README.md

metadata

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.