Singapore

Large Language Models (LLMs) have demonstrated exceptional capabilities but face two critical deployment challenges: high computational costs and scarcity of personalized domain training data. We address these dual challenges through a comprehensive framework that combines synthetic data generation with inference optimization techniques. Our approach employs LLMs for zero-shot and few-shot synthetic dataset creation while applying structural pruning, knowledge distillation, quantization, and prompt caching for computational efficiency. We evaluate three architectural paradigms: encoder-only, encoder-decoder, and decoder-only on synthetic building permit classification and assess optimization techniques on public benchmarks. Our systematic evaluation reveals that strategic architectural selection based on task characteristics is more critical than model complexity: encoder-only models provide superior efficiency-accuracy trade-offs for high-throughput scenarios, demonstrating that understanding problem requirements enables effective deployment without requiring the largest available models. Knowledge distillation emerges as the key optimization technique for personalized domains, recovering pruning-induced performance degradation with Llama3-8B+KD achieving 87.1% accuracy at 20% pruning while exceeding unpruned baselines. Complementary optimization strategies, including dynamic caching (5-18% latency reduction with zero performance loss) and hardware acceleration (up to 200× speedup) enable flexible deployment configurations for domain specific applications.

AAAI 2026

Domain-Specific LLM Adaptation: Bridging Personalization and Efficiency Through Synthetic Data and Optimization

model personalization

inference optimization

model pruning

compression

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

workshop paper

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.

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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.

As AI becomes more personal, e.g., Agentic AI, there is an increasing need for personalizing models for various use cases. Personalized federated learning (PFL) enables each client to collaboratively leverage other clients' knowledge for better adaptation to the task of interest, without privacy risks. Despite its potential, existing PFL methods remain confined to rather simplified scenarios where data and models are the same across clients. To move towards realistic scenarios, we propose FedMosaic, a method that jointly addresses data and model heterogeneity with a task-relevance-aware model aggregation strategy to reduce parameter interference, and a dimension-invariant module that enables knowledge sharing across heterogeneous architectures without huge computational cost. To mimic the real-world task diversity, we propose a multi-modal PFL benchmark spanning 40 distinct tasks with distribution shifts over time. The empirical study shows that FedMosaic outperforms the state-of-the-art PFL methods, excelling in both personalization and generalization capabilities under challenging, realistic scenarios.

Not All Clients Are Equal: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients

Personalizing Large Language Model (LLM) agents requires conditioning them on user-specific data, creating a critical trade-off between task utility and data disclosure. While the utility of adding user data often exhibits diminishing returns (i.e., submodularity), enabling near-optimal greedy selection, real-world personalization is complicated by structural constraints. These include logical dependencies (e.g., selecting fact A requires fact B), categorical quotas (e.g., select at most one writing style), and hierarchical rules (e.g., select at most two social media preferences, of which at most one can be for a professional network). These constraints violate the assumptions of standard subset selection algorithms. We propose a principled method to formally model such constraints. We introduce a compilation process that transforms a user‚Äôs knowledge graph with dependencies into a set of abstract macro-facets. Our central result is a proof that common hierarchical and quota-based constraints over these macro-facets form a valid laminar matroid. This theoretical characterization lets us cast structured personalization as submodular maximization under a matroid constraint, enabling greedy with constant-factor guarantees (and (1‚àí1/e) via continuous greedy) for a much richer and more realistic class of problems.

Structured Personalization: Modeling Constraints as Matroids for Data-Minimal LLM Agents

Large Language Models (LLMs) are typically aligned for safety during the post-training phase; however, they may still generate harmful outputs that pose risks to users. 
This challenge underscores the need for robust safeguards that operate across both model inputs and outputs. In this work, we introduce RoGuard 1.0, a state-of-the-art instruction fine-tuned LLM designed to enhance the safety of LLM systems through comprehensive input‚Äìoutput moderation, using a pipeline of LLMs to enhance moderation capability. 
Built on the Llama-3.1-8B-Instruct backbone, our model is instruction fine-tuned to generalize across previously unseen safety taxonomies and demonstrates strong performance on out-of-domain safety benchmarks. 
The instruction fine-tuning process uses a mix of synthetic and open-source safety datasets, augmented with chain-of-thought (CoT) rationales and input inversion to enhance contextual understanding and decision making. 
To support systematic evaluation, we also release RoGuard Evaluation, a new benchmark featuring an extensible safety taxonomy to assess the effectiveness of LLM guardrails and moderation frameworks.

Taxonomy-Adaptive Moderation Model with Robust Guardrails for Large Language Models

Large foundation models (LFMs) transform healthcare AI in prevention, diagnostics, and treatment. However, whether LFMs can provide truly personalized treatment recommendations remains an open question. Recent research has revealed multiple challenges for personalization, including the fundamental generalizability paradox: models achieving high accuracy in one clinical study perform at chance level in others, demonstrating that personalization and external validity exist in tension. This exemplifies broader contradictions in AI-driven healthcare: the privacy-performance paradox, scale-specificity paradox, and the automation-empathy paradox. As another challenge, the degree of causal understanding required for personalized recommendations, as opposed to mere predictive capacities of LFMs, remains an open question. N-of-1 trials -- crossover self-experiments and the gold standard for individual causal inference in personalized medicine -- resolve these tensions by providing within-person causal evidence while preserving privacy through local experimentation. Despite their impressive capabilities, this paper argues that LFMs cannot replace N-of-1 trials. We argue that LFMs and N-of-1 trials are complementary: LFMs excel at rapid hypothesis generation from population patterns using multimodal data, while N-of-1 trials excel at causal validation for a given individual. We propose a hybrid framework that combines the strengths of both to enable personalization and navigate the identified paradoxes: LFMs generate ranked intervention candidates with uncertainty estimates, which trigger subsequent N-of-1 trials. Clarifying the boundary between prediction and causation and explicitly addressing the paradoxical tensions are essential for responsible AI integration in personalized medicine.

Personalization of Large Foundation Models for Health Interventions

Real-world deployment of large vision‚Äìlanguage models (LVLMs) makes everyday photos vulnerable to unintended geolocation inference, motivating techniques that enable safe, user-controllable personalization and privacy-aware deployment. We present Adversarial Tiled Location Anonymity Shield (ATLAS), a fully user-side, zero-query, black-box image-layer adaptation module that can be inserted before any LVLM to control the visibility of geospatial and contextual cues under strict perceptual constraints. ATLAS learns such modulations through a Mixture-of-Tiles (MoT) formulation: a semantic segmenter decomposes an image into regions, and a compact tile bank is routed by learned semantic gates and assembled per region before blending with the input to yield a perceptually faithful image that misdirects LVLM geolocation inference. ATLAS is trained on multiple frozen surrogate vision encoders (e.g., LLaMA-4 vision tower, CLIP, BLIP), each paired with lightweight GeoHeads for geolocation supervision; tower-wise losses are aggregated to guide the MoT generator while keeping all backbones frozen. We further explore two complementary training domains: a pixel-space variant perturbing texture and color statistics, and a frequency-space variant modulating low-frequency phase structure to probe distribution-level fragility. On OpenStreetView-5M, ATLAS reduces country-level accuracy by over 40\% and increases mean localization error by over 3,000 km under matched perceptual fidelity. In practice, ATLAS serves as a deployable personalization-safety layer, complementing dialogue-level policies with portable, image-level control, offering a practical path toward privacy-preserving and user-controllable personalization in real-world LVLM deployments.

ATLAS: User-Side Personalization and Privacy Protection Against Geolocation Risks in Large Vision–Language Models

Recent advancements in large language models (LLMs) have significantly enhanced many aspects of research and daily lives. While LLMs are inherently powerful, their performance can be further improved through post-training tailored to specific user needs. For individual users who are unable to directly finetune base models due to resource constraints, classifier guidance offers a more cost-effective solution for controlled text generation. However, traditional ways of applying classifier guidance (CFG) dose not transfer well to Diffusion Large Language Models (DLLMs), which would suffer from instability, criterion mismatch and slow inference. In this work, we propose an efficient approach to utilize and adapt classifier guidance on DLLMs, substantially reducing training and inference time while delivering notable improvements in task performance.

Controlled Text Generation of DLLMs with Efficient Classifier Guidance

Foundation models for knowledge graphs (KGs) achieve strong cohort-level performance in link prediction, yet fail to capture individual user preferences; a key disconnect between general relational reasoning and personalized ranking. We propose GatedBias, a lightweight inference-time personalization framework that adapts frozen KG embeddings to individual user contexts without retraining or compromising global accuracy. Our approach introduces structure-gated adaptation: profile-specific features combine with graph-derived binary gates to produce interpretable, per-entity biases, requiring only ${\sim}300$ trainable parameters. We evaluate GatedBias on two benchmark datasets (Amazon-Book and Last-FM), demonstrating statistically significant improvements in alignment metrics while preserving cohort performance. Counterfactual perturbation experiments validate causal responsiveness; entities benefiting from specific preference signals show 6--30$\times$ greater rank improvements when those signals are boosted. These results show that personalized adaptation of foundation models can be both parameter-efficient and causally verifiable, bridging general knowledge representations with individual user needs.

Lightweight Inference-Time Personalization for Frozen Knowledge Graph Embeddings

Online grocery shopping presents unique challenges for sequential recommendations due to repetitive purchase patterns and complex item relationships within the baskets. Unlike traditional e-commerce, grocery recommendations must capture both complementary item associations and temporal dependencies across shopping sessions. To address these challenges in Amazon's online grocery business, we propose T-REX, a novel transformer architecture that generates personalized category-level suggestions by learning both short-term basket dependencies and long-term user preferences. Our approach introduces three key innovations: (1) an efficient sampling strategy utilizing dynamic sequence splitting for sparse shopping patterns, (2) an adaptive positional encoding scheme for temporal patterns, and (3) a category-level modeling approach that reduces dimensionality while maintaining recommendation quality. Although masked language modeling techniques like BERT4Rec excel at capturing item relations, they prove less suitable for next basket generation due to information leakage issues. In contrast, T-REX's causal masking approach better aligns with the sequential nature of basket generation, enabling more accurate next-basket predictions. Experiments on large-scale grocery offline data and online A/B tests show significant improvement over existing systems.

T-REX: Transformer-based Category Sequence Generation for Grocery Basket Recommendation

Language identification is a crucial first step in multilingual systems such as chatbots and virtual assistants, enabling linguistically and culturally accurate user experiences. Errors at this stage can cascade into downstream failures, setting a high bar for accuracy. Yet, existing language identification tools struggle with key cases--such as music requests where the song title and user language differ. Open-source tools like LangDetect, FastText are fast but less accurate, while large language models, though effective, are often too costly for low-latency or low-resource settings. We introduce PolyLingua, a lightweight Transformer-based model for in-domain language detection and fine-grained language classification. It employs a two-level contrastive learning framework combining instance-level separation and class-level alignment with adaptive margins, yielding compact and well-separated embeddings even for closely related languages. Evaluated on two challenging datasets‚ÄîAmazon Massive (multilingual digital assistant utterances) and a Song dataset (music requests with frequent code-switching)‚ÄîPolyLingua achieves $99.25\%$ F1 and $98.15\%$ F1, respectively, surpassing Sonnet 3.5 while using 10√ó fewer parameters, making it ideal for compute- and latency-constrained environments.

PolyLingua: Margin-based Inter-class Transformer for Robust Cross-domain Language Detection

We revisit continual pre-training for large language models and argue that progress now depends more on scaling the right structure than on scaling parameters alone. We introduce SCALE, a width-upscaling architecture that inserts lightweight expansion into linear modules while freezing all pre-trained parameters. This preserves the residual and attention topologies and increases capacity without perturbing the base model‚Äôs original functionality. SCALE is guided by two principles: Persistent Preservation, which maintains the base model‚Äôs behavior via preservation-oriented initialization and freezing of the pre-trained weights, and Collaborative Adaptation, which selectively trains a subset of expansion components to acquire new knowledge with minimal interference. We instantiate these ideas as SCALE-Preserve (preservation-first), SCALE-Adapt (adaptation-first), and SCALE-Route, an optional routing extension that performs token-level routing between preservation and adaptation heads. On a controlled synthetic biography benchmark, SCALE mitigates the severe forgetting observed with depth expansion while still acquiring new knowledge. In continual pre-training on a Korean corpus, SCALE variants achieve less forgetting on English evaluations and competitive gains on Korean benchmarks, with these variants offering the best overall stability‚Äìplasticity trade-off. Accompanying analysis clarifies when preservation provably holds and why the interplay between preservation and adaptation stabilizes optimization compared to standard continual learning setups.

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Not All Clients Are Equal: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients

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