}(p)\,dp$ $\displaystyle=-\int_{0}^{1}[A(p)\log p+B(p)\log(1-p)]\,dp$ $$

Using standard integrals $\int_{0}^{1}\log p\,dp=-1$, $\int_{0}^{1}p\log p\,dp=-\frac{1}{4}$, and symmetry:

$$ $\mathbb{E}[\ell]=\frac{1}{2}+\eta$ $$

We now consider another forecaster, who is more cautious and *clamps* their probability estimates to the interval $[t,1-t]$ for some $t\in(0,1)$.

Clamped forecaster.
Fix a threshold $t\in(0,\tfrac{1}{2})$.
Instead of reporting the raw probability $p$, the forecaster outputs

$$ $q_{t}(p)\;=\;\max\!\bigl{\{}\,t,\,\min\{p,1-t\}\bigr{\}},$ $$

i.e. it *clamps* every estimate to the interval $[\,t,\,1-t\,]$.

Expected log-score.
Write
$A(p)=\eta+(1-2\eta)p,\;B(p)=1-A(p)$ as before.
With $p\sim\mathrm{Unif}(0,1)$ and the same symmetric label-noise rate
$\eta\in(0,\tfrac{1}{2})$,

$$ $\displaystyle\mathbb{E}[\ell_{t}]$ $\displaystyle=-\!\int_{0}^{t}[A(p)\log t+B(p)\log(1-t)]\,dp-\!\int_{t}^{1-t}[A (p)\log p+B(p)\log(1-p)]\,dp$ $\displaystyle\quad-\!\int_{1-t}^{1}[A(p)\log(1-t)+B(p)\log t]\,dp.$ $$

Evaluating the three elementary integrals gives the remarkably simple
closed-form expression

$$ $\mathbb{E}[\ell_{t}]=\frac{1}{2}+\eta\;-\;t\,(1+2\eta)\;-\;\log(1-t).$ $$

Optimal clamp level.
Differentiating and setting to zero,

$$ $\frac{d}{dt}\mathbb{E}[\ell_{t}]=-(1+2\eta)+\frac{1}{1-t}=0\quad \Longrightarrow\quad t^{\star}=\frac{2\eta}{1+2\eta}.$ $$

Substituting $t^{\star}$ back:

$$ $\mathbb{E}[\ell_{t^{\star}}]=\frac{1}{2}-\eta+\log(1+2\eta).$ $$

Comparison with the unclamped forecaster.
Recall that the naive, perfectly calibrated forecaster had
$\mathbb{E}[\ell]=\tfrac{1}{2}+\eta$.
The improvement from clamping is therefore

$$ $(\tfrac{1}{2}+\eta)-(\tfrac{1}{2}-\eta+\log(1+2\eta))\;=\;2\eta-\log(1+2\eta)= \Theta(\eta^{2})\;>\;0\quad\text{for every }\eta\in(0,\tfrac{1}{2}).$ $$

Conclusion. This proof shows that reporting the true probability is not the optimal strategy for the logarithmic scoring rule under label noise.
We make no attempt to derive a “good” scoring rule in the presence of label noise, as that would require modeling the distribution of label noise, which depends on the exact dataset collection process.

## Appendix E Betting on Shared Latent Events Maximizes Chance of Winning: A Toy Example

Here, we show a simple mathematical example of the problem described in [Section 3.2](https://arxiv.org/html/2506.00723v1#S3.SS2).
We predict a sequence of coin flips, $X_{1},\dots,X_{N}$.
Before any data are observed, we flip a *latent* fair coin $Z\sim\text{Bernoulli}(1/2)$;
the coin flips are then biased towards heads if $Z=1$ and towards tails if $Z=0$.

If $Z=1$ the observable process is a sequence of $N$ independent coin-flips
$X_{1},\dots,X_{N}\stackrel{{\scriptstyle\text{i.i.d.}}}{{\sim}}\text{Bernoulli
}(2/3)$;
if $Z=0$ then $X_{1},\dots,X_{N}\stackrel{{\scriptstyle\text{i.i.d.}}}{{\sim}}\text{Bernoulli
}(1/3)$.
Note that the marginal (unconditional) distribution of each $X_{i}$ is that of a fair coin, i.e. $\mathbb{E}X_{i}=1/2$.

Consider now three different forecasters:

- •
Forecaster $F_{+}$ ignores the latent uncertainty and always predicts
$\hat{p}_{i}=2/3$ (“bets that $Z$ came up heads”).
- •
Forecaster $F_{-}$ always predicts $\hat{p}_{i}=1/3$ (“bets that $Z$ came up tails”).
- •
Forecaster $F_{0}$ is *calibrated* and predicts the marginal
$\hat{p}_{i}=1/2$ for every $i$.

After the $N$ flips resolve, each forecaster receives the (strictly proper) log-score
$S=\sum_{i=1}^{N}\bigl{[}X_{i}\log\hat{p}_{i}+(1-X_{i})\log(1-\hat{p}_{i})\bigr
{]}$.
Whoever attains the *highest* score wins the contest.

We now compute the *expected* log-scores of the three forecasters, conditional on the
value of the latent variable $Z$.
Let $\ell(p\|\hat{p})=p\log\hat{p}+(1-p)\log(1-\hat{p})$ be the expected
log-score obtained when the true bias is $p$ but the forecaster predicts
$\hat{p}$. With this notation, the conditional expectations are

$$ $\displaystyle\mathbb{E}[S_{F_{+}}\mid Z=1]$ $\displaystyle=N\,\ell\bigl{(}\tfrac{2}{3}\|\tfrac{2}{3}\bigr{)},$ $\displaystyle\mathbb{E}[S_{F_{+}}\mid Z=0]$ $\displaystyle=N\,\ell\bigl{(}\tfrac{1}{3}\|\tfrac{2}{3}\bigr{)},$ $\displaystyle\mathbb{E}[S_{F_{-}}\mid Z=1]$ $\displaystyle=N\,\ell\bigl{(}\tfrac{2}{3}\|\tfrac{1}{3}\bigr{)},$ $\displaystyle\mathbb{E}[S_{F_{-}}\mid Z=0]$ $\displaystyle=N\,\ell\bigl{(}\tfrac{1}{3}\|\tfrac{1}{3}\bigr{)},$ $\displaystyle\mathbb{E}[S_{F_{0}}\mid Z=1]$ $\displaystyle=N\,\ell\bigl{(}\tfrac{2}{3}\|\tfrac{1}{2}\bigr{)},$ $\displaystyle\mathbb{E}[S_{F_{0}}\mid Z=0]$ $\displaystyle=N\,\ell\bigl{(}\tfrac{1}{3}\|\tfrac{1}{2}\bigr{)}.$ $$

Numerically evaluating the six quantities gives

$$ $\begin{array}[]{c|ccc}&Z=1&&Z=0\\ \hline\cr F_{+}&-0.64\,N&&-0.87\,N\\ F_{-}&-0.87\,N&&-0.64\,N\\ F_{0}&-0.69\,N&&-0.69\,N\end{array}$ $$

- •
If $Z=1$ (the coins are $2/3$-biased), $F_{+}$ attains the highest
expected score; $F_{0}$ is second; $F_{-}$ is last.
- •
If $Z=0$ (the coins are $1/3$-biased), the ordering reverses:
$F_{-}$ wins, $F_{0}$ again finishes in the middle, and $F_{+}$ is last.

Thus the calibrated forecaster $F_{0}$ never maximizes the
conditional expected score; there is always another forecaster who does strictly
better by committing to one of the two latent worlds.
Given $N$ is large enough, the Law of Large Numbers ensures that the forecaster with the highest expected score has a high probability of winning the contest.
This toy example makes concrete how hidden common causes can create incentives for extreme, correlated bets.

## Appendix F ForecastBench Question Templates

Figure: Figure 8: ForecastBench obtains questions from multiple sources, but from each source, questions follow very specific templates [Karger et al., 2024b]. Large parts of the dataset thus resembles more an aggregation of predictive performance over some very specific time series, rather than general judgmental forecasting.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 4
presentation: 4
questions: NA
soundness: 3
strengths: S1: I think this paper is timely is quite relevant given the surge of papers in forecasting.This is an essential paper for the community. The primary focus of the paper is to bring forth the flaws and issues in evaluation and benchmarks.

S2: Paper demonstrates various issues with prior work’s evaluations such as model cut-off date, bias in retrieval, leakag. The paper reads well. And is structured for ease of understanding.
summary: This is a meta paper which presents a critical analysis of various evaluation approaches to LLM forecasting. Main theme is that most claims about LLMs outperforming humans on forecasting may have flawed evaluation.
weaknesses: W1. My main concern is that this is a meta analysis paper, where main contribution is the analysis and synthesizing prior work from the lens of evaluation. It is not a new artifact in a traditional sense like algo, data, methods etc.

W2. Some claims are supported through prompts/evidence, while others, like LLMs gaming the benchmarks, etc, are extrapolated/opinionated about.

---

confidence: 2
contribution: 3
initial_rating: 4
presentation: 3
questions: Please refer to Weakness.
soundness: 2
strengths: 1. The paper explores an interesting topic on the evaluation of LLM forecasters. The problem is well scoped and the structure is clear. It groups the challenges into two main categories and then discusses more specific cases under each.

2. The challenges identified by the authors are valid and important, and experiments in the subsequent work [1] further confirm them.

[1]FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction
summary: The paper analyzes and synthesizes the evaluation of LLM forecasters. It identifies two main challenges: (1) various forms of temporal leakage make it difficult to trust evaluation results; and (2) performance in evaluations is hard to extrapolate to real-world forecasting settings, thereby affecting the effectiveness of LLMs as forecasters. Drawing on concrete cases from existing work, it illustrates how these challenges impact the evaluation of LLM forecasters.
weaknesses: 1. Overall, I consider this paper closer to a position paper: its core contribution lies in raising the problem, summarizing the challenges, and offering methodological recommendations, while lacking new methods, benchmarks, or rigorous, controlled experimental evaluation to substantiate the main claims.

2. The paper focuses primarily on backtesting or retrodiction, and the authors contend that “The gold standard for evaluating a forecaster involves running it on unresolved questions, waiting until the questions resolve, and then scoring the predictions” is impractical for rapid model evaluation; however, benchmarks of this type already exist [1].  

[1]FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction

---

confidence: 5
contribution: 4
initial_rating: 8
presentation: 4
questions: Two recent benchmarks emerged after ICLR deadline: https://mirai-llm.github.io/ and https://futurex-ai.github.io/. I wonder if the author(s) could offer any comments and whether these new eval candidates suffer any of the pitfalls.
soundness: 4
strengths: I find the paper very well written and the arguments are presented cleanly and convincingly. In particular, the main argument is thoughtful. It systematizes failure modes for evaluating LLM forecasters into two categories—temporal leakage in backtests and misinterpretation of benchmark gains—which is a novel perspective in the literature.

For each of the pitfalls, the paper provides concrete examples. For instance, on logical leakage, the authors show that the very fact that a question is being graded at time T rules out some outcomes (e.g., “Jan 6” queries biased toward post-2021 associations) and argue that many published results likely overstate LLM forecasting ability because models may implicitly access post-T information.

These contributions are, to my knowledge, original. Beyond critiques, the paper offers mitigation guidance.

Finally, the paper offers a comprehensive view of the field and provides a strong critique of the status quo. I believe this kind of critical examination and revisiting is healthy for the area to move forward.
summary: The paper argues that evaluating LLMs as forecasters has two primary failure modes. First, backtests are hard to trust: they can leak information via “logical leakage”, unreliable date-restricted retrieval (search engines yield future-informed or misdated pages), and over-reliance on reported model cutoff dates that are only loose guidelines. Second, the paper explains why even “clean” benchmark gains may not translate to real-world forecasting skill. For example, LLMs can ingest or retrieve human crowd forecasts and then be credited with matching humans. 

The paper offers practical guidance for the field moving forward, such as penalizing memorization and being cautious about turning backtests into training objectives. It also suggests that research in the field should constrain retrieval to well-dated corpora, add buffers around model cutoffs, report multiple metrics, and, where possible, include live market evaluations to substantiate claims
weaknesses: While I find the arguments convincing, the paper could be stronger in offering more systematic and empirical studies. For example, regarding logical leakage, I wonder if the impact of that could be precisely measured (in the context of any of the previous benchmarks). Similarly, for retrieval leakage, the paper offers a few examples. However, a broader measurement could strengthen the argument. 

For each pitfall, the paper suggests various potential solutions. It would be nice to implement & test some critical ones and compare them end-to-end to demonstrate improved robustness.

Finally, I wonder if the author(s) have examined https://futurex-ai.github.io/ and have any comments.

---

confidence: 4
contribution: 2
initial_rating: 8
presentation: 2
questions: - Page 9 weirdly ends with empty vertical space. Have the authors added a page break or vertical spacing?
- Have the authors changed the default font of the ICLR latex file? This is not something that should be changed.
soundness: 3
strengths: - Very good arguments about current issues with using LLMs for forecasting.
- Provides empirical evidence of the discussed issues.
- They propose solutions to the discussed ideas, although only as future work directions to be explored.
summary: The authors discuss many issues with regards to forecasting benchmarks for LLMs. They mainly argue that LLMs can potentially not be trusted for real world forecasting due to these isssues.
weaknesses: - While the authors do have evidence for their claims, it is somewhat limited. As the authors mention themselves: "We also do not have proof that the benchmark issueswe uncoverwould lower the performance claims of LLM forecasters". More damning evidence would strengthen the claims.


Decision: Accept (Poster)