Papers by Sheridan Feucht

2 papers
Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs (2024.emnlp-main)

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Challenge: Current language models process text as sequences of tokens that roughly correspond to words . individual tokens are often semantically unrelated to the meanings of the words/concepts they comprise .
Approach: They propose a method to "read out" the implicit vocabulary of an autoregressive LLM by examining differences in token representations across layers.
Outcome: The proposed method "reads out" the implicit vocabulary of an autoregressive LLM by examining differences in token representations across layers.
NEWTS: A Corpus for News Topic-Focused Summarization (2022.findings-acl)

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Challenge: Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or professional content.
Approach: They propose a topical summarization corpus called NEWTS that is annotated via crowd-sourcing.
Outcome: The proposed model can condition summaries on a desired range of themes . the proposed model outperforms Lead-3 baselines on most benchmark datasets .

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