Singapore

Large Language Models (LLMs) have shown remarkable success in supporting a wide range of knowledge-intensive tasks. In specialized domains, there is growing interest in leveraging LLMs to assist subject matter experts with domain-specific challenges. However, deploying LLMs as SaaS solutions raises data privacy concerns, while many open-source models demand significant computational resources for effective domain adaptation and deployment. A promising alternative is to develop smaller, domain-specialized LLMs, though this approach is often constrained by the lack of high-quality domain-specific training data. In this work, we address these limitations by presenting a cost-efficient and scalable training pipeline that combines guided synthetic data generation from a small seed corpus with bottom-up domain data curation. Our pipeline integrates Domain-Adaptive Pre-training (DAPT), Domain-specific Supervised Fine-Tuning (DSFT), and Direct Preference Optimization (DPO) to train effective small-scale models for specialized use cases. We demonstrate this approach through DiagnosticSLM, a 3B-parameter language model tailored for fault diagnosis, root cause analysis, and repair recommendation in industrial settings. To evaluate model performance, we introduce four domain-specific benchmarks: multiple-choice questions (DiagnosticMCQ), question answering (DiagnosticQA), sentence completion (DiagnosticComp), and summarization (DiagnosticSum). DiagnosticSLM achieves up to 25% accuracy improvement over open-source models of comparable or larger size (2B-9B) on the MCQ task, while also outperforming or matching them in other tasks, demonstrating strong domain-specific reasoning and generalization capabilities.

AAAI 2026

Building Domain-Specific Small Language Models via Guided Data Generation

guided data generation

domain-specific models

small language models

parameter-efficient training

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

poster

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Auditing large language models (LLMs) for biases is an ongoing and dynamic process, resembling a proverbial cat-and-mouse game. As researchers identify new vulnerabilities in LLMs, guardrails are updated to address them, prompting the need for innovative approaches to audit the increasingly fortified LLMs for biases. 
This paper makes three contributions. First, it introduces a scalable, explainable framework to measure biases against various identity groups across multiple open large language models. Second, it conducts a bias audit considering five well-known open LLMs and demonstrates their bias inclinations towards several historically disadvantaged groups. Our audit reveals disturbing antisemitic, Islamophobic, and xenophobic biases present in several well-known LLMs. Finally, we release a dataset of 1,000 probes curated under the supervision of an expert social scientist that can facilitate similar audits.

How Can You Tell if Your Large Language Model Could Be a Closet Antisemite? An Explainability-Based Audit Framework for Implicit Bias

Theory of mind refers to the attribution of mental states
that humans ascribe to other humans or objects (such as
computer-based systems). Recently, the attribution of
mental states has been investigated toward Artificial
Intelligence (AI) as a basic manner to capture people's
engagement toward it, and people's perception about AI
social skills and AI capabilities.
In line with this idea, mental state attribution can be
used as an indirect measure of students' understanding of
AI functioning, and in particular of the kind of
interactions students may have with AI systems. Too often
is the case of people using generative AI systems in ways
that exceed their actual ways of functioning. In our study,
children of age in the range 9-12 were involved in one-shot
unplugged activities concerning data and models.
The unplugged activities were not aimed at teaching the
theory of Machine Learning but rather they were designed so
as to provide awareness on some basic mechanisms and help
developing a correct use of tools that are becoming more
and more present in everyday life. This paper introduces
the activities and reports
the results that were achieved.

Unplugged Activities on Machine Learning and Their Evaluation Through Mental States Attribution

As artificial intelligence (AI) becomes increasingly
integrated into daily life, higher education must move
beyond code-centric instruction to foster holistic AI
literacy. We present a novel pedagogical approach that
integrates embodied, unplugged activities into a
university-level Introduction to AI course. Inspired by the
effectiveness of CS Unplugged in K-12 education, our
physical, collaborative activities gave students a
first-person perspective on AI decision-making. Through
interactive games modeling Search Algorithms, Markov
Decision Processes, Q-learning, and Hidden Markov Models,
students built an intuition for complex AI concepts and
more easily transitioned to mathematical formalizations and
code implementations. We present four unplugged AI
activities, describe how to bridge from unplugged
activities to plugged coding tasks, reflect on
implementation challenges, and propose refinements. We
suggest that unplugged activities can effectively bridge
conceptual reasoning and technical skill-building in
university-level AI education.

AI Unplugged: Embodied Interactions for AI Literacy in Higher Education

Embedding deep neural networks (NNs) into mixed-integer programs (MIPs) is attractive for decision making with learned constraints, yet state-of-the-art “monolithic’’ linearisations blow up in size and quickly become intractable. In this paper, we introduce a novel dual-decomposition framework that relaxes the single coupling equality $u=x$ with an augmented Lagrange multiplier and splits the problem into a vanilla MIP and a constrained NN block. Each part is tackled by the solver that suits it best—branch \& cut for the MIP subproblem, first-order optimisation for the NN subproblem—so the model remains modular, the number of integer variables never grows with network depth, and the per-iteration cost scales only linearly with the NN size. On the public \textsc{SurrogateLIB} benchmark, our method proves scalable, modular, and adaptable: it runs 120$\times$ faster than an exact Big–M formulation on the largest test case; the NN sub-solver can be swapped from a log-barrier interior step to a projected-gradient routine with no code changes and identical objective value; and swapping the MLP for an LSTM backbone still completes the full optimisation in 47s without any bespoke adaptation.

Scalable Mixed-Integer Optimization with Neural Constraints via Dual Decomposition

Referring Multi-Object Tracking (RMOT) aims to achieve precise object detection and tracking through natural language instructions, representing a fundamental capability for intelligent robotic systems. However, current RMOT research remains mostly confined to ground-level scenarios, which constrains their ability to capture broad-scale scene contexts and perform comprehensive tracking and path planning. In contrast, Unmanned Aerial Vehicles (UAVs) leverage their expansive aerial perspectives and superior maneuverability to enable wide-area surveillance. Moreover, UAVs have emerged as critical platforms for Embodied Intelligence, which has given rise to an unprecedented demand for intelligent aerial systems capable of natural language interaction. 
To this end, we introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios, which aims to bridge this research gap. To facilitate its construction, we develop an innovative semi-automated collaborative agent-based labeling assistant (COALA) framework that significantly reduces labor costs while maintaining annotation quality. Furthermore, we propose HawkEyeTrack (HETrack), a novel method that collaboratively enhances vision-language representation learning and improves the perception of UAV scenarios. Comprehensive experiments validated the challenging nature of our dataset and the effectiveness of our method. More details about the dataset, development kits, and code can be found in the supplementary materials.

AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios

Estimating Body Mass Index (BMI) from camera images with machine learning models enables rapid weight assessment when traditional methods are unavailable or impractical, such as in telehealth or emergency scenarios. Existing computer vision approaches have been limited to datasets of up to 14,500 images. In this study, we present a deep learning-based BMI estimation method trained on our WayBED dataset, a large proprietary collection of 84,963 smartphone images from 25,353 individuals. We introduce an automatic filtering method that uses posture clustering and person detection to curate the dataset by removing low-quality images, such as those with atypical postures or incomplete views. This process retained 71,322 high-quality images suitable for training. We achieve a Mean Absolute Percentage Error (MAPE) of 7.9% on our hold-out test set (WayBED data) using full-body images, the lowest value in the published literature to the best of our knowledge. Further, we achieve a MAPE of 13% on the completely unseen (during training) VisualBodyToBMI dataset, comparable with state-of-the-art approaches trained on it, demonstrating robust generalization. Lastly, we fine-tune our model on VisualBodyToBMI and achieve a MAPE of 8.56%, the lowest reported value on this dataset so far. We deploy the full pipeline, including image filtering and BMI estimation, on Android devices using the CLAID framework. We release our complete code for model training, filtering, and the CLAID package for mobile deployment as open-source contributions.

Digital Scale: Open-Source On-Device BMI Estimation from Smartphone Camera Images Trained on a Large-Scale Real-World Dataset

Urinary tract infection (UTI) flare-ups pose a significant health risk for older adults with chronic conditions. These infections often go unnoticed until they become severe, making early detection through innovative smart home technologies crucial. Traditional machine learning (ML) approaches relying on simple binary classification for UTI detection offer limited utility to nurses and practitioners as they lack insight into prediction uncertainty, hindering informed clinical decision-making. This paper presents a clinician-in-the-loop (CIL) smart home system that leverages ambient sensor data to extract meaningful behavioral markers, train robust predictive ML models, and calibrate them to enable uncertainty-aware decision support. The system incorporates a statistically valid uncertainty quantification method called Conformal-Calibrated Interval (CCI), which quantifies uncertainty and abstains from making predictions ("I don’t know") when the ML model's confidence is low. Evaluated on real-world data from eight smart homes, our method outperforms baseline methods in recall and other classification metrics while maintaining the lowest abstention proportion and interval width. A survey of 42 nurses confirms that our system's outputs are valuable for guiding clinical decision-making, underscoring their practical utility in improving informed decisions and effectively managing UTIs and other condition flare-ups in older adults.

Clinician-in-the-Loop Smart Home System to Detect Urinary Tract Infection Flare-Ups via Uncertainty-Aware Decision Support

The proliferation of Model Context Protocol (MCP) servers in enterprise infrastructure management has revolutionized AI-driven automation while introducing critical multi-layered security vulnerabilities that traditional cybersecurity frameworks cannot adequately address. This paper presents a comprehensive intelligent guardrail system that addresses the unique security challenges of MCP-driven infrastructure management through a novel four-layer defense architecture. Our solution employs a dedicated guardian LLM that interprets natural language policies and applies contextual reasoning to complex infrastructure scenarios, providing dynamic policy enforcement that adapts to user roles, operational timing, and system context. Unlike existing rule-based security systems, our approach implements guardrails at four distinct control points: input message filtering, tool selection validation, execution-time verification, and post-action auditing. The system addresses critical gaps in existing security solutions by providing infrastructure-specific threat modeling, real-time policy adaptation, and comprehensive audit trails with explainable decision-making through confidence scores and detailed reasoning. Our evaluation demonstrates the system's effectiveness in preventing command injection, privilege escalation, and tool poisoning attacks across various enterprise infrastructure scenarios while maintaining operational agility essential for modern data center management.

InfrastructureSentinel: Policy Enforced Guardrails for Secure MCP-driven Infrastructure Agents

ArguBot Arena: Prompt Engineering a Debate on Responsible AI

We stand at the dawn of the AI era, a technological revolution poised to be the most consequential of our generation, presenting both unprecedented opportunities and profound challenges. But this promise is shadowed by significant challenges. To build a future we want, we must move beyond the hype and the headlines to confront the most pressing open problems—technical, sociotechnical, and multidisciplinary. This talk will review the rapid progress, dissect challenges ahead, and argue that our greatest task isn’t simply building smarter machines, but fostering the human wisdom to guide them towards a future that is not only intelligent but also equitable, safe, and profoundly human.

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How Can You Tell if Your Large Language Model Could Be a Closet Antisemite? An Explainability-Based Audit Framework for Implicit Bias

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Task Transfer and Domain Adaptation for Zero-Shot Question Answering

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Next from AAAI 2026

How Can You Tell if Your Large Language Model Could Be a Closet Antisemite? An Explainability-Based Audit Framework for Implicit Bias

Similar lecture

Task Transfer and Domain Adaptation for Zero-Shot Question Answering

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads