ATTRIB Workshop, NeurIPS’26
Data Attribution Should Guide Pretraining
Glenn Matlin, Chandreyi Chakraborty, Mohammed Rehan Parwani, Saehee Eom, Anthony Wen-Ming Zang, Akhil Theerthala, Arjun Chatterjee, Tejas Khandwekar, Brandon Michaels, Alvin Deng, Mark Riedl
Pretraining data curation decides what a model learns from, yet the properties that make a document worth learning from depend on what the model already knows. We argue that training-data attribution should steer pretraining. Influence-driven selectors already act on attribution to improve a run; we argue that the field should ask more of that attribution. A ranking of documents serves the run it was computed for, whereas a data property that explains a gain can be tested on new data and, where it holds, written into a curation criterion, including where familiar quality signals are known to diverge from measured usefulness. We specify a feedback loop that interprets attributed sources, tests the data changes they suggest, and folds the outcomes back into the next run and its curation criteria, and we state what evidence each step requires and what none of them establishes. Historical contribution, an understandable explanation, and future data value are distinct claims that require distinct evidence. We set out a research agenda for establishing when attribution improves curation decisions over existing methods at matched cost.
Social Simulations Workshop, COLM’26
Role Steering of Language Models for Social Simulations
Isaac Song, Mohammed Rehan Parwani, Glenn Matlin, Emile Anand, Akhil Theerthala, Arjun Chatterjee, Anthony Wen-Ming Zang, Maria Kostylew, Yonadav G. Shavit, Sebastien Krier, Mark Riedl
An activation-steering screening workflow for role-conditioned language-model agents in social simulations. Across 275 roles, role-specific directions improve judged role-profile alignment over an assistant-axis control while preserving lexical diversity; 38 roles decline across every measured dimension, showing why steering strength should be selected per role rather than applied uniformly.
Agentic AI in Financial Services Workshop, AAAI’26
FinForge: Semi-Synthetic Financial Benchmark Generation
Glenn Matlin, Akhil Theerthala, Anant Gupta, Anirudh JM, Rayan Castilla, Yi Mei Ng, Sudheer Chava
A scalable semi-synthetic pipeline for building finance-specific LM benchmarks from authoritative sources using expert-guided curation and controlled LM synthesis. FinForge-5k contains 5,000+ human-validated question-answer pairs across 11 finance subdomains and reveals substantial gaps in financial reasoning, even as leading models approach 80% accuracy.
