United States

Conventional fine-grained entity typing (FET) operates under the closed-set assumption, wherein all classified types are limited within a predefined type taxonomy derived from a knowledge base. As the world evolves, new entities of unknown types inevitably emerge in open environments, falling beyond the scope of the existing type taxonomy. To deal with this problem, in this paper, we investigate a novel and critical task: open-set entity typing (OSET), which aims to not only classify entity mentions within the known type taxonomy but also detect those outside it, termed as unknown-type instances. However, owing to the lack of exposure to unknown-type instances during training, existing FET models are susceptible to misclassify them as known types, limiting their practical effectiveness for this new OSET task. Moreover, manually collecting and annotating large-scale unknown-type instances is both time-consuming and labor-intensive in open environments. To mitigate this issue, we propose a two-stage generation model that automatically produces large-scale, high-quality and diverse pseudo unknown-type instances, beneficial for the tailor-designed unified open-set classifier to effectively distinguish between known and unknown types. Furthermore, an innovative unknown-aware hierarchical contrastive learning strategy is designed to facilitate a clear delineation between closely related known types and unknown types. Extensive experiments on two newly established benchmark datasets demonstrate that our proposed framework significantly surpasses all baselines in addressing the OSET task.

ACL 2026

Unveiling the Unknown: Open-Set Entity Typing via Two-Stage Generation

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### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and 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. 
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Large language models (LLMs) exhibit strong general intelligence, yet their multilingual performance remains highly imbalanced. Although LLMs encode substantial cross-lingual knowledge in a unified semantic space, they often struggle to reliably interface this knowledge with low-resource or unseen languages. Fortunately, pretrained encoder-decoder translation models already possess balanced multilingual capacity, suggesting a natural complement to LLMs. In this work, we propose XBridge, a compositional encoder-LLM-decoder architecture that offloads multilingual understanding and generation to external pretrained translation models, while preserving the LLM as an English-centric core for general knowledge processing and reasoning. To address the resulting representation misalignment across models, we introduce lightweight cross-model mapping layers together with an optimal transport-based alignment objective, enabling fine-grained semantic consistency for multilingual generation. Experiments on four LLMs across multilingual understanding, reasoning, and generation indicate that XBridge outperforms strong baselines, especially on low-resource and previously unseen languages, without retraining the LLM.

Language on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilinguality

Large Language Models (LLMs) often generate overconfident yet factually incorrect hallucinations. Current detection paradigms suffer from a trade-off between the high accuracy of computationally expensive black-box methods and the inability of white-box methods to detect stubborn hallucinations. To bridge this gap, we propose GHOST (Geometric Hidden-state Observation for Semantic Truthfulness), an efficient white-box framework for hallucination detection in LLMs. We primarily target confused hallucinations marked by internal reasoning instability, while also capturing stubborn hallucinations characterized by premature layer-wise convergence as a complementary signal. By integrating internal geometric dynamics with output probability distributions, GHOST constructs a high-dimensional feature space for non-linear truthfulness classification. Extensive evaluations on FinanceBench, RAGTruth, HaluEval, and PopQA show that GHOST outperforms white-box baselines and achieves competitive black-box performance while reducing computational overhead by over 90%, offering a robust solution for real-time detection.

The Digital Dunning-Kruger Effect: Decoupling Hallucinations via Geometric Hidden-state Observation for Semantic Truthfulness

Large Language Models (LLMs) excel at general language tasks but struggle in specialized domains. Specialized Generalist Models (SGMs) address this by preserving broad capabilities while adapting to target domains. However, existing architectures provide limited support for task-guided specialized memory mechanisms. In this work, we introduce Nirvana, an SGM featuring specialized memory, linear-time complexity, and test-time task information extraction. Central to Nirvana are: (1) Task-Aware Memory Trigger ($\textit{Trigger}$), which treats each input as a self-supervised fine-tuning task and adjusts task-related parameters on the fly; and (2) Specialized Memory Updater ($\textit{Updater}$), which dynamically consolidates task-relevant context. Nirvana matches or surpasses LLM baselines on general benchmarks and achieves the lowest perplexity across specialized domains including biomedicine, finance, and law. On the challenging task of Magnetic Resonance Imaging (MRI), we attach lightweight codecs to the frozen Nirvana backbone and fine-tune them on paired k-space signals and images. Nirvana achieves higher-fidelity reconstructions than conventional LLM-based models, with Trigger providing effective domain-specific adaptation. Ablation studies confirm that removing Trigger leads to substantial degradation across all tasks, underscoring its essential role in task-aware specialization. Models are available at https://huggingface.co/collections/YuhuaJiang/nirvana. Code is available at https://github.com/YuhuaJiang2002/Nirvana.

Nirvana: A Specialized Generalist Model With Task-Aware Memory Mechanism

The emergence of Large Vision-Language Models (LVLMs) has substantially expanded model capabilities beyond text-only understanding, enabling unified inference across both visual and textual modalities and supporting a broader range of real-world applications. To comprehensively evaluate the perception, understanding, reasoning, and cognition capabilities of LVLMs throughout the entire financial business workflow in Chinese contexts, we introduce CFMME, a novel Chinese financial multimodal evaluation benchmark. CFMME comprises 6,052 instances spanning from fundamental academic knowledge to complex real-world applications, covering eight primary financial image modalities and four core multimodal tasks. On CFMME, we conduct a thorough evaluation of representative LVLMs. The results show that the state-of-the-art model attains an overall accuracy of 66.11\% on the question answering task and an average score of 77.18 on the detection, recognition, and information extraction tasks, indicating substantial room for improvement in current LVLMs. In addition, we conduct detailed analyses of error causes, cross-modal capabilities, and multi-orientation settings, yielding valuable insights for future research. We hope that CFMME will spur further progress in LVLMs, especially by improving their performance on multiple multimodal tasks in the financial domain.

Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset

Adapting pretrained Large Language Models (LLMs) for time series forecasting primarily relies on token-level linguistic-temporal alignment, leading to the stacking of logically disjointed tokens as input. While empirically effective, these methods overlook a fundamental capability of LLMs: modeling linguistic logic and structure, rather than merely processing token features. To address this limitation, we propose the $\textbf{M}$arkovian-$\textbf{G}$uided $\textbf{S}$tructure-$\textbf{A}$ware $\textbf{A}$lignment ($\textbf{MGSAA}$). Our core contribution is a framework that transcends pointwise feature matching to achieve global structural isomorphism between the linguistic and temporal domains. Specifically, MGSAA distills latent evolutionary patterns of language within LLMs into a Markovian state transition graph, which is transferred as a structural prior to the time series domain. Under this prior, time series patches are decoded into latent states and then aligned via state-constrained cross-attention. Ultimately, MGSAA generates a token sequence topologically isomorphic to the LLM's inherent mental structure, reactivating its reasoning capabilities for forecasting. Comprehensive evaluations across multiple benchmarks demonstrate that MGSAA achieves state-of-the-art performance, providing an innovative solution for cross-modal alignment in LLM for time series forecasting. Code is available at https://github.com/sunzju/MGSAA.

Markovian Linguistic-Temporal Bridge: Unlocking the Potential of LLMs for Time Series Forecasting

VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control. However, current single-frame architectures suffer from intrinsic limitations: temporal myopia that discards historical dynamics, reasoning gaps between high-level instructions and low-level motor commands, and inference inefficiency due to autoregressive scalar decoding. In this work, we propose MIRTH, a unified framework designed to address these challenges. MIRTH augments a pretrained VLA backbone with three key innovations: (1) dual-scale temporal memory hubs that compress long-term scene evolution and short-term motion trends into compact embeddings; (2) latent reasoning tokens optimized via a mutual-information objective carving out a semantic plan space to align multimodal context with action trajectories; and (3) a parallel action decoding scheme that replaces autoregressive generation with vector-wise prediction to maximize control throughput. Extensive evaluations on the LIBERO simulation benchmark and a real-world LeRobot platform demonstrate that MIRTH achieves state-of-the-art performance and exhibiting emergent error recovery capabilities. We will release our code and collected datasets to facilitate reproducible research in embodied AI upon publication.

MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents

With the rapid proliferation of large language models (LLMs), model pools have become increasingly heterogeneous in both capability and efficiency. Larger LLMs can improve quality but incur higher latency and cost, while smaller LLMs are the opposite, making per-query model selection crucial in practice. This has spawned LLM routers that dispatch each query to an appropriate model. Existing routers lack fine-grained resource awareness across deployment settings, which degrades efficiency metrics in real-world serving. To this end, We propose FLARE, a length-centric, resource-aware multi-LLM routing framework that uses length-based models to estimate per-query latency and cost. FLARE formulates routing as a discrete multi-objective optimization problem to achieve efficient trade-off. Experiments show that FLARE reduces latency and cost by up to 68\% and 75\% while maintaining competitive accuracy, and can be easily applied to new datasets and LLMs.

FLARE: Fine-Grained Length-Aware Routing for Resource-Efficient Heterogeneous LLM Serving

Classroom discourse analysis is critical for tracing cognitive restructuring, yet existing research predominantly focuses on Dialogue Acts (DA), overlooking the deeper dimension of Opinion Evolution (OE). In this paper, we formally define the task of Classroom Opinion Evolution Recognition and introduce the Classroom Opinion Evolution Dataset (COED). Addressing the "Accuracy-Cost-Data" trilemma in real-world educational scenarios and the "overconfidence" failure mode of traditional confidence-based cascading systems on long-tail samples, we propose the Multi-task Enhanced Cascade Hybrid (MECH) framework. Grounded in the CODA (Continuous Opinions and Discrete Actions) theory, MECH conceptually translates the "Action-Opinion" dualism into a risk-aware routing mechanism. Instead of relying solely on prediction confidence, this mechanism utilizes high-risk argumentative DA signals derived from multi-task learning to construct a "semantic safety net" effectively routing implicit or ambiguous samples to a Large Language Model for reasoning. Experimental results demonstrate that MECH achieves a state-of-the-art accuracy of 78.55% while reducing API costs by 44.4%. Furthermore, the framework exhibits robustness in few-shot scenarios (using only 20% of data), offering a cost-effective and interpretable solution for large-scale educational dialogue analysis. Our code and data are available at https://github.com/ywh24284-code/MECH.

MECH: A Cost-Effective Multi-Task Cascade Framework for Classroom Opinion Evolution Recognition

Language is a vehicle for thought, intricately tied to sounds, symbols, and meaning. However, most large language model (LLM) research focuses on meaning (semantics) and symbols (spelling) while largely overlooking sounds. Existing benchmarks on LLMs' phonological abilities are either solvable through rote memorization or intertwined with other abilities, making them inadequate to measure LLMs' genuine ability in *phonological understanding*. Here, we present $\texttt{Phun-Bench}$, a purpose-built Chinese benchmark with diverse tasks and settings across three dimensions (Homophony, Rhyme, and Phonetic Similarity), designed to systematically evaluate LLMs' phonological understanding. Our results show that while LLMs excel at recalling correct pronunciations, they generally struggle to leverage phonological knowledge in the flexible and intuitive way that human speakers do. Moreover, through detailed analyses, we propose a hypothesis regarding the underlying mechanism of LLMs' phonological understanding and ``perception'', highlighting an underexplored frontier for future research.

\texttt{Phun-Bench}: Evaluating LLMs on Phonological Understanding in Chinese

Large reasoning models enhanced by reinforcement learning with verifiable rewards have achieved significant performance gains by extending their chain-of-thought. However, this paradigm incurs substantial deployment costs as models often exhibit excessive verbosity on simple queries. Existing efficient reasoning methods relying on explicit length penalties often introduce optimization conflicts and leave the generative mechanisms driving overthinking largely unexamined. In this paper, we identify a phenomenon termed length shift where models increasingly generate unnecessary reasoning on trivial inputs during training. To address this, we introduce Dynamic Outlier Truncation (DOT), a training-time intervention that selectively suppresses redundant tokens. This method targets only the extreme tail of response lengths within fully correct rollout groups while preserving long-horizon reasoning capabilities for complex problems. To complement this intervention and ensure stable convergence, we further incorporate auxiliary KL regularization and predictive dynamic sampling. Experimental results across multiple model scales demonstrate that our approach significantly pushes the efficiency-performance Pareto frontier outward. Notably, on the AIME-24, our method reduces inference token usage by 78% while simultaneously increasing accuracy compared to the initial policy and surpassing state-of-the-art efficient reasoning methods.

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Language on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilinguality

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