John Michael Giorgi — accepted AI-conference papers

2023

Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset

Saeid Alavi Naeini, Raeid Saqur, mozhgan saeidi, John Michael Giorgi, Babak Taati

NeurIPS 2023poster

The quest for human imitative AI has been an enduring topic in AI research since inception. The technical evolution and emerging capabilities of the latest cohort of large language models (LLMs) have reinvigorated the subject beyond academia to cultural zeitgeist. While recent NLP evaluation benchm…

Cited by 14 Source ↗PDF ↗code ↗Scholar ↗

2023

Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval

John Michael Giorgi, Luca Soldaini, BO WANG, Gary D. Bader, Kyle Lo, Lucy Lu Wang +1

EMNLP 2023long findings

Multi-document summarization (MDS) assumes a set of topic-related documents are provided as input. In practice, this document set is not always available; it would need to be retrieved given an information need, i.e. a question or topic statement, a setting we dub "open-domain" MDS. We study this mo…

Cited by 0 Source ↗Scholar ↗

2022

BigBio: A Framework for Data-Centric Biomedical Natural Language Processing

Jason Alan Fries, Leon Weber, Natasha Seelam, Gabriel Altay, Debajyoti Datta, Samuele Garda +37

NeurIPS 2022accept

Training and evaluating language models increasingly requires the construction of meta-datasets -- diverse collections of curated data with clear provenance. Natural language prompting has recently lead to improved zero-shot generalization by transforming existing, supervised datasets into a variety…

Cited by 58 Source ↗PDF ↗code ↗Scholar ↗