[CFP] (Due: April 1st, 2026) The 4th Workshop on Towards Knowledgeable Foundation Models (KnowFM) at ACL 2026

[Canyu Chen]

The 4th Workshop on Towards Knowledgeable Foundation Models (KnowFM) at ACL 2026

San Diego, California, United States

Website: https://knowledgeable-lm.github.io/

OpenReview submission portal: https://openreview.net/group?id=aclweb.org/ACL/2026/Workshop/KnowFM

Submission Deadline: April 1st, 2026
Decision Notifications: April 20th, 2026
Camera-Ready Deadline: May 1st, 2026
Workshop Date: July 3, 2026

Workshop Theme

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.

Submission Topics

We welcome submissions on all topics related to knowledgeable foundation models, including:

• Analysis of knowledge within foundation models: how much they know, where that knowledge comes from, and how it is represented
• Enhancing models with existing knowledge sources (knowledge graphs, domain-specific databases, manuals, rules, etc.) during training or inference
• Analyzing and improving RAG (retrieval-augmented generation) systems
• Updating, editing, and erasing knowledge in foundation models
• Knowledge extraction, generation, and distillation using foundation models
• Synthetic data quality and reliability for knowledge-intensive tasks
• Evaluation of knowledge utilization (faithfulness, truthfulness, attribution) by foundation models
• Identification and mitigation of hallucinations and factual errors
• Knowledge conflicts: resolving inconsistencies between parametric memory and retrieved or long-context information
• Knowledge in multimodal foundation models: visual knowledge, cross-modal grounding, and multimodal RAG
• Knowledge-intensive agents: search agents, tool-augmented agents, and agentic RAG systems
• Grounding and knowledge acquisition in multi-step reasoning and planning agents

Paper Awards

We will also announce a Best Paper Award and an Outstanding Paper Award at our workshop.

Submission Instructions
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 (https://openreview.net/group?id=aclweb.org/ACL/2026/Workshop/KnowFM)

Organizers

Organizing Committee: Canyu Chen, Zihan Wang, Qineng Wang, and Manling Li from Northwestern University, alongside Yuji Zhang and Priyanka Kargupta from UIUC, Zoey Sha Li from Amazon, Jinyan Su from Cornell, Sara Vera Marjanović from the University of Copenhagen, and Jeff Z. Pan from the University of Edinburgh.

Advising Committee: Heng Ji and Jiawei Han from UIUC, Mohit Bansal from UNC Chapel Hill, and Isabelle Augenstein from the University of Copenhagen.

Best,

Canyu
canyuchen.com