1st CfP: Special Session on Emergent Phenomena in Deep Representations and Large Language Models @IJCNN 2024 & IEEE WCCI 2024
Apologies for cross-posting
------------------------------------------------------ Dear colleagues,
We invite you to submit to the special session on “Emergent Phenomena in Deep Representations and Large Language Models” as a part of IJCNN 2024 and IEEE WCCI 2024, which will be located in Yokohama, Japan.
We are looking forward to your contributions.
Please find the CfP below.
Best wishes, On behalf of Organising Committee Özge Alacam ------------------------------------------------------ First Call for Papers: Special Session on Emergent Phenomena in Deep Representations and Large Language Models @IJCNN 2024 & IEEE WCCI 2024:
Deep learning models trained on large datasets have shown spectacular performance in a wide range of tasks demonstrated by current applications of Large Language Models. However, recent works have shown that the abilities large machine learning models acquire often emerge unpredictably with increasing model complexity or training dataset size. These emergent phenomena include the unexpected appearance of abilities for which the model was not explicitly trained, but they might also be related to unexpected performance boosts due to the increased model complexity. Emergent phenomena are not always beneficial: larger models may pick up new biases from the training data or start hallucinating.
To move towards increasingly sustainable, reliable, and explainable applications of AI systems, it is necessary to increase the understanding of the mechanisms surrounding emergent phenomena. Moreover, this effort provides increased insight into the learning process behind the acquisition of abilities of large models to perform specific tasks. Important research questions relate to the definition of emergent phenomena, their causes (what controls which abilities are acquired and when?), training efficiency, and training data quality (e.g., acquiring desired abilities with less computational effort), prompting strategies to get or test for desired model behaviour (e.g., a chain of thought), and further verification methods of model abilities and properties.
The primary goal of this special session is (i) to discuss the emergent abilities and risks in deep neural networks and representations from very different angles and (ii) facilitate networking and encourage collaboration between various research fields that approach this issue from different perspectives, like computational linguistics, ethics in AI, computer science, physics, etc.
Topics of interest include, but are not limited to: • The definition of emergence in the context of NLP and ML • Prompting strategies • Physics-based/inspired analyses (e.g. phase transitions in ML models) • Explainability and interpretability (XAI) • Evaluation measures for model ability, monitoring strategies, assessment of model abilities (e.g. technical or psychology-based) • Knowledge distillation, model pruning, energy-efficient models. • Mitigation strategies for emergent risks and model deterioration. • Fine-tuning and Retrieval-augmented generation (RAG) • Papers focusing on specific emergent phenomena (reasoning, creativity, double descent phenomena etc.)
The website for the call for papers is accessible at https://sites.google.com/view/emergenn/call-for-papers
Organising Committee: ------------------------------ • Dr. Özge Alacam (Ludwig-Maximilian University & Uni Bielefeld, Germany) • Dr. Michiel Straat (Uni Bielefeld, Germany) • Prof. Dr. Hinrich Schütze (Ludwig-Maximilian University, Germany) • Prof. Dr. Alessandro Sperduti (University of Padova, Italy)
Important Dates: ------------------------------ • January 15, 2024 - Paper Submission Deadline • March 15, 2024 - Notification of Acceptance • May 1, 2024 - Camera-ready Deadline & Early Registration Deadline • June 30 - July 5, 2024 - Main Conference (IEEE WCCI 2024, Yokohama, Japan)
* All deadlines are 11:59 PM UTC-12:00 ("anywhere on Earth")
Submission Format and Platform: ------------------------------
• Submissions will be through the IEEE WCCI 2024 Submission page https://edas.info/login.php?rurl=aHR0cHM6Ly9lZGFzLmluZm8vTjMxNjE0P2M9MzE2MTQ=.
• Each paper is limited to 8 pages, including figures, tables, and references. Please refer to the author guidelines provided by IEEE WCCI 2024 • Please specify during the submission that your paper is intended for the Special Session: Emergent Phenomena in Deep Representations and Large Language Models. • Special session webpage: https://sites.google.com/view/emergenn/call-for-papers • IEEE WCCI 2024 webpage: https://2024.ieeewcci.org/
Contact information: ------------------------------ • Özge Alacam : oezge.alacam@uni-bielefeld.de • Michiel Straat : mstraat@techfak.uni-bielefeld.de
Apologies for cross-posting ------------------------------------------------------ Dear colleagues,
We invite you to submit to the special session on “Emergent Phenomena in Deep Representations and Large Language Models” as a part of IJCNN 2024 and IEEE WCCI 2024, which will be located in Yokohama, Japan.
We are looking forward to your contributions.
Please find the CfP below.
Best wishes, On behalf of Organising Committee Özge Alacam ------------------------------------------------------ First Call for Papers: Special Session on Emergent Phenomena in Deep Representations and Large Language Models @IJCNN 2024 & IEEE WCCI 2024:
Deep learning models trained on large datasets have shown spectacular performance in a wide range of tasks demonstrated by current applications of Large Language Models. However, recent works have shown that the abilities large machine learning models acquire often emerge unpredictably with increasing model complexity or training dataset size. These emergent phenomena include the unexpected appearance of abilities for which the model was not explicitly trained, but they might also be related to unexpected performance boosts due to the increased model complexity. Emergent phenomena are not always beneficial: larger models may pick up new biases from the training data or start hallucinating.
To move towards increasingly sustainable, reliable, and explainable applications of AI systems, it is necessary to increase the understanding of the mechanisms surrounding emergent phenomena. Moreover, this effort provides increased insight into the learning process behind the acquisition of abilities of large models to perform specific tasks. Important research questions relate to the definition of emergent phenomena, their causes (what controls which abilities are acquired and when?), training efficiency, and training data quality (e.g., acquiring desired abilities with less computational effort), prompting strategies to get or test for desired model behaviour (e.g., a chain of thought), and further verification methods of model abilities and properties.
The primary goal of this special session is (i) to discuss the emergent abilities and risks in deep neural networks and representations from very different angles and (ii) facilitate networking and encourage collaboration between various research fields that approach this issue from different perspectives, like computational linguistics, ethics in AI, computer science, physics, etc.
Topics of interest include, but are not limited to: • The definition of emergence in the context of NLP and ML • Prompting strategies • Physics-based/inspired analyses (e.g. phase transitions in ML models) • Explainability and interpretability (XAI) • Evaluation measures for model ability, monitoring strategies, assessment of model abilities (e.g. technical or psychology-based) • Knowledge distillation, model pruning, energy-efficient models. • Mitigation strategies for emergent risks and model deterioration. • Fine-tuning and Retrieval-augmented generation (RAG) • Papers focusing on specific emergent phenomena (reasoning, creativity, double descent phenomena etc.)
The website for the call for papers is accessible at https://sites.google.com/view/emergenn/call-for-papers
Organising Committee: ------------------------------ • Dr. Özge Alacam (Ludwig-Maximilian University of Munich & Uni Bielefeld, Germany) • Dr. Michiel Straat (Uni Bielefeld, Germany) • Prof. Dr. Hinrich Schütze (Ludwig-Maximilian University of Munich, Germany) • Prof. Dr. Alessandro Sperduti (University of Padova, Italy)
Important Dates: ------------------------------ • January 29, 2024 - Paper Submission Deadline • March 15, 2024 - Notification of Acceptance • May 1, 2024 - Camera-ready Deadline & Early Registration Deadline • June 30 - July 5, 2024 - Main Conference (IEEE WCCI 2024, Yokohama, Japan)
* All deadlines are 11:59 PM UTC-12:00 ("anywhere on Earth")
Submission Format and Platform: ------------------------------
• Submissions will be through the IEEE WCCI 2024 Submission page https://edas.info/login.php?rurl=aHR0cHM6Ly9lZGFzLmluZm8vTjMxNjE0P2M9MzE2MTQ=.
• Each paper is limited to 8 pages, including figures, tables, and references. Please refer to the author guidelines provided by IEEE WCCI 2024 • Please specify during the submission that your paper is intended for the Special Session: Emergent Phenomena in Deep Representations and Large Language Models. • Special session webpage: https://sites.google.com/view/emergenn/call-for-papers • IEEE WCCI 2024 webpage: https://2024.ieeewcci.org/
Contact information: ------------------------------ • Özge Alacam : oezge.alacam@uni-bielefeld.de • Michiel Straat : mstraat@techfak.uni-bielefeld.de
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Ozge Alacam