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Talent Scout
R
Ryan Chen
Northwestern University
B
Bradly C. Stadie
Northwestern University and Bridgewater AIA Labs
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Temporal experts on LinkedIn & GitHub
References (23)
[3]
EvolveBench: A Comprehensive Benchmark for Assessing Temporal Awareness in LLMs on Evolving Knowledge
Founder's Pitch
"Detect and mitigate temporal contamination in historical backtesting of LLMs."
Commercial Viability Breakdown
0-10 scaleHigh Potential
1/4 signals
Quick Build
3/4 signals
Series A Potential
2/4 signals
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arXiv Paper
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Why It Matters
Without properly addressing temporal contamination, evaluations of LLMs on historical data may provide inaccurate or inflated performance results, undermining their reliability for future forecasting tasks.
Product Angle
A potential product could involve an API service for financial and legal sectors to ensure forecasting models are free from post-cutoff data bias, guaranteeing clearer attribution of prediction performance.
Disruption
The solution could replace or enhance existing heuristic-based systems for de-biasing historical model evaluations in financial and legal predictions.
Product Opportunity
The market spans financial institutions like fund managers, legal analytics firms, or any entity needing reliable predictive modeling without the risk of hindsight bias. These institutions invest heavily in analytics to optimize decision-making.
Use Case Idea
Commercial application in ensuring the reliability and accuracy of LLM-driven financial forecasting tools by eliminating temporal contamination from predictions.
Science
The paper introduces methods to detect and measure temporal knowledge leakage in large language models (LLMs) when backtesting predictions based on historical data by decomposing predictions into atomic, verifiable claims and applying Shapley values to assess information leakage.
Method & Eval
The method was validated on datasets from three domains: Supreme Court case predictions, NBA salary estimations, and stock return rankings, demonstrating significant reduction in decision-critical leakage while retaining performance.
Caveats
The method is computationally intensive given its reliance on external verification and Shapley value computations, potentially limiting scalability and real-time application.
Author Intelligence
Zeyu Zhang
Northwestern University
zeyuzhang2028@u.northwestern.edu
Ryan Chen
Northwestern University
Bradly C. Stadie
Northwestern University and Bridgewater AIA Labs