First, we use this system to build stress tests for question answering, machine translation, and semantic parsing tasks.
Generating a chain of thought (CoT) can increase large language model (LLM) performance on a wide range of tasks.
Modern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands.
However, as a fixed-size model acquires more languages, its performance across all languages degrades, a phenomenon termed interference.
We model the entities/events in a reader's focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters.
Ranked #1 on Event Coreference Resolution on Gun Violence Corpus