122 citations · 540 across the 31 of their papers we have counts for
32 papers
An automatically discovered chain-of-thought prompt generalizes to novel models and datasets
Konstantin Hebenstreit, Robert Praas, Louis P Kiesewetter +1
Emergent chain-of-thought (CoT) reasoning capabilities promise to improve performance and explainability of large language models (LLMs). However, uncertainties remain about how re…
Applying unsupervised keyphrase methods on concepts extracted from discharge sheets
Hoda Memarzadeh, Nasser Ghadiri, Matthias Samwald +1
Clinical notes containing valuable patient information are written by different health care providers with various scientific levels and writing styles. It might be helpful for cli…
Model-agnostic explainable artificial intelligence for object detection in image data
Milad Moradi, Ke Yan, David Colwell +2
In recent years, deep neural networks have been widely used for building high-performance Artificial Intelligence (AI) systems for computer vision applications. Object detection is…
ThoughtSource: A central hub for large language model reasoning data
Simon Ott, Konstantin Hebenstreit, Valentin Liévin +6
Large language models (LLMs) such as GPT-4 have recently demonstrated impressive results across a wide range of tasks. LLMs are still limited, however, in that they frequently fail…
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…