Two public online talks in October (16th, 23rd) on Large Language Models, for accessibility or evaluation (NLPV seminars, Exeter)
We welcome you to the next Natural Language Processing and Vision (NLPV) seminars at the University of Exeter.
Talk 1 Scheduled: Thursday 16 Oct 2025 at 13:00 to 14:00, GMT+1 Location: https://Universityofexeter.zoom.us/j/94505914598?pwd=eXonSrKHuxUNnMCAmiZHicX... (Meeting ID: 945 0591 4598 Password: 903478)
Title: This One or That One? A Bilingual Study on Accessibility via Demonstratives with Multimodal Large Language Models
Abstract: Accessibility describes how easily a speaker can obtain or interact with an object, and it is often conveyed through demonstrative pronouns like “this" and “that" in English or “这” (zhè) and “那” (nà) in Chinese, indicating proximal or distal objects. The proximal vs. distal distinction is not absolute, since it depends on the speaker's viewpoint. Are Multimodal Large Language Models (MLLMs) able to solve accessibility problems based on demonstratives? In this talk, I would like to present some preliminary results on a referent identification task based on a bilingual (English and Chinese), multimodal dataset. In our experiments, all models show significant struggles, and particularly when perspective shifts are introduced.
Speaker's bio: Emmanuele Chersoni got a joint PhD in Language Sciences from Aix-Marseille University and the University of Pisa in 2018, under the supervision of Philippe Blache and Alessandro Lenci. Since 2021, he is an Assistant Professor in Computational Linguistics at the Department of Language Science and Technology of The Hong Kong Polytechnic University. His main research interests include classical distributional semantic models, thematic fit modeling, semantic relations and natural language processing for specialized domains. He has also served as a co-organizer of the *ACL workshop series on Cognitive Modeling and Computational Linguistics from 2019 to 2022.
Talk 2 Scheduled: Thursday 23 Oct 2025 at 15:00 to 16:00, GMT+1 Location: https://Universityofexeter.zoom.us/j/92868830537?pwd=0yvSNEwhIeC3x2Mxn76zOry... (Meeting ID: 928 6883 0537 Password: 100657)
Title: Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts
Abstract: Evaluating natural language generation (NLG) systems is inherently challenging. While human evaluation remains the gold standard, it is difficult to scale and often suffers from inconsistencies and demographic biases. LLM-based evaluation offers a scalable alternative but is highly sensitive to prompt design, where small variations can lead to significant discrepancies. In this talk, I will introduce an inversion learning method that learns effective reverse mappings from model outputs back to their input instructions, enabling the automatic generation of highly effective, model-specific evaluation prompts. This method is simple, requires only a single evaluation sample, and eliminates the need for manual prompt engineering, thereby improving both the efficiency and robustness of LLM-based evaluation.
Speaker's bio: Chenghua Lin is a Full Professor and Chair in Natural Language Processing in the Department of Computer Science at The University of Manchester. His research lies at the intersection of machine learning and natural language processing, with a focus on language generation, multimodal LLMs, and evaluation methods. He currently serves as Chair of the ACL SIGGEN Board, a member of the IEEE Speech and Language Processing Technical Committee, and Associate Editor for Computer Speech and Language. He has received several prizes and awards for his research and academic leadership, including the CIKM Test-of-Time Award, the INLG Best Paper Runner-up Award, and an Honourable Mention for the Scottish Informatics and Computer Science Alliance (SICSA) Supervisor of the Year Award. He has also held numerous program and chairing roles for *ACL conferences, including Documentation Chair for ACL’25, Publication Chair for ACL’23, Workshop Chair for AACL-IJCNLP’22, Program Chair for INLG’19, and Senior Area Chair for EMNLP’20, ACL’22–’23, EACL’23, NAACL’25, and AACL’25.
We will update future talks on the website: https://sites.google.com/view/neurocognit-lang-viz-group/seminars
Joining our *Google group* for future seminar and research information: https://groups.google.com/g/neurocognition-language-and-vision-processing-gr...
Dear all, Please see a reminder for the second public online talk in October by Prof Chenghua Lin (for the NLPV seminar, University of Exeter).
Talk 2 Scheduled: Thursday 23 Oct 2025 at 15:00 to 16:00, GMT+1 Location: https://Universityofexeter.zoom.us/j/92868830537?pwd=0yvSNEwhIeC3x2Mxn76zOry... (Meeting ID: 928 6883 0537 Password: 100657)
Title: Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts
Abstract: Evaluating natural language generation (NLG) systems is inherently challenging. While human evaluation remains the gold standard, it is difficult to scale and often suffers from inconsistencies and demographic biases. LLM-based evaluation offers a scalable alternative but is highly sensitive to prompt design, where small variations can lead to significant discrepancies. In this talk, I will introduce an inversion learning method that learns effective reverse mappings from model outputs back to their input instructions, enabling the automatic generation of highly effective, model-specific evaluation prompts. This method is simple, requires only a single evaluation sample, and eliminates the need for manual prompt engineering, thereby improving both the efficiency and robustness of LLM-based evaluation.
Speaker's bio: Chenghua Lin is a Full Professor and Chair in Natural Language Processing in the Department of Computer Science at The University of Manchester. His research lies at the intersection of machine learning and natural language processing, with a focus on language generation, multimodal LLMs, and evaluation methods. He currently serves as Chair of the ACL SIGGEN Board, a member of the IEEE Speech and Language Processing Technical Committee, and Associate Editor for Computer Speech and Language. He has received several prizes and awards for his research and academic leadership, including the CIKM Test-of-Time Award, the INLG Best Paper Runner-up Award, and an Honourable Mention for the Scottish Informatics and Computer Science Alliance (SICSA) Supervisor of the Year Award. He has also held numerous program and chairing roles for *ACL conferences, including Documentation Chair for ACL’25, Publication Chair for ACL’23, Workshop Chair for AACL-IJCNLP’22, Program Chair for INLG’19, and Senior Area Chair for EMNLP’20, ACL’22–’23, EACL’23, NAACL’25, and AACL’25.
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