# How to Evaluate your Question Answering System Every Day and Still Get Real Work Done

Source: [How to Evaluate your Question Answering System Every Day and Still Get Real Work Done](<https://www.sota2.com/research/paper/68fa5f9f1e209131ce9eb076>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:22:46.405Z

Authors: Eric Breck, John D. Burger, Lisa Ferro, Lynette Hirschman, David House, Marc Light, Inderjeet Mani (1) \_\_INSTITUTION\_7\_\_ The MITRE Corporation)

Published: 2000-04-17T19:29:51.000Z

## Abstract

In this paper, we report on Qaviar, an experimental automated evaluation system for question answering applications. The goal of our research was to find an automatically calculated measure that correlates well with human judges' assessment of answer correctness in the context of question answering tasks. Qaviar judges the response by computing recall against the stemmed content words in the human-generated answer key. It counts the answer correct if it exceeds agiven recall threshold. We determined that the answer correctness predicted by Qaviar agreed with the human 93% to 95% of the time. 41 question-answering systems were ranked by both Qaviar and human assessors, and these rankings correlated with a Kendall's Tau measure of 0.920, compared to a correlation of 0.956 between human assessors on the same data.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/cs/0004008>)

[PDF](<https://arxiv.org/pdf/cs/0004008.pdf>)