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Lukas Gienapp

AwardSIGIR 2026×2评测与基准对齐与偏好效率与系统推理
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2 一作 · 2 篇 · SIGIR 2026

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2026 顶会论文

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一作SIGIR 2026OralBest Student Paper· Information Retrieval (Full)

Topic-Specific Classifiers are Better Relevance Judges than Prompted LLMs

Lukas Gienapp · Martin Potthast · Andrew Yates · Harrisen Scells · Eugene Yang

摘要

The unjudged document problem, where systems that did not contribute to the original judgement pool may retrieve documents without a relevance judgement, is a key obstacle to the reuseability of test collections in information retrieval. This is commonly dealt with by treating unjudged documents...

评测与基准对齐与偏好效率与系统OpenReview
一作SIGIR 2026Poster· Information Retrieval (Short)

Humans, LLMs, and Measures Do Not Align in Attributed Information Retrieval

Lukas Gienapp · Jenny Lang · Martin Potthast · Harrisen Scells

摘要

Evaluating attributed information retrieval (AIR) systems requires assessing both informativeness and attributability. To enable scalable evaluation, LLM-sourced ground truth data is frequently used, yet the validity of this practice remains unclear. We replicate the evaluation framework of Djedd...

评测与基准对齐与偏好推理OpenReview

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