XLLMRP 2025 : 1st Workshop on the Application of LLM Explainability to Reasoning and Planning at COLM2025

1st Workshop on the Application of LLM Explainability to Reasoning and Planning at COLM2025


Website:
https://xllm-reasoning-planning-workshop.github.io/
Submission Deadline: June 23, 2025

We are thrilled to announce the First Workshop on the Application of LLM Explainability to Reasoning and Planning at COLM 2025.
Enabling large language models (LLMs) to reason (e.g., arithmetic reasoning, symbolic reasoning, commonsense reasoning, etc.) and plan (e.g., path-finding, tool use, web navigation, computer use, etc.) has been a popular topic in the past few years. Despite the exciting achievement, there have also been growing concerns about the safety and trustworthiness of these LLM applications, due to our large “unknowns” on how LLMs achieve these capabilities and where they could fail. On the other hand, LLM explainability (broadly including any research explaining or interpreting LLMs) has also attracted increasing attention, but existing research has mostly focused on simplified tasks and hardly yields insights that can be directly applied to realistic reasoning and planning tasks. This discrepancy has consequently raised doubts about the practical meaning of LLM explainability research.
In this workshop, we aim to bring together researchers from various perspectives to discuss the potential and practical applications of model explainability to advancing LLM reasoning and planning. Specifically, the workshop welcomes submissions on the following topics (non-exclusively):
Local explanations (e.g., feature attribution, textual explanations, including CoT type) of LLMs in reasoning and/or planning tasks;
Global explanations (e.g., mechanistic interpretability) of LLMs in reasoning and/or planning tasks;
Applications of explainability to enhance LLM’s effectiveness in reasoning and/or planning tasks;
Applications of explainability to enhance LLM’s safety and trustworthiness in reasoning and/or planning tasks;
User interface development driven by LLM explanations;
Human-LLM collaboration and teaming driven by explanations; and
Explainability-driven, automatic, or human-in-the-loop LLM evaluation.
We warmly invite researchers from both academia and industry with interests in LLM explainability, reasoning, and planning for participation.

IMPORTANT DATES
Submission deadline: June 23, 2025, 23:59 AoE
Acceptance notification: July 24, 2025

WEBSITE: https://xllm-reasoning-planning-workshop.github.io/

Google Group (join for latest updates via email): https://groups.google.com/g/xllm-reasoning-planning-workshop

ORGANIZERS:
Daking Rai, George Mason University
Ziyu Yao, George Mason University
Hanjie Chen, Rice University
Mengan Du, New Jersey Institute of Technology
Shi Feng, George Washington University
Q.Vera Liao, Microsoft Research/University of Michigan
Andreas Madsen, Guide Labs
Abulhair Saparov, Purdue University
Yilun Zhou, Salesforce Research

CONTACT: [email protected]