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Post-training activation compression is essential for deploying Large Language Models (LLMs) on resource-constrained hardware. However, standard methods like Singular Value Decomposition (SVD) are gradient-blind: they preserve high-variance dimensions regardless of their impact on factual knowledge preservation. We introduce Fisher-Aligned Subspace Compression (FASC), a knowledge-aware compression framework that selects subspaces by directly modeling activation-gradient coupling, minimizing a second-order surrogate of the loss function. FASC leverages the Fisher Information Matrix to identify dimensions critical for factual knowledge, which often reside in low-variance but high-gradient-sensitivity subspaces. We propose the Dependence Violation Score (ρ) as a general-purpose diagnostic metric that quantifies activation-gradient coupling, revealing where factual knowledge is stored within transformer architectures. Extensive experiments on Mistral-7B and Llama-3-8B demonstrate that FASC preserves 6–8% more accuracy on knowledge-intensive benchmarks (MMLU, LAMA) compared to variance-based methods at 50% rank reduction, effectively enabling a 7B model to match the factual recall of a 13B uncompressed model. Our analysis reveals that ρ serves as a fundamental signal of stored knowledge, with high-ρ layers emerging only when models internalize factual associations during training

ACL 2026

Beyond Variance: Knowledge-Aware LLM Compression via Fisher-Aligned Subspace Diagnostics

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

technical paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Detecting hate speech in online communities is increasingly challenging due to the implicit and context-dependent nature of toxic expressions. While text-only models often struggle with such ambiguity, incorporating user interaction signals offers critical pragmatic context for disambiguation. However, research in this direction is hindered by the scarcity of datasets that align textual content with comprehensive user behavioral graphs. To bridge this gap, we present a new dataset collected from a real-world community, featuring labeled hate speech enriched with fine-grained interaction histories. We further propose a novel user-aware hate speech detection framework that effectively fuses textual semantics with social interaction representations. Experiments demonstrate that our approach consistently outperforms strong text-only baselines by over 3.6\%, validating the critical role of social context in enhancing detection accuracy. Furthermore, to mitigate real-world adversarial risks such as graph spoofing and spam, we introduce a contrastive graph augmentation strategy, ensuring model robustness against unreliable community behaviors.

Improving Hate Speech Detection by Fusing Textual and User Interaction Representations in Online Communities

The demand for efficient large language model inference has spurred interest in sparsification, yet current hardware support remains narrowly focused on 2:4 weight sparsity. In this work, we argue that activation sparsity despite being overlooked in hardware design offers a promising path for dynamic, input-adaptive compression with significant I/O and memory benefits. We present a comprehensive post-training study of $N{:}M$ activation pruning across four LLMs (Llama2-7B-chat, Llama3.1-8B-Instruct, Qwen2.5-7B-Instruct, Gemma3-4B-Instruct), demonstrating that activation pruning consistently outperforms weight pruning at matched sparsity levels. We evaluate lightweight, plug-and-play error mitigation and selection strategies that require minimal or no calibration data across four sparsity patterns: 2:4, 4:8, 8:16, and 16:32. Among these, 16:32 approaches the performance of unstructured 50\% sparsity and is is approximately 2.7$\times$ better than 2:4, while 8:16 offers an optimal balance of accuracy and practicality. Our results provide evidence that next-generation accelerators should consider native support for $N{:}M$ activation sparsity and can serve as a strong baseline for the future methods.

Motivating Next-Gen Accelerators with Flexible Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches

Large Language Models (LLMs) based on Mixture-of-Experts (MoE) are pivotal in industrial applications for their ability to scale performance efficiently. However, standard MoEs enforce uniform expert sizes, creating a rigidity that fails to align computational costs with varying token-level complexity. While heterogeneous expert architectures attempt to address this by diversifying expert sizes, they often suffer from significant system-level challenges, specifically unbalanced GPU utilization and inefficient parameter utilization, which hinder practical deployment.
To bridge the gap between theoretical heterogeneity and robust industrial application, we propose Mixture of Heterogeneous Grouped Experts (MoHGE) which introduces a two-level routing mechanism to enable flexible, resource-aware expert combinations. To optimize inference efficiency, we propose a Group-Wise Auxiliary Loss, which dynamically steers tokens to the most parameter-efficient expert groups based on task difficulty.
To address the critical deployment challenge of GPU load balancing, we introduce an All-size Group-decoupling Allocation strategy coupled with an Intra-Group Experts Auxiliary Loss. These mechanisms collectively ensure uniform computation distribution across GPUs.
Extensive evaluations demonstrate that MoHGE matches the performance of MoE architectures while reducing the total parameters by approximately 20\% and maintaining balanced GPU utilization. Our work establishes a scalable paradigm for resource-efficient MoE design, offering a practical solution for optimizing inference costs in real-world scenarios.

Mixture of Heterogeneous Grouped Experts for Language Modeling

Large language models (LLMs) are increasingly adopted in downstream industries, yet aligning proprietary agents remains challenging due to limited high-quality data and hardware constraints in small and medium-sized enterprises (SMEs).
We propose EfficientTool, a cost-effective, tool-conditioned alignment framework forming a closed loop over data collection, iterative training, and deployment-oriented evaluation.
EfficientTool adopts a self-evolving bootstrapping-based Trajectory Collection Pipeline for high-quality trajectory generation, followed by iterative Model Training Pipeline using tool-conditioned parameter-efficient fine-tuning (PEFT).
We evaluate the model with Interaction and Evaluation Pipeline in public and private benchmarks, and deploy for an internal enterprise agent.
Results show that EfficientTool effectively aligns model in SME scenarios while preserving general tool-calling capability.

EfficientTool: A Cost-Effective Aligning Framework for Tool-Conditioned Agents in SME Scenarios

As large language models (LLMs) grow in size, efficient compression techniques like quantization and sparsification are critical. While quantization maintains performance with reduced precision, structured sparsity methods, such as N:M sparsification, often fall short due to limited flexibility and sensitivity to outlier weights. We explore 8:16 semi-structured sparsity, demonstrating its ability to surpass the Performance Threshold---where a compressed model matches the accuracy of its uncompressed or smaller counterpart under equivalent memory constraints. Compared to 2:4 sparsity, 8:16 offers greater flexibility with minimal storage overhead (0.875 vs. 0.75 bits/element). We also apply sparse structured patterns for salient weights, showing that structured sparsity for outliers is competitive with unstructured approaches, leading to equivalent or better results. Finally, we demonstrate that simple techniques such as variance correction and SmoothQuant-like weight equalization improve sparse models performance.

From 2:4 to 8:16 sparcity patterns in LLMs for Outliers and Weights with Variance Correction

Production LLMs must balance modeling quality with predictable latency, stable accelerator utilization, and cost-efficient scaling—constraints that remain difficult for existing architectures. Transformers provide strong reasoning but incur quadratic complexity, while state-space models (SSMs) scale efficiently yet lack fine-grained interactions; prior hybrids either introduce sequential bottlenecks or rely on learned routing that complicates deployment. We present FlowHN, a deployment-oriented parallel hybrid architecture that enables deterministic conditional computation via FLOP-aware token circulation across attention and SSM branches. Instead of dynamic expert routing, FlowHN performs hardware-aligned token scheduling that balances workloads, reduces synchronization stalls, and preserves full parameter utilization. Across 135M–1B models, FlowHN achieves up to 4× higher throughput and 15% higher MFU than strong Transformer, SSM, and hybrid baselines while maintaining competitive accuracy on reasoning, coding, and long-context tasks up to 32K tokens. FlowHN is designed to integrate directly into existing Hybrid pipelines without changes to optimizers, training stacks, or inference serving infrastructure, making it practical for real-world deployment.

FlowHN: Adaptive Token Routing for Efficient Parallel Hybrid Networks

NL2SQL systems deployed in industry settings often encounter ambiguous or unanswerable queries, especially in interactive scenarios with incomplete user clarifications. Existing benchmarks typically assume a single point of ambiguity and typically rely on users to resolve them via interactions, overlooking realistic failure modes. We introduce CLARITY, a framework that automatically generates ambiguous and unanswerable NL2SQL benchmark with multi-faceted ambiguity and diverse user behaviour in both single- and multi-turn settings. Using a constraint-driven pipeline, CLARITY transforms executable SQL into ambiguous queries augmented with grounded conversational continuations and schema-level metadata. Empirical evaluation on Spider and BIRD indicates that leading NL2SQL systems (based on strong LLMs) exhibit pronounced performance degradation under multi-faceted ambiguity. They frequently flag ambiguous inputs but fail to accurately localize or disambiguate the underlying schema-level sources, underscoring the need for further industry-focused research on robust ambiguity detection and resolution in NL2SQL bolstered by this work.

CLARITY: A Framework and Benchmark for Conversational Language Ambiguity and Unanswerability in Interactive NL2SQL Systems

Related search query recommendation is essential for enhancing user engagement and information discovery on digital platforms. While Large Language Models (LLMs) have shifted the field toward generative retrieval, existing methods suffer from two primary limitations: (1) pointwise generation via beam search often leads to semantic redundancy and wasted retrieval quota, and (2) current listwise approaches lack explicit reasoning, relying on superficial click-through rate (CTR) rewards. In this paper, we propose ReList, a novel framework that transforms related search into a reasoning-enhanced listwise generation task. ReList follows a two-stage training paradigm: first, Reasoning Activation constructs a high-quality dataset by back-translating diverse query lists into Chain-of-Thought (CoT) rationales; second, Alternative Training iteratively evolves the model using Reinforcement Learning with a Gated Multi-Objective Reward and a Corrective SFT mechanism to handle hard samples. Experimental results on real-world search benchmarks and online A/B tests demonstrate that ReList significantly outperforms state-of-the-art methods in both query diversity and user engagement, providing more insightful and logically grounded query recommendations.

ReList: A Multi-objective Reasoning Framework for Diversified Listwise Query Recommendation

Large Language Models (LLMs) have demonstrated considerable efficacy in 
classification tasks, yet their performance depends on two critical prompt components: Task Instructions (HOW to classify) and Class Descriptions (WHAT defines each class). While prompt engineering research has extensively explored instruction optimization, class descriptions have received comparatively less attention, often being treated as fixed inputs or simple label names. This represents a critical gap for real-world classification tasks, particularly in contact center domains, where labels often suffer from ambiguous boundaries, overlapping definitions, and incomplete coverage of possible cases—substantially limiting accuracy regardless of instruction 
quality.


We propose a multi-agent framework for iteratively refining class descriptions based on classification errors. By analyzing misclassified 
instances, language agents automatically generate improved descriptions that better capture class distinctions and resolve ambiguities. Empirical evaluation across contact center and public benchmark datasets demonstrates upto 20.71% accuracy improvements over static class descriptions, addressing an orthogonal dimension to existing instruction optimization techniques.

Beyond Instruction Optimization: Multi-Agent Error-Driven Class Description Refinement for LLM-Based Classification

Large-scale integrity enforcement on short-form video platforms typically relies on multiple specialized vertical modules, each dedicated to a specific risk category. However, exhaustively executing these computationally intensive modules over massive content streams leads to substantial inference overhead, despite the fact that most content is benign and violations are usually confined to limited policy domains. To address this inefficiency, we propose RADAR, a lightweight risk-aware routing framework that selectively releases low-risk content while dispatching high-risk instances to appropriate vertical modules. Industrial deployment of such routing systems presents two major challenges: (1) systematic label sparsity caused by disjoint annotation pipelines across risk categories, and (2) the capacity-efficiency tradeoff inherent to compact routing architectures. To overcome these challenges, RADAR incorporates Validity-Aware Masking to handle fragmented supervision and Expert-Guided Knowledge Distillation to transfer knowledge from heavyweight expert models into the lightweight router. Experiments on large-scale real-world datasets demonstrate that the proposed masking strategy effectively mitigates disjoint annotation issues, while distillation substantially enhances routing accuracy, enabling the lightweight router to achieve competitive or superior performance compared to specialized expert models.

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Improving Hate Speech Detection by Fusing Textual and User Interaction Representations in Online Communities

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