llm-based-forecasting-systems-reported-benchmark-gains-often-cannot-be CLM-0419For LLM-based forecasting systems, reported benchmark gains often cannot be attributed to temporal reasoning, because test periods predate the training cutoff, ablations show the LLM component sometimes adds nothing over the statistical or retrieval parts, and accuracy shifts under small input perturbations.
Capability: Predicting future events
Observed on
Holds for forecasting setups evaluated on fixed historical benchmarks rather than live, post-cutoff questions; drawn from a survey across finance, weather, health, energy and opera.
Sources
- supportsLLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and ApplicationsValidated by: Not measured; the survey argues for it from assembled negative evidence.. Argued, not measured: a survey aggregating reported perturbation sensitivity, null ablations, and contamination concerns across published forecasting-agent papers. No new experiment; the specific size of the contamination effect is not quantified here.
Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.
Related claims
- Across LLM-based forecasting systems, reported accuracy gains frequently cannot be attributed to the language model's temporal reasoning, because evaluation windows predate training cutoffs, component ablations show the LLM sometimes adds nothing over the statistical or retrieval parts, and results shift under small input perturbations.Predicting future events · reviewed by AI
- When a frozen pretrained time-series forecaster leaves structured residual error, having an LLM propose named semantic features of that residual (e.g. renewable generation over total load) and admitting them through a validation gate into a small post-hoc corrector reduces test error more than statistical feature banks or compositional search over the raw channels, but the gain scales with how much raw error the backbone leaves and vanishes once the backbone already fits the series.Predicting future events · reviewed by AI
- On anonymized retrospective social-event forecasting (entities replaced, dates shifted), frontier LLMs score much better at assigning low probability to events that did not happen than at assigning high probability to events that did, with a mean false-minus-true calibration gap of about 15.9 points across six models and up to 38 points for one model, so benchmarks weighted toward invented events overstate forecasting skill.Predicting future events · reviewed by AI
- On natural-language stock questions that require judging a future condition (e.g. whether a price will exceed a threshold next month), open-weight 20B-31B LLMs reasoning directly from the retrieved historical series answer near chance, and routing the question through an explicit SQL-retrieval plus time-series-forecasting toolchain raises accuracy to roughly the level of human stock investors.Predicting future events · reviewed by AI
- When a search agent is asked to forecast a future distribution rather than report the current state over the same cumulative literature archive, the forward-looking target changes what it retrieves — it allocates a smaller share of retrieved documents to the most recent window, which is the more predictive evidence — so carrying the agent's own state estimate forward beats its explicit forecast.Predicting future events · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: Forecasting agents that keep their reported margin over statistical baselines on decontaminated live questions, with LLM-ablation showing the language component carries the gain and perturbation tests leaving accuracy stable.