96 citations · 146 across the 7 of their papers we have counts for
8 papers
WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models
John Giorgi, Augustin Toma, Ronald Xie +4
This paper describes our submission to the MEDIQA-Chat 2023 shared task for automatic clinical note generation from doctor-patient conversations. We report results for two approach…
Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset
Saeid Naeini, Raeid Saqur, Mozhgan Saeidi +2
The quest for human imitative AI has been an enduring topic in AI research since its inception. The technical evolution and emerging capabilities of the latest cohort of large lang…
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval
John Giorgi, Luca Soldaini, Bo Wang +4
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 retri…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
Jason Alan Fries, Leon Weber, Natasha Seelam +40
Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance. Natural language prompt…
A sequence-to-sequence approach for document-level relation extraction
John Giorgi, Gary D. Bader, Bo Wang
Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (DocRE). DocRE requires integrating…
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
John Giorgi, Osvald Nitski, Bo Wang +1
Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corp…
End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models
John Giorgi, Xindi Wang, Nicola Sahar +3
Named entity recognition (NER) and relation extraction (RE) are two important tasks in information extraction and retrieval (IE \& IR). Recent work has demonstrated that it is bene…