Ferdinand Schlatt
@fschlatt.bsky.social
PhD Student, efficient and effective neural IR models 🧠🔎
Happy to share that our paper "The Viability of Crowdsourcing for RAG Evaluation" received the Best Paper Honourable Mention at #SIGIR2025! Very grateful to the community for recognizing our work on improving RAG evaluation. Â đź“„ webis.de/publications...
Want to know how to make bi-encoders more than 3x faster with a new backbone encoder model? Check out our talk on the Token-Independent Text Encoder (TITE) #SIGIR2025 in the efficiency track. It pools vectors within the model to improve efficiency dl.acm.org/doi/10.1145/...
@mrparryparry.bsky.social presenting our work on reproducing TREC DL 2019 judgements and the implications for evaluating modern ranking models on modern collections. Paper: arxiv.org/abs/2502.20937
Variations in Relevance Judgments and the Shelf Life of Test Collections
The fundamental property of Cranfield-style evaluations, that system rankings are stable even when assessors disagree on individual relevance decisions, was validated on traditional test collections. ...
arxiv.org
Thank you Carlos for the shout-out of Lightning IR in the LSR tutorial at #SIGIR2025 If you want to fine your own LSR models, check out our framework at github.com/webis-de/lig...
Honored to receive the best short paper award and best paper honourable mention award at #ECIR2025. Thank you to all co-authors @maik-froebe.bsky.social, @hscells.bsky.social, Shengyao Zhuang, @bevankoopman.bsky.social, Guido Zuccon, Benno Stein, @martin-potthast.com, @matthias-hagen.bsky.social 🥳
Now we have @fschlatt.bsky.social on the #ECIR2025 stage predenting the research on the Set-Encoder. The paper is online at: webis.de/publications...
Great post that captures the tension between classic ML approaches and modern deep learning while acknowledging the nuances of both. “Working with LLMs doesn’t feel the same. It’s like fitting pieces into a pre-defined puzzle instead of building the puzzle itself.” www.reddit.com/r/MachineLea...
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