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Supervised fine-tuning (SFT) is a fundamental post-training strategy to align Large Language Models (LLMs) with human intent. However, traditional SFT often ignores the $\textit{one-to-many}$
nature of language by forcing alignment with a single reference answer, leading to the model overfitting to non-core expressions. Although our empirical analysis suggests that introducing multiple reference answers can mitigate this issue, the prohibitive data and computational costs necessitate a strategic shift: prioritizing the mitigation of single-reference overfitting over the costly pursuit of answer diversity. To achieve this, we reveal the intrinsic connection between token probability and semantic importance: high-probability tokens carry the core logical framework, while low-probability tokens are mostly replaceable expressions. Based on this insight, we propose $\textbf{ProFit}$
, which selectively masks low-probability tokens to prevent surface-level overfitting. Extensive experiments confirm that ProFit consistently outperforms traditional SFT baselines on general reasoning and mathematical benchmarks

ACL 2026

ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection

## 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.

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. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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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.

Dunhuang art, a cornerstone of global heritage, demands fine-grained visual perception anchored by specialized cultural knowledge. Given the strong performance of multimodal large language models (MLLMs) on generic multimodal benchmarks, to what extent can they understand artifacts from Dunhuang art that are grounded in cultural context? To this end, we construct Dunhuang-Bench, a large-scale benchmark comprising 486 images and 22,970 QA pairs. It incorporates diverse task formats to evaluate MLLMs' cultural understanding: Question Answering with Text Description, Multi-turn Dialogue, and Question Answering with Choices. Guided by Panofsky’s theory of iconology, we design two tasks including visual perception and knowledge reasoning for the evaluation of content understanding. In addition, we follow the theory of formal analytic tradition to design another task of artistic appreciation in our Dunhuang-Bench. Extensive evaluations of 20 mainstream MLLMs on Dunhuang-Bench reveal a consistent performance drop from perception and appreciation to reasoning. Moreover, CoT and few-shot prompting show marginal or negative impact, highlighting the limits of prompting-based improvements. Dunhuang-Bench thus provides a challenging benchmark for advancing multimodal cultural understanding. Data and code will be publicly available.

Dunhuang-Bench: How Well Do MLLMs Understand Cultural Heritage?

Consistency models (CMs) have shown promise in the efficient generation of both image and text. 
This raises the natural question of whether we can learn a unified CM for efficient multimodal generation (e.g., text-to-image) and understanding (e.g., image-to-text). 
Intuitively, such a model could be acquired by applying the consistency distillation (CD) to existing unified multimodal models. 
However, the key challenge is establishing a unified denoising perspective for both image and text generation, which is essential for establishing the consistency mapping. 
To tackle this, at the representation level, we advocate for discrete tokens for both modalities to best preserve language modeling capabilities. 
Critically, instead of defining the text denoising trajectory via recent discrete diffusion language modeling principles, we specify it using the parallel decoding trace of an autoregressive language model, benefiting from the latter's superior performance in general text generation tasks. 
The denoising trajectory of image tokens adheres to standard discrete diffusion. 
We train our unified consistency models (UniCMs) on these combined multimodal trajectories simultaneously with a unified objective. 
We introduce a trajectory segmentation strategy to improve the training convergence. 
Empirically, in text-to-image generation, UniCMs outperform SD3 on GenEval and Image Reward, while requiring only approximately ${1}/{8}$ of the sampling time. 
Meanwhile, in image-to-text generation, 
UniCMs surpass Show-o on the MMMU benchmark while being $1.5 \times$ faster at long-sequence generating speed.

UniCM: A Unified Consistency Model For Efficient Multimodal Generation and Understanding

Recent advancements in visual context compression enable MLLMs to process ultra-long contexts efficiently by rendering text into images. However, we identify a critical vulnerability inherent to this paradigm: lowering image resolution inadvertently catalyzes jailbreaking. Our experiments reveal that the safety defenses of SOTA models deteriorate sharply as resolution degrades, surprisingly persisting even when text remains legible. We attribute this to ``Cognitive Overload``, hypothesizing that the effort required to decipher degraded inputs diverts attentional resources from safety auditing. This phenomenon is consistent across various visual perturbations, including noise and geometric distortion. To address this, we propose a simple ``Structured Cognitive Offloading'' strategy that mitigates these risks by enforcing a serialized pipeline to decouple visual transcription from safety assessment. Our work exposes a significant risk in vision-based compression and provides critical insights for the secure design of future MLLMs.

Hard to Read, Easy to Jailbreak: How Visual Degradation Bypasses MLLM Safety Alignment

Optimizing communication topology is fundamental to the efficiency and effectiveness of Large Language Model (LLM)-based Multi-Agent Systems (MAS). While recent approaches utilize reinforcement learning to dynamically construct task-specific graphs, they typically rely on single-sample policy gradients with absolute rewards (e.g., binary correctness). This paradigm suffers from severe gradient variance and the credit assignment problem: simple queries yield non-informative positive rewards for suboptimal structures, while difficult queries often result in failures that provide no learning signal. To address these challenges, we propose Graph-GRPO, a novel topology optimization framework that integrates Group Relative Policy Optimization. Instead of evaluating a single topology in isolation, Graph-GRPO samples a group of diverse communication graphs for each query and computes the advantage of specific edges based on their relative performance within the group. By normalizing rewards across the sampled group, our method effectively mitigates the noise derived from task difficulty variance and enables fine-grained credit assignment. Extensive experiments on reasoning and code generation benchmarks demonstrate that Graph-GRPO significantly outperforms state-of-the-art baselines, achieving superior training stability and identifying critical communication pathways previously obscured by reward noise.

Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization

With the rapid progress of large language models (LLMs), aligning a general-purpose model with downstream tasks through fine-tuning has become a central research focus. Selecting only high-quality examples for training has been shown to be one of the most effective ways to improve fine-tuning performance. However, prior work concentrates almost exclusively on data preprocessing: filtering and cleaning data before training begins. While the order and composition of training data during training have received little fine-grained attention. To fill this gap, our work proposed Fine-Grained Order Fine-Tuning, a fine-grained scheduling method of data order in epochs. Drawing on curriculum-learning principles, FOT defines data difficulty based on the relevance between the data and the model, and then performs dynamic scheduling of the training order in each epoch according to the difficulty. On both large-scale continued pre-training and small-scale supervised fine-tuning experiments, FOT has achieved an average 2.4% improvement over baselines. Our study offers a new perspective on data governance in the fine-tuning phase.

Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models

Autoregressive (AR) language modeling remains the dominant paradigm due to its dense supervision signal and highly optimized serving infrastructure, but its strictly causal, token-by-token decoding limits parallelism and non-causal modeling. While masked diffusion offers a promising path toward parallel generation, it faces two critical bottlenecks: training inefficiency stemming from sparse masked objectives, and high latency caused by iterative whole-sequence denoising. We present a systematic study of blockwise discrete diffusion, a pragmatic middle ground that preserves AR-compatible serving while enabling parallel intra-block generation. Our study proceeds in four steps: (i) a \textbf{controlled, compute- and scale-matched comparison} revealing that AR is a more effective backbone for blockwise hybrids than masked diffusion objectives; (ii) a \textbf{scalable conversion recipe, \textsc{SDAR}}, validating that AR models spanning 1.7B to 30B parameters can be adapted into block diffusion models with minimal compute while preserving backbone capabilities; and (iii) a \textbf{systematic characterization of decoding dynamics}, which reveals a virtuous cycle where larger models enable more aggressive parallel decoding, achieving {theoretical speedups over 5$\times$} and {wall-clock speedups of 2.3$\times$} on H200 GPUs in latency-critical regimes; and (iv) an \textbf{investigation of local non-causal modeling capabilities}, showing that SDAR's local bidirectional attention overcomes causal bottlenecks in scientific domains (e.g., chemistry) and enables robust test-time scaling. We release the full model suite, the training framework, and our inference engines for further innovation in non-autoregressive generative paradigms.

SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation

Machine Unlearning (MU) enables Large Language Models (LLMs) to remove unsafe or outdated information. However, existing work assumes that all facts are equally forgettable and largely ignores whether the forgotten knowledge originates from pretraining or supervised fine-tuning (SFT). In this paper, we introduce DUAL (Dual Unlearning Evaluation across Training Stages), a benchmark of 28.6k Wikidata-derived triplets annotated with fact popularity using Wikipedia link counts and LLM-based salience scores. Our experiments show that pretrained and SFT models respond differently to unlearning. An SFT step on the forget data yields smoother forgetting, more stable tuning, and 10–50% higher retention, while direct unlearning on pretrained models remains unstable and prone to relearning or catastrophic forgetting.

Anatomy of Unlearning: The Dual Impact of Fact Salience and Model Fine-Tuning

While Large Language Models (LLMs) are widely used, they remain susceptible to jailbreak prompts that can elicit harmful or inappropriate responses. This paper introduces STAR-Teaming, a novel black-box framework for automated red teaming that 
effectively generates such prompts. STAR-Teaming integrates a Multi-Agent System (MAS) with a Strategy-Response Multiplex 
Network and employs network-driven optimization to sample effective attack strategies. This network-based approach 
recasts the intractable high-dimensional embedding space into a tractable structure, yielding two key advantages: it enhances 
the interpretability of the LLM's strategic vulnerabilities, and it streamlines the search for effective strategies by 
organizing the search space into semantic communities, thereby preventing redundant exploration. Empirical results demonstrate 
that STAR-Teaming significantly surpasses existing methods, achieving a higher attack success rate (ASR) at a lower 
computational cost. Extensive experiments validate the effectiveness and explainability of the Multiplex Network. The code is available at https://github.com/selectstar-ai/STAR-Teaming-paper.

STAR-Teaming: A Strategy-Response Multiplex Network Approach to Automated LLM Red Teaming

As Large Language Models (LLMs) are deployed globally, ensuring their safety and alignment across multiple languages becomes paramount. However, safety behaviors often vary unpredictably between languages, posing significant challenges for consistent and ethical AI. In this work, we systematically investigate the dynamics of multilingual alignment, exploring whether single-language alignment transfers cross-lingually, how language consistency is preserved during training, and the resulting trade-offs with general knowledge capabilities. We introduce RefusEU a novel refusal alignment dataset covering 12 European languages, including a dedicated test set for evaluating current state-of-the-art models. Our controlled Direct Preference Optimization (DPO) experiments provide two key insights: aligning models exclusively in English is insufficient to ensure cross-lingual safety, even for the same harm categories, whereas training on multilingual datasets can improve safety without degrading general performance, as measured by the Global MMLU benchmark.

Multilingual Refusal Alignment for Safer Large Language Models

Large language models (LLMs) have achieved good performance in multiple reasoning tasks. However, they are limited to adapt the rapid knowledge updates in the real-world scenario without retraining the entire LLM or modifying the model weights. Excluding these consuming methods, knowledge graphs (KGs) are used as external memory under knowledge updating because of their structural knowledge and efficient updating ability, which is yet limited by the gap between structural KG and LLM, and the deficient entity-independent semantics. To this end, we propose an LLM reasoning framework with hierarchical relational retrieval for large-scale knowledge updating, named G-HiRel. To integrate the structural edited KG into continuous LLMs, G-HiRel generates hierarchical instructions based on natural language questions. In order to handle the knowledge inconsistency between the KG and LLM and obtain the entity independence, G-HiRel utilizes a designed hierarchical relational retrieval for relational path candidates, which are selected by a designed semantics-based strategy. Finally, top entity-independent relational paths are instantiated and integrated into LLMs to generate the answer, in order to verify the reasoning performance under knowledge edits. Extensive experiments of G-HiRel on three benchmarks show that G-HiRel achieves superiority in terms of accuracy and interpretability. The code of G-HiRel is available at the link: https://github.com/HJJ-designed/G-HiRel.

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Dunhuang-Bench: How Well Do MLLMs Understand Cultural Heritage?

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