Title: Going Straight to the Source Ori Ernst

Title: Going Straight to the Source Abstract: Quality assessments of generated text usually focus on the output and the model that produced it, measuring model confidence scores, detecting errors, and performing exhaustive evaluations. However, a critical component is frequently overlooked in this process: the input source. In this talk, I introduce Source Learning: a paradigm that examines the source only, before any text is generated. By analyzing the source alone, we can proactively predict the performance of large language models, identify problematic sentences, and inform interventions without generating any text. Because it bypasses inference, Source Learning is fast, resource‑light, model‑agnostic, and it reveals the true origins of generation errors. Ultimately, it opens a new path for improving generation quality by shifting attention back to where it all begins - the input. Bio: "Ori is an NLP researcher who recently completed a postdoctoral fellowship as an IVADO fellow at McGill University and the Mila AI Institute, under the guidance of Prof. Jackie Cheung. His research centers on text generation, with a focus on multi-document setups, hallucination reduction, and text attribution. He earned his Ph.D. from the Natural Language Processing Lab at Bar-Ilan University. Ori has published extensively at leading ACL conferences, including a Best Paper Runner-Up award, and co-organizes the New Frontiers in Summarization (NewSumm) workshop. He has also held research-oriented roles in industry, including at Amazon, IBM, and Intel.