Hi colleagues,

We invite submissions to the 4th Workshop on Towards Knowledgeable Foundation Models (KnowFM), virtually co-located with ACL 2026.

Foundation models have demonstrated remarkable capabilities in storing and utilizing knowledge acquired during pre-training. Yet as these models scale and are deployed in increasingly complex settings, critical challenges remain: How can we reliably assess what a model knows? How do we reconcile conflicting knowledge from parametric memory and retrieved context? How can we keep model knowledge up-to-date without compromising reasoning abilities? And how do we extend these capabilities beyond text to multimodal and agentic settings?

This workshop brings together researchers working on different stages and aspects of the knowledge lifecycle, from structured and unstructured knowledge sources to knowledge acquired and synthesized by models themselves, to discuss how knowledge should be represented, acquired, verified, and applied in the era of foundation models. This includes knowledge emergence through pre-training, external knowledge injection, knowledge updating and editing, as well as probing and generation of knowledge.

We welcome work on knowledge analysis in LMs, knowledge-enhanced training/inference, retrieval-augmented generation (RAG), model editing, truthfulness and faithfulness evaluation, hallucination mitigation and factual error correction.

Location: San Diego, California
Workshop date: July 3, 2026
Submission deadline: April 1, 2026 (AoE)

We welcome two types of papers: regular workshop papers and non-archival submissions. Only regular workshop papers will be included in the workshop proceedings. Review process will be double-blind. All submissions should be in PDF format following the ACL template (8 pages for main text) and made through OpenReview submission portal

Tentative speakers include Yoav Artzi, Sewon Min, Eunsol Choi, Mohit Iyyer, Danqi Chen, Yulia Tsvetkov, and Xin Luna Dong.

More information is available at our website: https://knowledgeable-lm.github.io/