LLMSS Conference Hong Kong 2026 – Talking to Machines

Call for Papers: Inaugural Large Language Models and the Social Sciences Conference (LLMSS 2026)

Date: 14 – 16 October
Venue: City University of Hong Kong (CityU)

The Large Language Models and the Social Sciences 2026 (LLMS 2026) conference is an interdisciplinary forum for scholars and practitioners to explore the intersection of advanced large language models (LLMs) and social science research. It will bring together AI researchers (computer scientists, engineers, machine learning experts, applied mathematicians), industry practitioners (e.g., health sciences, legal and fintech) government representatives and social scientists from fields like political science, sociology, economics, and communication. Participants will share cutting-edge developments in LLM technology, novel applications to social data, and insights gained from cross-disciplinary collaboration. The conference is organized by Peking University’s Analytics Lab for Global Risk Politics, CityU’s Computational Social Sciences Lab, and University of Oxford’s Nuffield College Talking to Machines Initiative, with support from the European Political Science Association (EPSA).

LLMS 2026 comes at an early stage of integrating LLM-based methods into social science workflows. This conference provides a timely international forum to showcase innovative research, exchange ideas, and receive constructive feedback. The workshop-style format – including research presentations with designated discussants and Q&A – is designed to foster open discussion, collaboration, and methodological advancement among participants from diverse fields. We especially encourage interdisciplinary work and aim to cultivate a feedback-oriented environment that sparks new partnerships between the technical AI community and social scientists.

Core Research Themes

We invite submissions spanning a broad range of topics at the nexus of LLM development and the social sciences. Specific areas of interest include, but are not limited to, the following themes. (Submissions may be theoretical or applied, and we also welcome papers that introduce new tools, datasets, libraries, or platforms to support LLM-driven social science research).

Data Generation & Collection

LLMs in Experimentation: Using AI agents as survey respondents, interviewers, experimental subjects or confederates in social science studies.
Adaptive Experiment Design: LLM-guided design of surveys and experiments (e.g. dynamically generating or refining treatment vignettes and protocols).
Synthetic Data & Augmentation: Generating synthetic text or multimedia data with LLMs to augment training datasets or simulate social scenarios.
Dataset Curation with LLMs: Large-scale compilation of text, image, video, and audio corpora for social research, and LLM-assisted data labeling and annotation.

LLM Applications in Social Science Research

Text Analysis & Classification: LLM-based methods for content analysis, topic classification, sentiment analysis, and embedding-based representations of social science text data.
Causal Inference from Text: Using LLMs to design or interpret experiments (e.g. estimating treatment effects from text and image vignettes created for studies).
Open-Ended Responses & Conversations: Analyzing interviews, focus group transcripts, social media conversations, and open-ended survey responses with the help of LLMs.
Domain-Specific Applications: Innovative uses of LLMs in domains such as political communication, public policy, economic modeling, law, and cultural analysis.

LLM Development & Adaptation

Interpretability and Explanation: Techniques for interpreting LLM decisions and outputs (model explainability, transparency, probing of internal representations).
Fine-Tuning & Domain Adaptation: Methods for fine-tuning or adapting LLMs to domain-specific tasks and datasets in the social sciences (including low-resource or specialty corpora).
Prompt Engineering: Strategies for prompt design and conditioning that improve LLM performance, reliability, and factuality in social science use-cases.
Evaluation & Benchmarking: Developing evaluation protocols, benchmark tasks, and metrics for LLMs (including performance benchmarking on social science tasks, bias and fairness audits, and behavioral analysis of models).
Model Behavior & Emergent Capabilities: Characterizing LLM behavior, biases, and emergent capabilities (e.g. consistency, truthfulness, adaptability), and identifying failure modes or hallucinations.
Multimodal & New Architectures: Advances in LLM architectures, including multimodal models that integrate text with images, audio, or video, and other emerging model innovations relevant to social science data.

Tools, Platforms & Infrastructure

Research Tools and Libraries: Development of software tools, libraries, or frameworks (preferably open-source) to facilitate LLM-based analysis and workflows for social scientists.
Platforms and Pipelines: LLM-driven research platforms, toolchains, or pipelines that integrate LLMs into data collection, analysis, or visualization processes (for example, interactive analysis notebooks, API integrations, or collaborative environments for human-AI research).
Scalability and Deployment: Practical challenges and solutions for deploying LLMs in real-world settings – including scalable model serving, computing infrastructure for large models, cost-efficient inference, and integrating LLMs into organizational or public-facing applications.
Reproducibility & Best Practices: Infrastructure and methodological best practices to ensure reproducible LLM experiments (versioning of models/prompts, evaluation standardization, and result validation).

Ethics, Policy & Societal Impact

Ethical & Responsible AI: Ethical challenges in using LLMs for research and in deployment (e.g. issues of privacy, consent, misinformation, and the responsible design of human-AI interactions).
Fairness and Bias: Identifying and mitigating biases in LLM training data or outputs; ensuring fairness and equity when applying LLMs across different populations or languages.
Policy and Governance: Implications of widespread LLM adoption for institutions and policy – including regulation of AI, legal considerations, and governance frameworks for LLM use in society.
Societal & Economic Impact: The broader impact of LLMs on society and the economy (such as effects on labor markets, education, media, and public discourse) and how social scientists can study these changes.
Robustness & Reproducibility: Ensuring reproducibility and robustness in LLM-based research findings (validation of results, model stability, and transparent reporting of methods), as well as discussions of safety and alignment for LLMs used in sensitive social contexts.

Conference Format

Invited Keynote Talks: Talks by leading experts from academia and industry at the forefront of LLM research and applications.
Research Paper and Poster Sessions: Presentations of accepted papers and poster displays for work-in-progress, with ample time for questions and discussion.
Interactive Workshop Presentations: Seminar-style sessions where researchers can demo tools, share data, or conduct mini-tutorials on specialized methods.
Methodological Demonstrations: Live demonstrations of new software, libraries, or experimental techniques relevant to LLMs and social science.
Structured Feedback & Discussion: Dedicated discussant feedback for each presented paper and open-floor discussions to provide in-depth, constructive critique and foster collaboration.

Paper Submission

Submissions should be made through the conference submission system (COMS):

Submit Paper

We welcome full research papers as well as extended abstracts describing ongoing work, preliminary results, or promising projects that would benefit from feedback.

Key Dates

Abstract submission deadline: 31 March 2026
Notification of acceptance: 1 May 2026

Further deadlines (e.g. camera-ready paper submission) will be communicated to accepted authors.

Registration

Early registration (until 31 May 2026): €550
Regular registration (1 July 2026 onward): €650
Registration deadline: 15 July 2026

Cancellation Policy

If you must cancel your conference registration, please notify us as soon as possible at melanie.sawers@nuffield.ox.ac.uk. Refunds will be processed under the following terms:

Cancellations before 15 July 2026: Full refund minus a €50 processing fee.
Cancellations after 15 July 2026, or failure to attend: No refund available.

Accommodation

Participants are responsible for arranging their own accommodation. Please refer to the conference website (see Programme section) for suggested hotels and practical information for visitors. Early booking is recommended as October is a busy period in Hong Kong.

Contact

For any queries or difficulties with the submission system, please contact Melanie Sawers (conference coordinator) at melanie.sawers@nuffield.ox.ac.uk. We are happy to assist with questions about the conference scope, submissions, or logistics.

Venue & Accessibility

Location: City University of Hong Kong – a modern, fully accessible campus located at Tat Chee Avenue, Kowloon Tong, Hong Kong.
Public Transit: The campus is directly accessible via the Kowloon Tong MTR station (connected to several major lines).
Amenities: Immediately adjacent to Festival Walk shopping mall (with numerous restaurants, cafés, and shops).
Airport Access: Approximately 30–40 minutes from Hong Kong International Airport, either by taxi or by taking the Airport Express train to Kowloon Station and a short taxi ride to campus.

Explore Hong Kong

Take the opportunity to explore Hong Kong’s vibrant culture and sights during your visit:

Victoria Peak: Enjoy panoramic views of the famous skyline and Victoria Harbour.
Kowloon Walled City Park: A historic site and tranquil garden (located near CityU).
Mong Kok Markets: Experience the bustling street markets, local shops, and diverse dining options in Mong Kok.
Tsim Sha Tsui Promenade: Stroll along the waterfront for stunning harbour views, especially beautiful at night.
October Weather: Hong Kong’s autumn weather is typically warm and comfortable (≈26–29°C / 79–84°F), ideal for sightseeing.

	Timing	Room		Presentation Schedule
Registration	8:30	Foyer just outside the Auditorium
Welcome and Introduction	09:00 – 09:10	Auditorium	Livia & Ray
Panel Session 1	09:10 – 11:00	Auditorium	Discussant – Frederik Hjorth, University of Copenhagen	Arianna Muti, Bocconi University (Milan) “Where are the leftist feminists now?” An LLM approach to how political events influence social media discourse on feminism Christy Coulson, London School of Economics and Political Science (LSE) “Gender-based Activism and Violent Backlash: Evidence from Latin America” Matilde Ceron, University of Salzburg “Leveraging Crowdcoding and LLMs for the analysis of gender mainstreaming: gender-sensitive recovery policies in the EU” Nils-Christian Bormann and Edoardo Alberto Viganò, Witten/Herdecke University. “Historical Conflict Event Data Collection via Large Language Models (LLMs)” Ashrakat Elshehawy, Stanford University “How Biased Police Reporting Shapes Misperceptions of Out-Group Crime”
Break	11:00 – 11:20	Foyer just outside the Auditorium
Panel Session 2	11:20 – 13:00	Auditorium	Discussant – Elizabeth Rhodes, OpenResearch	Gloria Gennaro, KCL “The evolution of political rhetoric on immigration in the UK” Sascha Riaz, European University Institute “Regime Loyalty during Wartime – Evidence from Nazi Germany” Tore Wig, University of Oslo “Prompting for Theoretical Progress: Using Generative AI to evaluate the evidence support for Grand Theories of Politics” Xiao Liu, Peking University “Automated Annotation of Evolving Corpora for Augmenting Longitudinal Network Data: A Framework Integrating Large Language Models and Expert Knowledge” Christopher Klamm, University of Cologne “Measuring Personal Attacks in Parliamentary Debates”
Workshop Lunch	13:00 – 13:30	Foyer just outside the Auditorium
Panel Session 3	13:30 – 15:10	Auditorium	Discussant – Xun PANG, Peking University	Javier Osorio, University of Arizona “ConfliBERT: A Language Model for Political Conflict” Jongwoo Jeong, Georgia State University “Spelling correction with large language models to reduce measurement error in open-ended survey responses” Winnie Xia, Aarhus University, Yen-Chieh Liao & Slava Jankin, University of Birmingham “Dynamic and Multilingual Embedding Regression for Political Text” David Muchlinski, Georgia Tech “SCOPE: Supercharging Conflict Prediction and Explanability using Advanced AI” Giovanni Pagano and Luigi Curini University of Milan “More than words? Understanding Multimodal Political Communication: a Computational Analysis of Textual and Visual Elements in the EP 2024 Campaign”
Break	15:10 – 15:20	Foyer just outside the Auditorium
Panel Session 4	15:20 – 17:00	Auditorium	Discussant – Moritz Marbach, UCL	Giuliano Formisano, University of Oxford “The Network Dynamics of Online Polarisation in the US” Camilo Cristancho, Universitat de Barcelona “Attitudes towards protest: A large-scale comparative perspective of media representations of protest in Latin America 2000-2024” Rachel Bernhard, Nuffield College, University of Oxford “Kiss, Marry, Kill: Appearance-Based Discrimination in Politics” Bryce J Dietrich, Purdue University, Using AI to Summarize US Presidential Campaign TV Advertisement Videos, 1952–2012 Taehee Kim, University of Konstanz “Evaluating Generative Agents in Social Media: Replicating User Attitudes and Behaviors”
Panel Session 5	17:10 – 18.40	Auditorium	Discussant – Isaac Mehlhaff, Texas A&M University	Bastián González-Bustamante, Leiden University “Charting Reproducibility and Performance: LLMs in Multilingual Toxic Speech Detection” Fabrizio Gilardi, University of Zurich “Understanding and Mitigating Online Toxicity: A Chatroom-Based Experimental Framework with LLM Agents” Linette Lim, University College Dublin, Yen-Chieh Liao & Slava Jankin, University of Birmingham “Who Believes and Who Shares Fake News: A Multi-Agent System Application for Experimental Misinformation Research with LLMs” Nicolai Berk, ETH Zurich “What is a Good Conversation? Improving the Operationalization and Measurement of Deliberative Quality by Analyzing Interactions” Moritz Osnabrügge, Durham University “Polarized Speech: How Elite Rhetoric and Echo Chambers Fuel Negative Emotive Political Debate”