CIKM Short Paper: Knowing When to Correct

Venue: ACM International Conference on Information and Knowledge Management (CIKM)
Authors: Stergios Konstantinidis, Hayman Lotfy, Michalis Vlachos
Affiliation: Department of Information Systems (DESI), HEC Lausanne
GitHub Public: CIKM_public
Overleaf Project: 
https://git.overleaf.com/6a1d78e803cbdf7def32a839
Core Codebase: 
/Users/stergios/Documents/GitHub/Optimizing-LLM-based-OCR-Corrections
1. Problem Statement
At multi-million page scale, LLM-based OCR correction is constrained by cost:
Full LLM correction costs hundreds of thousands of dollars on national archival collections.
Many archival passages are already accurate; invoking LLMs on them introduces latency, cost, and risk of hallucinations.
Core Research Questions:
RQ1: How closely does selective routing match the theoretical oracle ceiling, and does it outperform naive confidence thresholding?
RQ2: How well does the regression-estimated improvement \hat{Δ}_i correlate with the true realized improvement Δi?
RQ3: What is the actual operational cost saved when routing only segments predicted to benefit across various frontier LLMs?
Problem Visual Illustration
Figure 1: Typical OCR degradation in historical periodicals requiring cost-aware routing.