3 papers
cs.LG2025
Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training
Jakub Krajewski, Amitis Shidani, Dan Busbridge +2
While scaling laws for Large Language Models (LLMs) traditionally focus on proxy metrics like pretraining loss, predicting downstream task performance has been considered unreliabl…
cs.CL2024
ChatShop: Interactive Information Seeking with Language Agents
Sanxing Chen, Sam Wiseman, Bhuwan Dhingra
The desire and ability to seek new information strategically are fundamental to human learning but often overlooked in current language agent evaluation. We analyze a popular web s…
cs.CL2023
Seq2seq is All You Need for Coreference Resolution
Wenzheng Zhang, Sam Wiseman, Karl Stratos
Existing works on coreference resolution suggest that task-specific models are necessary to achieve state-of-the-art performance. In this work, we present compelling evidence that…