作者位次 #4/5NeurIPS 2026Poster· General Machine Learning / Representation Learning
From Topology to Retrieval: Decoding Embedding Spaces with Unified Signatures
Florian Rottach · William Rudman · Bastian Rieck · Harrisen Scells · Carsten Eickhoff
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Florian Rottach · William Rudman · Bastian Rieck · Harrisen Scells · Carsten Eickhoff
Lukas Gienapp · Martin Potthast · Andrew Yates · Harrisen Scells · Eugene Yang
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...
Gregory Polyakov · Harrisen Scells · Carsten Eickhoff
Learned sparse retrieval models such as SPLADE combine the effectiveness of neural architectures with the efficiency of inverted indices. As these models assign weights to terms from a fixed vocabulary, interpretability is often touted as a major benefit of these models. However, the emergence of...
Lukas Gienapp · Jenny Lang · Martin Potthast · Harrisen Scells
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...