Sebastian Bordt@ICML
@sbordt
Language models and interpretable machine learning. Postdoc @ Uni Tübingen.
Have you ever wondered whether a few times of data contamination really lead to benchmark overfitting?🤔 Then our latest paper about the effect of data contamination on LLM evals might be for you!🚀 "How Much Can We Forget about Data Contamination?" (accepted at #ICML2025) shows…
Today at 4:30 pm in the East Exhibition Hall. #icml icml.cc/virtual/2025/p…
During the last couple of years, we have read a lot of papers on explainability and often felt that something was fundamentally missing🤔 This led us to write a position paper (accepted at #ICML2025) that attempts to identify the problem and to propose a solution. Introducing:…
Today at 11:00 a.m. #ICML poster session East. icml.cc/virtual/2025/p…
Have you ever wondered whether a few times of data contamination really lead to benchmark overfitting?🤔 Then our latest paper about the effect of data contamination on LLM evals might be for you!🚀 "How Much Can We Forget about Data Contamination?" (accepted at #ICML2025) shows…
I'm at #ICML in Vancouver this week, hit me up if you want to chat about pre-training experiments or explainable machine learning. You can find me at these posters: Tuesday: How Much Can We Forget about Data Contamination? icml.cc/virtual/2025/p… Wednesday: Position:…
It turns out that you can train your LLM by injecting benchmark eval data into your train data, and still have no effect on benchmark evals! Accepted at @icmlconf Joint work with @sbordt @valentynepii and Ulrike von Luxburg
Have you ever wondered whether a few times of data contamination really lead to benchmark overfitting?🤔 Then our latest paper about the effect of data contamination on LLM evals might be for you!🚀 "How Much Can We Forget about Data Contamination?" (accepted at #ICML2025) shows…