Michelle Wastl

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.

Larger LLMs may ignore user context more than smaller models, revealing critical insights into how linguistic framing can sway model responses.