Morocco

Large language models (LLMs) have demonstrated strong performance on emotion understanding tasks, yet their ability to faithfully generate emotionally aligned text remains less well understood. We propose a semantic evaluation framework that jointly assesses emotion understanding, emotion generation, and internal consistency, using a VAE-based emotion cost matrix that captures graded semantic similarity between emotion categories. Our framework introduces four complementary metrics that disentangle baseline understanding, human-perceived emotion in generated text, generation quality, and model consistency. Experimental results show that while understanding and consistency scores are highly correlated, emotion generation exhibits substantially weaker correlations with these metrics. These findings motivate the development of specialized evaluation protocols that independently measure emotional understanding and generation, enabling more reliable assessments of LLM emotional intelligence.

EACL 2026

Disentangling Emotion Understanding and Generation in Large Language Models

emotional text evaluation

emotional intelligence

large language models

The 46th Annual Meeting of the Cognitive Science Society was an in-person meeting held in Rotterdam, The Netherlands at the Postillion Hotel & Conference Centre.

**ON-DEMAND PROGRAM ACCESS AFTER THE CONFERENCE**<br>
Recordings of the invited program are now available. You can access them by clicking on the 'Schedule' icon on the left. Select view by week and navigate to the day and time slot of the recording you wish to view. Click on the time slot to access the recording.

Recordings available (click on each recording to view):

**Thursday, July 25**<br>
0900: [Keynote Speaker Morgan Barense on Enhancing real-world event memory](https://underline.io/events/465/sessions/17997/lecture/99084-dynamics-between-minds-and-the-environment)<br>
1000: [Gleitman Award Winner Isabelle Dautriche on Language Foundations: Insights from acquisition, communication, cognition, and more](https://underline.io/events/465/sessions/18000/lecture/99085-gleitman-talk)<br>
1415: [Invited Symposium: Dynamics between minds and the environment](https://underline.io/events/465/sessions?eventSessionId=18016&searchGroup=lecture)<br>
1700: [Rumelhart Prize Presentation Speaker Alison Gopnik on Exploit, explore, empower: Three ages and three intelligences](https://underline.io/events/465/sessions/18028/lecture/99161-rp1-rumelhart-prize-presentation)<br>

**Friday, July 26**<br>
0900: [C.L. de Carvalho-Heineken Prize Keynote Speaker Kia Nobre on Focusing in memory](https://underline.io/events/465/sessions/18035/lecture/99162-hp1-c-l-de-carvalho-heineken-prize-keynote-address)<br>
1030: [Rumelhart Symposium: Childhood as exploration](https://underline.io/events/465/sessions/18038/lecture/100346-childhood-as-exploration)<br>
1415: [Elman Prize Symposium](https://underline.io/events/465/sessions?eventSessionId=18053&searchGroup=lecture)<br>
1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture)<br>
1745: [Keynote Speaker Andrea E. Martin on Neural dynamics encode the structure and statistics of language](https://underline.io/events/465/sessions/18077/lecture/99271-neural-dynamics-encode-the-structure-and-statistics-of-language)<br>

**Saturday, July 27**<br>
0900: [Keynote Speaker Gregor Schöner on How higher cognition emerges from the dynamics of strongly interacting neural populations](https://underline.io/events/465/sessions/18083/lecture/99273-dynamics-within-the-mind)<br>
1000: [Glushko Talks](https://underline.io/events/465/sessions?eventSessionId=18086&searchGroup=lecture)<br>
1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture)<br>


#### **CogSci 2024 Program Book**
<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/cble/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>


**The Program Book can be downloaded [here](https://drive.google.com/file/d/1yRVp1cBqRnAbdYi4GXWV_pZeyWNsRquv/view?usp=sharing).**

#### **Letter from the President**

Welcome to the 2024 Meeting of the Cognitive Science Society in Rotterdam! CogSci 2024 brings together a large community of cognitive scientists who have traveled here from around the world, as well as a smaller group of remote presenters. I would like to extend an especially warm welcome to our first-time CogSci meeting attendees, and hope that 
their experience inspires them to join our future meetings for many years to come.

I want to thank this year’s conference co-chairs, Larissa K Samuelson, Stefan Frank, Mariya Toneva, Allyson Mackey, and Eliot Hazeltine, for putting together an amazing series of invited talks and symposia around the theme of Dynamics of Cognition. The co-chairs also deserve credit for handling a record-breaking number of submissions and producing a truly exciting conference program 

Beyond the invited talks and symposia dedicated to this year’s conference theme, I would like to highlight the presentations and events honoring the 
Rumelhart, Elman, Gleitman and C .L . de Carvalho Heineken prize winners, including the Rumelhart reception on Thursday. As you may know, we are recording all of the invited talks and symposia, and all prize-winners’ talks, and will be making this content available after the conference. 

The Cognitive Science Society relies on the volunteer efforts of our Governing Board members who work on matters related to our membership; conference policies; diversity and inclusion, international, and outreach initiatives; prizes and much more. We welcome interest from Society members who would like to become more involved in what we do. The 
Society is deeply grateful to Rick Dale and Andrea Bender, who serve as editors of the two Society journals, Cognitive Science and Topics in Cognitive Science respectively. We especially acknowledge our editors’ generosity in donating their stipends each year to D&I and outreach efforts. 

This year’s Annual Meeting depends critically on the support of Marischal De Armond and his team at Podium Conference Specialists: Sharon Zwack, Cendrine De Vis, Sarah-Kate Burke, and Rachel Waller worked tirelessly to secure the venue and handled a multitude of logistical and organizational issues, from arranging coffee breaks and receptions to making sure that a huge number of posters fits comfortably into the available space. Finally, and importantly, I would like to acknowledge the Cognitive Science Society Executive Officer, Erica Wojcik, for managing the complex and ever expanding sphere of our Society’s activities.

I hope you enjoy this year’s meeting and the many different opportunities to engage with the vibrant community of cognitive scientists gathered here. I also hope you get a chance to explore the great city of Rotterdam with its many attractions, museums, restaurants and scenic views. If you’d like to talk more about our Society, or simply want to say hello, please stop by!

![](https://assets.underline.io/markdown_image/1/image/3c66d0e21fff30e204df1138ffd15173.jpeg)

**Anna Papafragou**  
President, Cognitive Science 
Society 2023-2024

**CODE OF CONDUCT**<br>
By attending the CogSci 2024 Conference, you are required to adhere to the society’s [Code of Conduct](https://drive.google.com/file/d/16-6KkptF0Gn3ZYGJDlpqpwTdImw45Ng0/view?usp=drive_link).

**ABOUT THE COGNITIVE SCIENCE SOCIETY**<br>
The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.
<br><br>

You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

It looks like you are not registered for this event. 

To access the site please register [**here**](https://cognitivesciencesociety.org/registration/). 

Please register!

CogSci 2024

The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

workshop paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)*<br><br>

<html><button style="display: inline-flex; align-items: center; justify-content: center; white-space: nowrap; border-radius: 9999px; font-weight: bold; background: #7c3aed; color: white; font-family: 'Space Grotesk', sans-serif; height: 40px; font-size: 16px; padding: 0 20px; border: none; cursor: pointer" onclick="window.open('https://underline.io/events/522/reception','_blank')">Go to Workshops and Tutorials Program</button></html>
<br><br><br>

To access this event page, you need to log in with the **email address you registered with**. <br>Access credentials will be sent to your email from Underline -  subject line "Welcome to EACL 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://2026.eacl.org/registration/) first.

**Online Registration Form**: https://acl.swoogo.com/eacl2026

Registration Required

Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

In an era of rapid misinformation spread, LLMs have emerged as tools for assessing news credibility at scale. However, the assessments are influenced by social and cultural biases. Studies investigating political bias, compare model credibility ratings with expert credibility ratings. Comparing LLMs to the perceptions of political camps extends this approach to detecting similarities in their biases. We compare LLM-generated credibility and bias ratings of news outlets with expert assessments and stratified political opinions collected through surveys. We analyse three models (Llama 3.3 70B, Mixtral 8x7B, and GPT-OSS 120B) across 47 news outlets from two countries (U.S. and Germany). We found that models demonstrated consistently high alignment with expert ratings, while showing weaker and more variable alignment with public opinions. For US-American news outlets all models showed stronger alignment with center-left perceptions, while for German news outlets the alignment is more diverse.

News Credibility Assessment by LLMs and Humans: Implications for Political Bias

The transition of Large Language Models (LLMs) from exploratory tools to active "silicon subjects" in social science lacks extensive validation of operational validity. This study introduces Conditioned Comment Prediction (CCP), a task in which a model predicts how a user would comment on a given stimulus by comparing generated outputs with authentic digital traces. This framework enables a rigorous evaluation of current LLM capabilities with respect to the simulation of social media user behavior. We evaluated open-weight 8B models (Llama-3.1, Qwen3, Ministral) in English, German, and Luxembourgish language scenarios. By systematically comparing prompting strategies (explicit vs. implicit) and the impact of Supervised Fine-Tuning (SFT), we identify a critical form vs. content decoupling in low-resource settings: while SFT aligns the surface structure of the text output (length and syntax), it degrades semantic grounding. Furthermore, we demonstrate that explicit conditioning (generated biographies) becomes redundant under fine-tuning, as models successfully perform latent inference directly from behavioral histories. Our findings challenge current "naive prompting" paradigms and offer operational guidelines prioritizing authentic behavioral traces over descriptive personas for high-fidelity simulation.

Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction

We propose an agentic data augmentation method for Aspect-Based Sentiment Analysis (ABSA) that uses iterative generation and verification to produce high-quality synthetic training examples. To isolate the effect of agentic structure, we also develop a closely matched prompting-based baseline using the same model and instructions. Both methods are evaluated across three ABSA subtasks—Aspect Term Extraction (ATE), Aspect Sentiment Classification (ATSC), and Aspect Sentiment Pair Extraction (ASPE)—four SemEval datasets, and two encoder–decoder models: T5-Base and Tk-Instruct. Our results show that the agentic augmentation outperforms raw prompting in label preservation of the augmented data, especially when the tasks require aspect term generation. In addition, when combined with real data, agentic augmentation provides higher gains, consistently outperforming prompting-based generation. These benefits are most pronounced for T5-Base, while the more heavily pretrained Tk-Instruct exhibits smaller improvements. As a result, augmented data helps T5-Base achieve comparable performance with its counterpart.

Label-Consistent Data Generation for Aspect-Based Sentiment Analysis Using LLM Agents

Antisocial behavior (ASB) on social media encompasses online behaviors that harm individuals, groups, or platform ecosystems, including hate speech, harassment, cyberbullying, trolling, and coordinated abuse. While most prior work has focused on detecting harm after it occurs, a growing body of research on ASB prediction seeks to forecast future harmful outcomes before they materialize, including—but not limited to—hate-speech diffusion, conversational derailment, and user recidivism. However, this emerging field remains fragmented, with limited conceptual grounding and few integrative frameworks. This paper establishes a foundation for ASB prediction by introducing a structured taxonomy spanning temporal, structural, and behavioral dimensions. Drawing on 49 machine learning studies identified through a literature review, we map predictive goals to datasets, modeling choices, and evaluation practices, and identify key challenges, including the lack of standardized benchmarks, the dominance of text-centric representations, and trade-offs between accuracy and interpretability. We conclude by outlining actionable directions toward more robust, generalizable, and responsible ASB prediction systems.

Antisocial Behavior Prediction: A Survey and Practical Guide

Speech emotion recognition (SER) is a compelling yet challenging research area with substantial practical relevance, particularly in enhancing human–machine interaction. Despite considerable progress in the field, the scarcity of realistic datasets that reflect real-world conditions makes it difficult to analyze system behavior in practice and can lead to degraded performance in industrial applications. In this study, we propose a system that detects negative emotions at each turn in a conversation by leveraging both linguistic and acoustic features. The approach is evaluated on real-world data, with a particular focus on identifying and responding to negative emotion in customer support scenarios. Designed for real-time application, the system is suitable for live deployment in call center environments. Furthermore, we propose an effective prompting strategy for using large language models (LLMs) as annotators, generating labeled data used to fine-tune small language models that achieve performance on par with the LLM used for annotation, while remaining suitable for real-time deployment.

Real-Time Mitigation of Negative Emotion in Customer Care Calls

Alongside its literal meaning, text also carries implicit social signals: information that is used by the reader to assign the author of the text a specific identity or make assumptions about the author's character. The reader creates a mental image of the author which influences the interpretation of the presented information. This is especially relevant for argumentative text, where the credibility of the information might depend on who provides it. We therefore focus on the question: How do readers of an argument imagine its author? Using the ContArgA corpus, we study arguments annotated for convincingness and perceived author properties (level of education and Big Five personality traits). We find that annotators perceive an author to be similar to themselves when they agree with the stance of the argument. We also find that the envisioned personality traits and education level of the author are statistically significantly correlated with the argument's convincingness. We conduct experiments with four generative LLMs and a RoBERTa-based regression model showing that LLMs do not replicate the annotators judgments. Argument convincingness can however provide a useful signal for modeling perceived author personality when it is explicitly used during training.

Says Who? Argument Convincingness and Reader Stance Are Correlated with Perceived Author Personality

Sarcasm detection, with its figurative nature, poses unique challenges for affective systems designed to perform sentiment analysis. While these systems typically perform well at identifying direct expressions of emotion, they struggle with sarcasm's inherent contradiction between literal and intended sentiment. Since transformer-based language models (LMs) are known for their efficient ability to capture contextual meanings, we propose a method that leverages LMs and prototype-based networks, enhanced by sentiment embeddings to conduct interpretable sarcasm detection. Our approach is intrinsically interpretable without extra post-hoc interpretability techniques. We test our model on three public benchmark datasets and show that our model outperforms the current state-of-the-art. At the same time, the prototypical layer enhances the model's inherent interpretability by generating explanations through similar examples in the reference time. Furthermore, we demonstrate the effectiveness of incongruity loss in the ablation study, which we construct using sentiment prototypes.

A Transformer and Prototype-based Interpretable Model for Contextual Sarcasm Detection

Automated Fact-Checking (AFC) relies on claim extraction as a first step, yet existing methods largely overlook the multimodal nature of today’s misinformation. Social media posts often combine short, informal text with images such as memes, screenshots, and photos, creating challenges that differ from both text-only claim extraction and well-studied multimodal tasks like image captioning or visual question answering. In this work, we present the first benchmark for multimodal claim extraction from social media, consisting of posts containing text and one or more images, annotated with gold-standard claims derived from real-world fact-checkers. We evaluate state-of-the-art multimodal LLMs (MLLMs) under a three-part evaluation framework (semantic alignment, faithfulness, and decontextualization) and find that baseline MLLMs struggle to model rhetorical intent and contextual cues. To address this, we introduce MICE, an intent-aware framework which shows improvements in intent-critical cases.

Multimodal Claim Extraction for Fact-Checking

Data augmentation (DA) using large language models (LLMs) is a cost-effective method for generating synthetic data, particularly for tasks with scarce datasets. However, its potential remains largely underexplored, both in terms of augmentation configuration and evaluation of synthetic data. This paper investigates LLM-based synthetic data generation for irony and sarcasm, two subjective and context-dependent forms of figurative language. We propose a multi-aspect evaluation framework assessing synthetic data's utility-plausibility and extrinsic-intrinsic dimensions through four aspects: predictive performance, sample diversity, linguistic properties, and human judgment. Our findings indicate that other aspects of evaluation, like diversity and linguistic features, do not necessarily correlate with an increase in predictive performance, underscoring the importance of multi-faceted evaluation. This work highlights the potential of LLM-based DA for irony and sarcasm detection, offering insights into the linguistic competence of LLMs. As synthetic data becomes increasingly prevalent, our framework offers a broadly applicable and crucial evaluation method, particularly for linguistically complex tasks.

A Multi-Aspect Evaluation Framework for Synthetic Data: Case Study on Irony and Sarcasm

Over the past four decades, work at the intersection of Arabic and technology has evolved alongside major technical breakthroughs and shifting regional and global political dynamics. In this talk, I revisit the development of Arabic NLP and AI, from early foundational efforts to today’s large-scale generative systems, and highlight how each phase has engaged with distinct aspects of Arabic’s linguistic complexity. I reflect on the language’s ambiguous orthography, rich morphology, diglossic landscape, and wide geographic and historical reach, and on the growing research community committed to ensuring that AI technologies meaningfully support Arabic and its diverse cultural expressions. I conclude with a forward-looking vision for building a cohesive and sustainable ecosystem that advances Arabic in AI through strengthened training, deeper collaboration, and sustained innovation for the next generation of researchers and practitioners.

Downloads

Next from EACL 2026

News Credibility Assessment by LLMs and Humans: Implications for Political Bias

Similar lecture

A Computational Framework for Emotion Understanding

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from EACL 2026

News Credibility Assessment by LLMs and Humans: Implications for Political Bias

Similar lecture

A Computational Framework for Emotion Understanding

Downloads