Leshem Choshen

Machine translation benchmarks have functionally saturated, but pairing human-authored failure cases with deterministic verification rules reveals critical multimodal blind spots that automated metrics consistently miss.

User feedback can significantly enhance LLM performance, but current evaluation methods often overlook its benefits, leading to misguided assessments of model improvements.

Using a top or bottom-performing LLM as an anchor in "LLM-as-a-judge" benchmarks can dramatically skew results, making the choice of a mediocre anchor key to reliable evaluation.

BabyLM 2026 seeks to push the boundaries of data-efficient and cognitively plausible language models, now with a multilingual twist.