1 citations · 2 across the 3 of their papers we have counts for
3 papers
Interpretable Tabular Foundation Models via In-Context Kernel Regression
Ratmir Miftachov, Bruno Charron, Simon Valentin
Tabular foundation models like TabPFN and TabICL achieve state-of-the-art performance through in-context learning, yet their architectures remain fundamentally opaque. We introduce…
SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents
Muhammad Shihab Rashid, Christian Bock, Yuan Zhuang +10
Coding agents powered by large language models have shown impressive capabilities in software engineering tasks, but evaluating their performance across diverse programming languag…
Cost-Effective Hallucination Detection for LLMs
Simon Valentin, Jinmiao Fu, Gianluca Detommaso +3
Large language models (LLMs) can be prone to hallucinations - generating unreliable outputs that are unfaithful to their inputs, external facts or internally inconsistent. In this…