United States

Speculative decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose outputs that a stronger target model verifies. However, its token-centric nature allows erroneous steps to propagate. Prior approaches mitigate this using external reward models, but incur additional latency, computational overhead, and limit generalizability. We propose SpecGuard, a verification-aware speculative decoding framework that performs step-level verification using only model-internal signals. At each step, SpecGuard samples multiple draft candidates and selects the most consistent step, which is then validated using an ensemble of two lightweight model-internal signals: (i) an attention-based grounding score that measures attribution to the input and previously accepted steps, and (ii) a log-probability-based score that captures token-level confidence. These signals jointly determine whether a step is accepted or recomputed using the target, allocating compute selectively. Experiments across a range of reasoning benchmarks show that SpecGuard improves accuracy by 3.6% while reducing latency by ~11%, outperforming both SD and reward-guided SD.

ACL 2026

From Tokens to Steps: Verification-Aware Speculative Decoding for Efficient Multi-Step Reasoning

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

[![](https://assets.underline.io/markdown_image/1/image/b087f8a4dc5816d6e1a6514e59c59ac3.png)](https://drive.google.com/file/d/1T96GzPqObXrMln2BMByCSXTSizjTg69P/view?usp=sharing)

You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

To access NAACL 2025 event page you are required to register. Please follow [**this link**](https://2025.naacl.org/registration/registration/) to register. Access will depend on your registration type.

Please register!

NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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) 
<html><button style="display: inline-flex; align-items: center; justify-content: center; white-space: nowrap; border-radius: 9999px; font-weight: bold; background: #7c3aed; color: white; font-family: 'Space Grotesk', sans-serif; height: 40px; font-size: 16px; padding: 0 20px; border: none; cursor: pointer" onclick="window.open('https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing')">Conference Handbook</button></html>

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to ACL 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://2026.aclweb.org/registration/) first.

**Online Registration Form**: https://acl.swoogo.com/acl2026

Registration Required

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

The rise of Human-AI collaboration can effectively speed up the research process for experts and allow anyone with critical thinking skills to conduct innovative work. A key part of this collaboration is the AI's ability to improve a paper with human feedback—updating both the text and experiments to meet high standards. To evaluate this skill, we introduce ReviseBench, an extensible benchmark built on real academic data that can be easily scaled via agent-driven automated data collection. It tests the skills of Large Language Models (LLMs) on paper interpretation, experimental implementation, and paper formulation, using authors' camera-ready versions as natural human baselines. To facilitate a fine-grained assessment, we further propose ReviseArena, a platform supporting pair-wise comparisons between different AI-revised papers. Our initial evaluation results on ReviseBench reveal that even state-of-the-art foundation LLMs struggle significantly in this domain, achieving a win rate of less than 10\% against human experts, and facing issues like incremental revision, unprofessional revision, and potential data fabrication. Our code and data are released publicly at: https://github.com/CGCL-codes/ReviseBench.

Can AI Revise Research Papers with Human Review Feedback? An Empirical Study and Benchmark

Exploratory GUI testing is essential for software quality but suffers from high manual costs. While Multi-modal Large Language Model (MLLM) agents excel in navigation, they fail to autonomously discover defects due to two core challenges: Goal-Oriented Masking, where agents prioritize task completion over reporting anomalies, and Execution-Bias Attribution, where system defects are misidentified as agent errors. To address these, we first introduce GUITestBench, the first interactive benchmark for this task, featuring 143 tasks across 26 defects. We then propose GUITester, a multi-agent framework that decouples navigation from verification via two modules: (i) a Planning-Execution Module (PEM) that proactively probes for defects via embedded testing intents, and (ii) a Hierarchical Reflection Module (HRM) that resolves attribution ambiguity through interaction history analysis. GUITester achieves an F1-score of 48.90% (Pass@3) on GUITestBench, outperforming state-of-the-art baselines (33.35%). Our work demonstrates the feasibility of autonomous exploratory testing and provides a robust foundation for future GUI quality assurance.

GUITester: Enabling GUI Agents for Exploratory Defect Discovery

Failures are inevitable when embodied agents execute complex tasks. Visual-language models (VLMs) serve as the core component of embodied agents in perceiving the environment and making decisions. Assessing the capabilities of VLMs in detecting and reasoning about failures has become increasingly important. Previous work primarily considered low-level manipulation failures (e.g., 3cm grasp offsets), neglecting high-level failures arising during long-horizon task execution (e.g., object-dropping failure in the ``clean room'' task) by embodied agents. In this paper, we propose FAER, a failure-aware benchmark aiming to evaluate the performance of VLMs in terms of failure detection, failure categorization, failure description, and failure correction in long-horizon tasks. FAER comprises 3,323 episodes, spanning 3 scenes, 65 tasks, and 83 objects. We assess the performance of 16 widely utilized VLMs and 4 LLMs for FAER tasks. Experimental results show that nearly all VLMs, even GPT-4o, exhibit limited performance in failure detection with a high false negative rate, meaning that they tend to ignore abnormal events, revealing notable gaps in current models' capacity to effectively handle failures.

FAER: Benchmarking VLMs for Failure-Aware Embodied Reasoning

Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in French remains underdeveloped, primarily due to the lack of culturally relevant, human-annotated, large-scale datasets. In this work, we release TOXIFRENCH, a dataset of 53,622 French online comments, together with a 1,388-sample balanced benchmark split for systematic evaluation. The dataset is constructed via a semi-automated annotation pipeline that reduces manual labeling to only 10% through high-confidence LLM-based pre-annotation and human verification, while ensuring statistically near-perfect alignment with human-only annotation. We then benchmark a broad range of models and uncover a counterintuitive insight: Small Language Models (SLMs) often surpass larger models in robustness and generalization on this task. Motivated by this finding, we propose a novel Chain-of-Thought (CoT) fine-tuning strategy using a dynamic weighted loss that progressively emphasizes the model's final decision, significantly improving faithfulness. Our fine-tuned 4B model (Qwen3-4B) achieves state-of-the-art performance on the benchmark, improving its balanced accuracy by 10% over its baseline and achieving better performance than GPT-4o and DeepSeek-R1 on our benchmark, while successfully retaining cross-lingual capabilities.

TOXIFRENCH: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection

AI coding has emerged as a core application of large language models (LLMs), evolving from single-file coding tasks towards complex software engineering (SWE) scenarios. Recent advances in agents have enabled multi-file, multi-language, and dependency-aware AI coding, significantly expanding the scope of AI-assisted software development. While a variety of benchmarks have been proposed to evaluate coding capabilities in general-purpose or GPU coding ecosystems such as CUDA and ROCm, systematic evaluation for Huawei Ascend CANN remains largely underexplored. In this work, we propose LiveCANNBench, an SWE-level benchmark designed for AI coding in the CANN software stack. LiveCANNBench is constructed from real-world CANN repositories and consists of over 400 task instances spanning multi-file, multi-language, and execution-aware coding challenges. Unlike existing static benchmarks that primarily focus on kernel-level code generation, LiveCANNBench adopts a live benchmarking paradigm, effectively mitigating data leakage and enabling more reliable evaluation of modern coding agents.

LiveCANNBench: Benchmark SWE AI Coding for Ascend CANN

Distributed LLM inference avoids sending raw inputs by transmitting intermediate hidden states, a practice widely assumed to preserve privacy. We challenge this assumption and demonstrate that intermediate representations alone are sufficient to leak sensitive user attributes. This setting poses a fundamental obstacle for existing attribute inference attacks, which typically rely on auxiliary embedding-attribute pairs. To characterize this previously underexplored privacy risk, we reformulate attribute inference as zero-shot matching over candidate attributes directly in the intermediate representation space, and introduce a purely intermediate-representation-based attribute inference attack, termed IR-AIA. To address two structural challenges that hinder attribute inference from intermediate representations, we propose SG-APCR to address layer-dependent anisotropy in intermediate embeddings and a sliding-window similarity matching strategy to handle subword-level semantic fragmentation. Experiments across three LLMs and three real-world datasets show that sensitive attributes can be reliably inferred using only intermediate representations, achieving Top-1 accuracy of up to 0.997 on CMS, 0.980 on Skytrax, and 0.986 on ECHR. These results reveal that intermediate states commonly considered safe to share can expose sensitive personal attributes on their own.

Privacy Risks of Intermediate Representations: Attribute Inference in Distributed LLM Inference

Human Label Variation (HLV) refers to legitimate disagreement in annotation that reflects the diversity of human perspectives rather than mere error. Long treated in NLP as $\textit{noise}$ to be eliminated, HLV has only recently been reframed as a $\textit{signal}$ for improving model robustness. With the rise of large language models (LLMs) and post-training methods such as human feedback-based alignment, the role of HLV has become increasingly consequential. Yet current preference-learning datasets routinely collapse multiple annotations into a single label, flattening diverse perspectives into artificial consensus. Preserving HLV is necessary not only for pluralistic alignment but also for sociotechnical safety evaluation, where model behavior must be assessed in relation to human interaction and societal context.
This position paper argues that preserving HLV as an embodiment of human pluralism must be treated as a $\textit{Selbstzweck}$, an intrinsic value in itself. We analyze the limitations of existing preference datasets and propose actionable strategies for incorporating HLV into dataset construction to better preserve pluralistic human values.

Position: From Noise to Signal to Selbstzweck - Reframing Human Label Variation in the Era of Post-training in NLP

Understanding the intricate interplay among sequence, structure, and function remains a fundamental challenge in proteomics. The sequence-structure-function paradigm posits that biological roles are governed by the tertiary geometric conformations encoded within primary sequences; consequently, integrating these multi-modal descriptors is imperative for accurate functional annotation. While protein language models (pLMs) have achieved significant progress via representation learning on massive sequence data, they often lack the capacity to incorporate high-resolution structural information and the rich textual context that characterizes protein roles. In this work, we present STELLA, a multimodal LLM that synergistically aligns bimodal (sequence-structure) representations with the textual modality to advance protein functional annotation. By leveraging ESM3 for unified bimodal encoding and Llama-3.1-8B-Instruct for natural language modeling, STELLA achieves state-of-the-art performance in two critical tasks: Functional Description Prediction and Enzyme-catalyzed Reaction Prediction. This study demonstrates that multimodal LLMs represent a paradigm shift beyond pure pLMs, offering a new frontier for protein biology and biomedical discovery. The codes can be accessed via https://github.com/ocx-lab/STELLA.

STELLA: A Multimodal LLM for Protein Functional Annotation via Unified Sequence-Structure Encoding

We present NeoAraBERT, a state-of-the-art open-source Arabic text-embedding model built on the NeoBERT architecture. We pre-train NeoAraBERT on diverse open-source and internal datasets covering modern standard, classical, and dialectal Arabic. We guided our design choices with Arabic tailored ablation studies including text normalization, light stemming, and diacritics-aware tokenization handling. We also performed more general POS-aware token masking and learning-rate scheduling ablation studies. We benchmarked NeoAraBERT against five top-performing Arabic models on 23 tasks, including a novel synonym-based task, "Muradif", that directly assesses embedding quality with no additional fine-tuning. NeoAraBERT variants (MSA, dialectal, and mixed) rank first in 18 tasks, second in two, third in two, and fourth in one task. They show strong performance on classical and modern standard Arabic, substantial margins of improvement ($>$7\%) in two tasks, and a $+$2.75\% improvement on average across all tasks. Our code and links to checkpoints for our model variants are available on our website: \url{https://acr.ps/neoarabert}.

NeoAraBERT: A Modern Foundation Model for Arabic Embeddings with Diacritics-Aware Tokenization and POS-Targeted Masking

Large language models (LLMs) perform well on well-posed questions, yet standard question-answering (QA) benchmarks remain far from solved. We argue that this gap is partly due to underspecified questions, that are queries whose interpretation cannot be uniquely determined without additional context. We introduce an LLM-based classifier to identify underspecified questions and apply it to several widely used QA datasets, finding that 16% to over 60% of benchmark questions are underspecified and that LLMs perform significantly worse on them. To isolate the effect of underspecification, we conduct a controlled rewriting experiment that serves as an upper-bound analysis, rewriting underspecified questions into fully specified variants while holding gold answers fixed. QA performance consistently improves under this setting, indicating that many apparent QA failures stem from question underspecification rather than model limitations. Our findings highlight underspecification as an important confound in QA evaluation and motivate greater attention to question clarity in benchmark design.

Downloads

Next from ACL 2026

Can AI Revise Research Papers with Human Review Feedback? An Empirical Study and Benchmark

Similar lecture

Decoding Speculative Decoding