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Summary of Small Models Are (still) Effective Cross-domain Argument Extractors, by William Gantt and Aaron Steven White


Small Models Are (Still) Effective Cross-Domain Argument Extractors

by William Gantt, Aaron Steven White

First submitted to arxiv on: 12 Apr 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This study investigates two promising methods – question answering (QA) and template infilling (TI) – for effective ontology transfer in event argument extraction (EAE). The researchers explore zero-shot transfer using both techniques on six major EAE datasets at the sentence and document levels. Notably, they challenge the reliance on large language models (LLMs) by showing that smaller models trained on a suitable source ontology can achieve superior performance to GPT-3.5 or GPT-4 in zero-shot extraction.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper looks at two ways to help computers understand events and their relationships: question answering (QA) and template infilling (TI). It tests how well these methods work when transferring knowledge from one event dataset to another, without any extra training. The results show that smaller models trained on a good starting point can be better than using super powerful language models like GPT-3.5 or GPT-4.

Keywords

» Artificial intelligence  » Gpt  » Question answering  » Zero shot