| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pretty_name: FinDendro |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - question-answering |
| - multiple-choice |
| tags: |
| - finance |
| - economics |
| - knowledge-cutoff |
| - temporal-knowledge |
| - llm-evaluation |
| - look-ahead-bias |
| - backtesting |
| configs: |
| - config_name: questions |
| data_files: data/questions.jsonl |
| default: true |
| - config_name: sample_facts |
| data_files: data/sample_facts.jsonl |
| - config_name: fact_pool_macro_policy |
| data_files: data/fact_pool_macro_policy.jsonl |
| - config_name: results_frontier |
| data_files: results/frontier/*.jsonl |
| - config_name: results_open |
| data_files: results/open/*.jsonl |
| - config_name: results_point_in_time_letter |
| data_files: results/point_in_time/letter/*.jsonl |
| - config_name: results_point_in_time_likelihood |
| data_files: results/point_in_time/likelihood/*.jsonl |
| --- |
| |
| # FinDendro: Temporal Self-Knowledge of LLMs on Dated Financial Facts |
|
|
| FinDendro measures two dates for a language model: where its knowledge of dated financial facts actually ends, and where the model *says* it ends. Every answer is tied to the model's own claim of knowing it, so the benchmark can test the safeguards used in LLM backtests (vendor-stated cutoffs, self-reports, self-report filters, and "assume today is ..." prompts). |
|
|
| ## Facts |
|
|
| Dated, effectively unrevised U.S. facts from January 1999 to September 2026, in three families: |
|
|
| | Family | Series | Source | Sample | Pool released here | |
| |---|---|---|---|---| |
| | Macroeconomic | 12 monthly series (headline/core CPI YoY, unemployment, effective fed funds, 3M/2Y/10Y Treasury, 30Y mortgage, JPY/USD, CNY/USD, USD/EUR, WTI) | FRED | 1,353 | 3,993 | |
| | Stock prices | Month-end close of 50 large U.S. companies, as quoted at the time (later splits reversed) | Public historical quotes | 540 | not released (see below) | |
| | Fed decisions | Monthly target-rate outcome (raised / lowered / unchanged, incl. intermeeting moves) | FRED DFEDTAR / DFEDTARU | 333 | 333 | |
|
|
| The evaluation sample has **2,226 facts** (sparse before 2021, dense from 2021 on) and **11,130 prompts** (five per fact). 1,340 facts count toward knowledge after the controls below. |
|
|
| ## Questions (`data/questions.jsonl`) |
|
|
| Each fact yields five prompts that share one set of candidates. Numerical facts offer four values spaced by roughly one typical monthly change, with the true value's rank drawn uniformly; Fed decisions offer the three outcomes. |
|
|
| | `id` suffix | Task | Description | |
| |---|---|---| |
| | `/T2` | Task 2 (value) | Choose the value. Reference for what the model knows. | |
| | `/T1-recall-none` | Task 1, Recall | Every candidate offered twice: "I know this value" or "I do not know this value; my best guess is". | |
| | `/T1-recall-declared` | Task 1, Recall + declared date | Same, prefixed with "Assume that today is [end of the previous month]." | |
| | `/T1-predict-none` | Task 1, Predict | "I already know the outcome" or "I do not know the outcome; my prediction is". | |
| | `/T1-predict-declared` | Task 1, Predict + declared date | Same, with the declared date. | |
|
|
| Key fields: `prompt`, `gold` (option letter), `option_rank` (letter → candidate rank; for Task 1 see `option_meta` in the results, which gives `stance` and `rank`), `true_rank`, `persistence_correct` (True if repeating last month's value would land on the correct option), `family`, `period`, `indicator`. |
|
|
| **Controls used in the paper.** Numerical knowledge is measured only on facts with `persistence_correct == False`; Fed decisions use balanced accuracy over the three outcomes ("unchanged" is correct in 78% of months). Chance accuracy is about 0.27. |
|
|
| ## Raw model results (`results/`) |
|
|
| One JSON line per prompt, with the model's raw `response`. |
|
|
| | Folder | Models | Scoring | |
| |---|---|---| |
| | `frontier/` | GPT-5, GPT-4o, DeepSeek-V3.2, Llama-3.3-70B-Instruct (API, temperature 0 where supported) | single option letter | |
| | `open/` | Qwen3-8B, Qwen3.5-9B (greedy, thinking disabled) | single option letter | |
| | `point_in_time/letter/` | TiMaGPT2, ChronoGPT-Instruct, DatedGPT vintages | single option letter (these small models mostly pick option A) | |
| | `point_in_time/likelihood/` | the same families, Task 2 only | candidate with the highest mean log-probability (`value_scores`) | |
|
|
| `results/summary/` holds the aggregate metrics: `headline_metrics.json` (knows, says, accuracy on claimed/disclaimed facts, within-period gap, declared-date effects), `quarterly_timeline.json` (quarterly accuracy and claim rate, and the estimated knowledge-end and claims-end quarters), and `extra_analyses.json` (by fact family, Recall vs Predict wording, self-report filter, threshold robustness). |
|
|
| ## Headline results |
|
|
| | Model | Knows | Says it knows | Acc. on disclaimed facts | Knowledge ends | Claims end | Vendor cutoff | |
| |---|---|---|---|---|---|---| |
| | GPT-5 | 0.76 | 0.38 | 0.61 | 2024Q3 | 2024Q3 | 2024-09 | |
| | DeepSeek-V3.2 | 0.54 | 0.28 | 0.48 | 2024Q3 | 2024Q4 | n/a | |
| | GPT-4o | 0.50 | 0.20 | 0.34 | 2023Q1 | 2023Q2 | 2023-10 | |
| | Llama-3.3-70B | 0.43 | 0.48 | 0.29 | 2021Q2 | 2023Q1 | 2023-12 | |
|
|
| ## Notes and licensing |
|
|
| - Questions, annotations and model outputs are released under CC BY 4.0. Model outputs remain subject to the respective providers' terms. |
| - Macroeconomic and Fed series come from FRED; please also respect the terms of the original data providers listed on FRED. |
| - Stock prices are released only as question candidates and answers. The full monthly price series used to build the pool is not redistributed. |
| - Model outputs were collected in October 2026. API-served models change over time, so re-running may give different answers. |
|
|
