United States

Exposing latent lexical overlap, script romanization has emerged as an effective strategy for improving cross-lingual transfer (XLT) in multilingual language models (mLMs). Most prior work, however, focused on setups that favor romanization the most: **(1)** transfer from high-resource Latin-script to low-resource non-Latin-script languages and/or **(2)** between genealogically closely related languages with different scripts. It thus remains unclear whether romanization is a good representation choice for *pretraining* general-purpose mLMs, or, more precisely, if information loss associated with romanization harms performance for high-resource languages. 
We address this gap by pretraining encoder LMs from scratch on both romanized and original texts for six typologically diverse high-resource languages, investigating two potential sources of degradation: **(i)** loss of script-specific information and **(ii)** dilution of language-specific representations from increased subword overlap. Using two romanizers with different fidelity profiles, we observe negligible performance loss for languages with segmental scripts, whereas languages with morphosyllabic scripts (Chinese and Japanese) suffer degradation that higher-fidelity romanization mitigates but cannot fully recover. Importantly, comparing monolingual LMs with their mLM counterpart, we find no evidence that increased subword overlap dilutes language-specific representations. We further show that romanization improves encoding efficiency (i.e., fertility) for segmental scripts at a negligible performance cost.

ACL 2026

One Script Instead of Hundreds? On Pretraining Romanized Encoder Language Models

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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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.

In real-world applications of natural language processing, it is essential to effectively adapt a pre-trained model to a downstream task. While text classification is undertaken as a downstream task, it is crucial to produce meaningful sentence embedding that is adaptive to the task. In this paper, we explore how to effectively adapt a pre-trained model for extracting meaningful context representations from sentences, and propose an uncertainty-aware contrastive sentence embedding approach that involves addressing language ambiguity and inter-class separability for a text classification task. Specifically, we design an end-to-end strategy for driving the process of learning to transform a word embedding matrix into a contextualized sentence vector and to quantify the representation uncertainty of the sentence, while the word embedding matrix is produced by a pre-trained model without fine-tuning, and a label-wise contrastive learning strategy is designed to enhance intra-class compactness and inter-class separability. The results on public data sets show that a considerable improvement of text classification accuracy is achieved by adopting the proposed approach in comparison with using those state-of-the-art methods.

Uncertainty-Aware Contrastive Sentence Embedding With Local Context Representation for Text Classification

Constructing Knowledge Graphs (KGs) from unstructured text provides a structured framework for knowledge representation and reasoning, yet current LLM-based approaches struggle with a fundamental trade-off: factual coverage often leads to relational fragmentation, while premature consolidation causes information loss. To address this, we propose SocraticKG, an automated KG construction method that introduces question-answer pairs as a structured intermediate representation to systematically unfold document-level semantics prior to triple extraction. By employing 5W1H-guided QA expansion, SocraticKG captures contextual dependencies and implicit relational links typically lost in direct KG extraction pipelines, providing explicit grounding in the source document that helps mitigate implicit reasoning errors. Evaluation on the MINE benchmark demonstrates that our approach effectively addresses the coverage-connectivity trade-off, achieving superior factual retention while maintaining high structural cohesion even as extracted knowledge volume substantially expands. These results highlight that QA-mediated semantic scaffolding plays a critical role in structuring semantics prior to KG extraction, enabling more coherent and reliable graph construction in subsequent stages.

SocraticKG: Knowledge Graph Construction via QA-Driven Fact Extraction

PseudoSeer is a novel search engine for academic pseudocode, enabling retrieval over 320,000 algorithm implementations extracted from the arXiv. Using the system's caption-reference pairs, we study asymmetric retrieval, matching short queries with a median length of five words against long documents of roughly 300 words composed primarily of natural language with limited LaTeX notation. Our evaluation reveals scaling limitations in embedding models: a 149M parameter encoder outperforms 1.5B parameter alternatives, while BM25 remains competitive with pretrained models. Analyzing attention patterns over 33,000 caption document pairs, we identify two factors driving these results: attention efficiency and attention concentration. Models that significantly attend to sinks or non-discriminative tokens leave less attention for discriminative content, while models with overly diffuse attention fail to form discriminative representations. Guided by these findings, PseudoSeer’s embedding model, trained via contrastive learning with efficient attention patterns, outperforms the best pretrained model by 8.7 points. A hybrid approach combining learned embeddings with BM25 reaches 66.5\% R@10. PseudoSeer is deployed at pseudoseer.ist.psu.edu as both a practical search system and a benchmark for retrieval evaluation.

PseudoSeer: a Search Engine for Pseudocode

Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified. We introduce MAMA (Multi-Agent Memory Attack), a controlled evaluation framework for comparing topology-conditioned memory leakage in multi-agent LLM systems. MAMA operates on synthetic documents containing labeled Personally Identifiable Information (PII) entities, from which we generate sanitized task instructions. We execute a two-phase protocol: Engram (seeding private information into a target agent's memory) and Resonance (multi-round interaction where an attacker attempts extraction). Over 10 rounds, we measure leakage using a two-stage recovery criterion that combines exact-match extraction with LLM-based inference over the attacker's final output. We evaluate six canonical topologies (complete, circle, chain, tree, star, star-ring) across $n\in\{4,5,6\}$, attacker–target placements, and base models. Results are consistent: denser connectivity, shorter attacker–target distance, and higher target centrality increase leakage; most leakage occurs in early rounds and then plateaus; model choice shifts absolute rates but preserves broad structural trends; spatiotemporal/location attributes leak more readily than identity credentials or regulated identifiers. We distill practical guidance for system design: favor sparse or hierarchical connectivity, maximize attacker–target separation, and restrict hub/shortcut pathways via topology-aware access control. Our code is available at https://github.com/llll121/mama-eval.

Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs

Approximately 30% of autistic individuals remain non- or minimally-speaking throughout their lives, yet communicate richly through gestures, vocalizations, facial expressions, and augmentative devices. Interpreting this communication is an inherently multimodal task: caregivers rely on the simultaneous integration of visual cues, auditory signals, and contextual understanding to infer intent. Despite this natural alignment with multimodal large language models (MLLMs), research in this intersection remains narrowly focused on diagnosis rather than communication understanding. We address this gap by reframing the problem around two complementary dimensions: communicative actions (the physical modality) and communicative functions (the pragmatic intent). We analyze the ROSCO dataset, containing 2,903 caregiver-annotated video samples from 27 non- and minimally-speaking individuals, with multi-label annotations capturing up to three concurrent actions and two functions per sample across 6 action and 6 function classes. We further propose ROSCO-Omni, a teacher-student distillation framework that generates label-guided instruction data from a high-capability teacher MLLM and uses it to finetune a student MLLM for domain-specialized inference. ROSCO-Omni achieves performance comparable to closed-source models, demonstrating that open-source MLLMs can be adapted to understand communication in this underserved population.

ROSCO-Omni: Multimodal LLM-Based Communication Understanding for Non- and Minimally-Speaking Autistic Individuals

We introduce ChemBench, a comprehensive benchmark for evaluating LLMs' capabilities in analytical chemistry scenarios. Unlike existing benchmarks focused on factual knowledge, ChemBench assesses model abilities to provide contextualized, practical guidance for complex analytical chemistry challenges, including instrument readiness checks, system suitability testing, method development, and troubleshooting for both liquid chromatography coupled mass spectrometry (LC-MS) and Gas Chromatography-Mass Spectrometry (GC-MS) platforms. We evaluate three enhancement approaches: chemistry-specialized models, human-guided Chain-of-Thought reasoning, and Retrieval-Augmented Generation (RAG). Our findings reveal that general-purpose commercial models often outperform domain-specialized ones, while RAG and reasoning significantly improve performance. The six-dimension evaluation framework (specificity, correctness, usefulness, feasibility, misinformation risk, and error handling) provides valuable insights into LLMs' real-world utility for chemistry researchers, establishing a foundation for developing more effective AI assistants for scientific research.

LLMs as Lab Engineers: A Benchmark for Analytical Method Lifecycle Management

Weight tying, i.e. sharing parameters between input and output embedding matrices, is common practice in language model design, yet its impact on the learned embedding space remains poorly understood. In this paper, we show that tied embedding matrices align more closely with output (unembedding) matrices than with input embeddings of comparable untied models, indicating that the shared matrix is shaped primarily for output prediction rather than input representation. This unembedding bias arises because output gradients dominate early in training. Using tuned lens analysis, we show this negatively affects early-layer computations, which contribute less effectively to the residual stream. Scaling input gradients during training reduces this bias, providing causal evidence for the role of gradient imbalance. This is mechanistic evidence that weight tying optimizes the embedding matrix for output prediction, compromising its role in input representation. These results help explain why weight tying can harm performance at scale and have implications for training smaller LLMs, where the embedding matrix contributes substantially to total parameter count.

Weight Tying Biases Token Embeddings Towards the Output Space

Existing low-resource in-context learning-based knowledge graph question answering (KGQA) methods rely heavily on large language models (LLMs) to convert the natural language question into its corresponding logical form (LF), such as SPARQL, KoPL, etc. Recently, a few alignment techniques have been introduced that enable instruction-based fine-tuning of language models. They provide explicit negative signals and comparative objectives to learn how to avoid negative signals using preference optimization methods. Exploring such fine-tuning techniques with LLMs becomes very challenging due to the high computational resource requirements associated with them. Due to this, the focus has been shifted towards Small Language Models (SLMs), which offer advantages such as ease of (i) deployment for practical applications and (ii) instruction fine-tuning for specialized tasks. Motivated by this, in this work, we propose PO-KGQA: An SLM-based preference optimization framework for the complex KGQA task in a low-resource setting. Our extensive experiments demonstrate how PO-KGQA outperforms other fine-tuning alignment techniques on complex benchmarks such as KQA Pro by approximately 9% (avg).

PO-KGQA: Preference Optimization for Low-Resource Complex Knowledge Graph Question Answering

In simultaneous interpretation (SI), interpreters perform real-time translation by segmenting the source speech into chunks and translating them in the order they appear.
Since surface-matching metrics such as BLEU correlate poorly with human evaluations, translation quality is often evaluated using neural metrics that measure semantic similarity, such as COMET.
However, while SI translation ideally exhibits high monotonicity, COMET tends to assign higher scores to offline translations with long-distance reordering, because it is trained on such offline translation data.
To address this gap, we propose Simul-COMET, a variation of COMET adapted for SI evaluation specifically designed for monotonicity.
We train Simul-COMET on the SI-style translation data, which was converted from the offline translation of the COMET training data by leveraging large language models.
In English--Japanese translation experiments, we demonstrate that Simul-COMET assigns higher scores to SI-style translations than to offline ones.
Moreover, Simul-COMET shows stronger alignment with evaluation scores provided by professional interpreters than the original COMET.
Simul-COMET is available at https://github.com/kosuked/simul-comet.

Simul-COMET: A Quality Metric for Simultaneous Interpretation in Distant Language Pair Considering Word Order Difference

In fine-grained sparse Mixture-of-Experts (MoE) models, a large pool of specialized experts replaces a small homogeneous set, shifting performance and throughput to be governed by inference-time expert activation. Yet most existing optimization recipes implicitly assume a fixed activation budget (e.g., a constant Top-$k$ per layer), whose behavior in fine-grained MoEs is poorly understood. We first characterize runtime skipping strategies, quantifying the accuracy--efficiency trade-off of (i) uniform fixed activation and (ii) static layer-wise Top-$k$ allocation found by search. Our analysis reveals that static skipping can already provide substantial throughput gains, but optimal static schedules vary significantly across models and routing mechanisms. We therefore introduce Adaptive Skipping with Entropy-Penalized Thresholding (ASET), a training-free policy that adapts token-level activation using router confidence and entropy while remaining within the model's original budget. Across the fine-grained MoEs we study, static skipping policies yield 10--78\% throughput gains with minimal performance degradation, including $\ge$10\% improvement on DeepSeek-V3 without measurable loss. On the OLMoE testbed, ASET yields a Pareto frontier between average activation and task quality. Overall, these results identify expert skipping as a practical lever for faster fine-grained MoE inference, with adaptive activation helping when fixed budgets are too rigid.

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Uncertainty-Aware Contrastive Sentence Embedding With Local Context Representation for Text Classification

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