paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
MP

Martin Potthast

AwardSIGIR 2026×4评测与基准对齐与偏好效率与系统检索与知识推理
新星分贡献者
62
一作贡献0
核心/参与34
方向广度10
合作网络9
0 一作 · 4 篇 · SIGIR 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

4
作者位次 #2/5SIGIR 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
作者位次 #9/10SIGIR 2026Poster· Information Retrieval (Resource)

Auto-Judge: A Cross-Task Benchmark for Comparing LLM Judges for Citation-Grounded RAG Systems

Naghmeh Farzi · Tim Hagen · Eugene Yang · Maik Fröbe · Ronak Pradeep · Hossein A. Rahmani · Xi Wang · Oleg Zendel · Martin Potthast · Laura Dietz

摘要

We present the Auto-Judge resource for the meta-evaluation of automated LLM judges, especially judges that evaluate Retrieval-Augmented Generation (RAG) systems that ground their response with citations. The resource couples (i) a data release of topics, pooled RAG responses, and human judgments,...

评测与基准检索与知识对齐与偏好OpenReview
作者位次 #3/4SIGIR 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
作者位次 #4/4SIGIR 2026Poster· Information Retrieval

ReNeuIR at SIGIR 2026: The Fifth Workshop on Reaching Efficiency in Neural Information Retrieval

Maik Fröbe · Tim Hagen · Franco Maria Nardini · Martin Potthast

效率与系统检索与知识OpenReview

常见合作者