15 citations · 53 across the 13 of their papers we have counts for
18 papers
Deep Label-Wise Attentive Temporal Convolutional Networks Improve Medical Coding
Muhammed Yavuz Nuzumlalı, Alexander Fabbri, Irene Li +1
Medical coding is the task of assigning a set of diagnosis and procedure codes for a hospitalization using recorded notes. It requires aggregating information from different parts…
MultiNRC: A Challenging and Native Multilingual Reasoning Evaluation Benchmark for LLMs
Alexander R. Fabbri, Diego Mares, Jorge Flores +5
Although recent Large Language Models (LLMs) have shown rapid improvement on reasoning benchmarks in English, the evaluation of such LLMs' multilingual reasoning capability across…
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Griffin Adams, Alexander Fabbri, Faisal Ladhak +2
Selecting the ``right'' amount of information to include in a summary is a difficult task. A good summary should be detailed and entity-centric without being overly dense and hard…
Generating EDU Extracts for Plan-Guided Summary Re-Ranking
Griffin Adams, Alexander R. Fabbri, Faisal Ladhak +2
Two-step approaches, in which summary candidates are generated-then-reranked to return a single summary, can improve ROUGE scores over the standard single-step approach. Yet, stand…
LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond
Philippe Laban, Wojciech Kryściński, Divyansh Agarwal +4
With the recent appearance of LLMs in practical settings, having methods that can effectively detect factual inconsistencies is crucial to reduce the propagation of misinformation…
On Learning to Summarize with Large Language Models as References
Yixin Liu, Kejian Shi, Katherine S He +5
Recent studies have found that summaries generated by large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarizat…