Chalamalasetti Kranti

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.

LLM judges can drastically change their evaluations—by up to 85%—when provided with reference answers, revealing a critical flaw in no-reference assessments.