Papers by Adam Lelkes

4 papers
SDOH-NLI: a Dataset for Inferring Social Determinants of Health from Clinical Notes (2023.findings-emnlp)

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Challenge: Social and behavioral determinants of health (SDOH) play a significant role in shaping health outcomes, and extracting these determinant from clinical notes is a first step to help healthcare providers systematically identify opportunities to provide appropriate care and address disparities.
Approach: They propose a dataset that extracts social and behavioral determinants from clinical notes and uses them to form a natural language inference task.
Outcome: The proposed dataset is based on publicly available notes and is more challenging than standard NLI benchmarks.
How Does Generative Retrieval Scale to Millions of Passages? (2023.emnlp-main)

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Challenge: generative retrieval is a new paradigm for information retrieval, enabling a sequence-to-sequence model with a single Transformer . generative encoders have been used on small corpora, but only on large ones .
Approach: They propose to encode an entire document corpus within a single Transformer . they find generative retrieval is competitive with state-of-the-art dual encoders on small corpora .
Outcome: The proposed approach is competitive with state-of-the-art dual encoders on small corpora, the study finds . the proposed approach only evaluates on document corporales on the order of 100K in size .
SEMQA: Semi-Extractive Multi-Source Question Answering (2024.naacl-long)

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Challenge: Recent proposed long-form question answering systems have shown promising capabilities, but attributing and verifying their generated abstractive answers can be difficult.
Approach: They propose a task that summarises multiple sources in a semi-extractive fashion . they create a dataset with human-written semi-extractive answers to natural and generated questions .
Outcome: The proposed task summarizes multiple sources in a semi-extractive fashion and produces fine in-line attributions by-design that are easy to verify, interpret, and evaluate.
AgreeSum: Agreement-Oriented Multi-Document Summarization (2021.findings-acl)

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Challenge: Existing studies on agreement-oriented multidocument summarization have focused on clusters of articles . a recent study focused on the use of a pretraining framework to summarize articles based on the "union" of the articles.
Approach: They propose to use agreement-oriented multidocument summarization to provide agreement-orientated summaries that represent information common to all articles.
Outcome: The proposed task is called agreement-oriented multidocument summarization . the authors apply the pretrained model PEGASUS onto the task .

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