8 papers
Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions
Emmy Liu, Amanda Bertsch, Lintang Sutawika +9
Improvements in language model capabilities are often attributed to increasing model size or training data, but in some cases smaller models trained on curated data or with differe…
Evaluating Language Models as Synthetic Data Generators
Seungone Kim, Juyoung Suk, Xiang Yue +7
Given the increasing use of synthetic data in language model (LM) post-training, an LM's ability to generate high-quality data has become nearly as crucial as its ability to solve…
On Synthesizing Data for Context Attribution in Question Answering
Gorjan Radevski, Kiril Gashteovski, Shahbaz Syed +11
Question Answering (QA) accounts for a significant portion of LLM usage "in the wild". However, LLMs sometimes produce false or misleading responses, also known as "hallucinations"…
MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosis
Daniel Rose, Chia-Chien Hung, Marco Lepri +3
Differential Diagnosis (DDx) is a fundamental yet complex aspect of clinical decision-making, in which physicians iteratively refine a ranked list of possible diseases based on sym…
Aligning Generalisation Between Humans and Machines
Filip Ilievski, Barbara Hammer, Frank van Harmelen +22
Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and forming decisions, but may also disrupt…
Robust Text Classification: Analyzing Prototype-Based Networks
Zhivar Sourati, Darshan Deshpande, Filip Ilievski +2
Downstream applications often require text classification models to be accurate and robust. While the accuracy of the state-of-the-art Language Models (LMs) approximates human perf…