36.1791, Confs: 1st Workshop on Optimal Reliance and Accountability in Interactions with Generative Language Models (Canada)

[The LINGUIST List]

LINGUIST List: Vol-36-1791. Mon Jun 09 2025. ISSN: 1069 - 4875.

Subject: 36.1791, Confs: 1st Workshop on Optimal Reliance and Accountability in Interactions with Generative Language Models (Canada)

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Date: 06-Jun-2025
From: Nikhil Krishnaswamy [nkrishna at colostate.edu]
Subject: 1st Workshop on Optimal Reliance and Accountability in Interactions with Generative Language Models


1st Workshop on Optimal Reliance and Accountability in Interactions
with Generative Language Models
Short Title: ORIGen

Date: 10-Oct-2025 - 10-Oct-2025
Location: Montreal, Canada
Meeting URL: https://origen-workshop.github.io

Linguistic Field(s): Cognitive Science; Computational Linguistics;
Discourse Analysis; Linguistic Theories; Philosophy of Language

Submission Deadline: 20-Jun-2025

The First Workshop on Optimal Reliance and Accountability in
Interactions with Generative Language Models (ORIGen) will be held in
conjunction with the Second Conference on Language Modeling (COLM) at
the Palais des Congrès in Montreal, Quebec, Canada, on October 10,
2025!
With the rapid integration of generative AI, exemplified by large
language models (LLMs), into personal, educational, business, and even
governmental workflows, such systems are increasingly being treated as
“collaborators” with humans. In such scenarios, underreliance or
avoidance of AI assistance may obviate the potential speed,
efficiency, or scalability advantages of a human-LLM team, but
simultaneously, there is a risk that subject matter non-experts may
overrely on LLMs and trust their outputs uncritically, with
consequences ranging from the inconvenient to the catastrophic.
Therefore, establishing optimal levels of reliance within an
interactive framework is a critical open challenge as language models
and related AI technology rapidly advances.
 - What factors influence overreliance on LLMs?
 - How can the consequences of overreliance be predicted and guarded
against?
 - What verifiable methods can be used to apportion accountability for
the outcomes of human-LLM interactions?
 - What methods can be used to imbue such interactions with
appropriate levels of “friction” to ensure that humans think through
the decisions they make with LLMs in the loop?
The ORIGen workshop provides a new venue to address these questions
and more through a multidisciplinary lens. We seek to bring together
broad perspectives from AI, NLP, HCI, cognitive science, psychology,
and education to highlight the importance of mediating human-LLM
interactions to mitigate overreliance and promote accountability in
collaborative human-AI decision-making.
Submissions are due June 20, 2025. Please see our call for papers [1]
for more!
[1] https://origen-workshop.github.io/submissions/



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