United States

Metaphors are part of everyday language and shape the way in which we conceptualize the world. Moreover, they play a multifaceted role in communication, making their understanding and generation a challenging task for language models (LMs). While there has been extensive work in the literature linking metaphor to the fulfilment of individual intentions, no comprehensive taxonomy of such intentions, suitable for natural language processing (NLP) applications, is available to present day. In this paper, we propose a novel taxonomy of intentions commonly attributed to metaphor, which comprises 9 categories. We also release the first dataset annotated for intentions behind metaphor use. Finally, we use this dataset to test the capability of large language models (LLMs) in inferring the intentions behind metaphor use, in zero- and in-context few-shot settings. Our experiments show that this is still a challenge for LLMs.

ACL 2026

A framework for annotating and modelling intentions behind metaphor use

intention

figurative language

large language models

metaphor

### 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)

workshop 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.

The majority of contemporary computational methods for lexical semantic change (LSC) detection are based on neural embedding distributional representations. Although these models perform well on LSC benchmarks, their results are often difficult to interpret. We explore an alternative approach that relies solely on frame semantics. We show that this method is effective for detecting semantic change and can even outperform many distributional semantic models. Finally, we present a detailed quantitative and qualitative analysis of its predictions, demonstrating that they are both plausible and highly interpretable.

ReFRAME or Remain: Unsupervised Lexical Semantic Change Detection with Frame Semantics

This paper studies the impact of retrieved ideologically framed texts on the outputs of large language models (LLMs). While interest in understanding ideological framing in LLMs has recently increased, little attention has been given to this issue in the context of Retrieval-Augmented Generation (RAG). To fill this gap, we design an external knowledge source based on ideologically framed texts about COVID-19 treatments. Our corpus is based on 1,117 academic articles representing discourses about controversial and endorsed treatments for the disease. We propose a corpus linguistics framework, based on Lexical Multidimensional Analysis (LMDA), to identify discourse dimensions within the corpus. LLMs are tasked to answer questions derived from three identified discourse dimensions, and two types of contextual prompts are adopted: the first comprises the user question and ideologically framed texts; and the second contains the question, ideologically framed texts, and LMDA descriptions. Alignment between reference ideologically framed texts and LLMs' responses is assessed using cosine similarity for lexical and semantic representations. Results demonstrate that retrieved ideologically framed texts influence LLM responses toward the discourse framing represented in the external knowledge, with enhanced prompts further amplifying this effect. Our findings highlight the importance of identifying ideological framings within the RAG framework in order to mitigate not just unintended ideological bias, but also the risks of intentional discourse steering of such models.

Uncovering Ideological Bias in RAG with Lexical Multidimensional Analysis: A Case Study on COVID-19

Compositionality is considered central to language abilities. As performant language systems, how do large language models (LLMs) do on compositional tasks? We evaluate adjective–noun compositionality in LLMs using two complementary setups: prompt-based functional assessment and a representational analysis of internal model states. Our results reveal a striking divergence between task performance and internal states. While LLMs reliably develop compositional representations, they fail to translate consistently into functional task success across model variants. Consequently, we highlight the importance of contrastive evaluation for obtaining a more complete understanding of model capabilities.

Evaluating Adjective-Noun Compositionality in LLMs: Functional vs Representational Perspectives

We evaluate GPT-4o's color naming across nine languages using both synthetic and human-derived stimuli. Using hue wheels, fixed basic categories, low-chroma hue lines, and dense binned CIELAB grids, we separate lexical availability of color terms from distributional agreement with human color naming. GPT-4o reliably names vivid, high-chroma colors and reproduces several known language-specific distinctions under constrained settings. However, its performance degrades sharply for low-chroma colors and for stimuli near human category boundaries. In these regions, model-human divergence remains high. 
Overall, GPT-4o shows strong cross-linguistic lexical knowledge but does not reliably match human color-naming distributions, especially in low-chroma and boundary regions.

Lexical Availability and Human Distributional Agreement in GPT-4o's Color Naming

Zero-shot Named Entity Recognition is critical for low-resource domains, yet existing approaches rely on opaque prompting of large language models or dense representations that suffer from polysemanticity. We propose an alternative approach that leverages monosemantic features of Sparse Autoencoders. We introduce $\textbf{SAE-NER}$, a training-free framework that maps monosemantic SAE feature activations to entity types through direct precision estimation, requiring no supervision or prompting. Experiments across general and biomedical domains show that SAE-NER consistently outperforms trained probing classifiers, with especially a large margin in the biomedical domain (up to +20 F1). Finally, we evaluate the utility of SAE-NER predictions as silver training data for downstream NER models. Using controlled perturbations of gold annotations to simulate realistic annotation noise, we show that false negatives are the primary bottleneck for silver-data quality, outweighing the impact of boundary imprecision and false positives.

From Latents to Labels: Zero-Shot Named Entity Recognition using Sparse Autoencoder Features

Large language models (LLMs) appear successful in emulating compositional language, yet it remains unclear what these results entail about their underlying compositional semantic representations. The probing classifier paradigm has emerged as a tool to remedy this. This paper proposes to critically review the findings of 24 probing studies targeting a wide range of linguistic and semantic phenomena. It proposes a taxonomy of probing tasks based on the linguistic primitives they presuppose, distinguishing four tiers: lexical semantics, the syntax–semantics interface, propositional semantics, and discourse and pragmatics. A gradient in representational evidence emerges: LLMs robustly encode lexical information, display less consistent sensitivity to structural relations within sentences, and obtain unsatisfactory results on tasks requiring propositional content, speech acts, or pragmatic inference. The review underscores the need for a clearer theoretical grounding of what probing tasks measure and reflects on how probing can illuminate the compositional pathways available within current language models.

Compositional Meaning Representations in LLMs: a Critical Review of Probing Studies

We present a systematic study on paraphrase detection in Tamil by constructing a unified dataset through translation and semantic verification of three English benchmarks QQP, PAWS, and MRPC. Unlike prior efforts that focus on individual sources or limited scales, our dataset combines multiple paraphrase detection paradigms and is evaluated using semantic similarity metrics, round-trip translation checks, and classifier agreement analysis. We fine-tune five multilingual transformer models (mBERT, XLM-R, IndicBERT, MuRIL, and DistilmBERT) and a Tamil-specific compact model, TLMR (Tamil Language Model - DeBERTa), pretrained on 525M Tamil tokens. Furthermore, we assess the representational quality of the sentence embeddings that are taken from these models using lightweight classifiers (SVM, XGBoost, and Logistic Regression). We formulate an efficiency-oriented metric that incorporates top-5 accuracy, vocabulary usage, and script fidelity in relation to perplexity in order to facilitate resource-aware evaluation. The experimental findings lay the groundwork for future Tamil semantic understanding tasks by highlighting differences in generalization and efficiency across models.

Under the Surface: Probing Tamil Paraphrase Intelligence

Social biases based on regional identity (or regional bias) are often observed in Indian contexts on major online social networks and require critical attention. However, due to large linguistic and cultural diversity, high annotation costs, and inherent human biases, very little annotated data exists on regional biases in the Indian context. Recently, Large Language Models (LLMs) have garnered attention for the automatic annotation of text. However, such annotation efforts are largely limited to English texts, and LLMs often perform poorly when applied to low-resource languages. Therefore, this paper focuses on understanding the capabilities and challenges of popular open-source LLMs in annotating Indian regional biases. We utilize the recently proposed IndRegBias dataset, which consists of Indian regionally biased social media comments in both English and code-mixed formats.
First, we assess the annotation capabilities of LLMs in a zero-shot setting and critically analyze their performance across different writing styles, including code-mixing, transliteration, and English. We find that the majority of LLMs exhibit low agreement with human annotations (measured using Cohen’s kappa). Consequently, we extend our study by fine-tuning the models using 50% of the data and evaluating them on the remaining 50%. We observe a significant improvement in annotation agreement (kappa) for all the LLMs. To further assess the capabilities of the fine-tuned models, we evaluate them on 500 newly collected social media comments discussing regional issues in India. The results show that most fine-tuned LLMs outperform their zero-shot counterparts when annotating these new comments.

Annotating Indian Regional Biases using Large Language Models: Evaluation and Analysis

Updating bilingual dictionary entries is a tedious, time-consuming, and highly subjective task, especially when a new sense in the source language requires identifying an appropriate translation equivalent. To date, there have been no attempts to automatize the discovery of new bilingual sense entries. Related tasks such as Word-level Bilingual Dictionary Induction and cross-lingual embedding alignment do not account for polysemy and are not applied to lexicographic data.
In contrast to their monolingual counterparts, bilingual dictionaries fall short in terms of completeness, detail with respect to examples and glosses, and diachronic information.
We introduce a novel NLP task, Sense-Level Bilingual Dictionary Induction (\textsc{SenseBDI}), at the intersection of lexical semantics, cross-lingual, and diachronic NLP.
We construct a dataset of time-stamped sense-level bilingual dictionary entries by aligning two bilingual dictionaries, two monolingual dictionaries, and the multilingual resource BabelNet, thereby enriching bilingual entries with monolingual source-language information.
We propose a baseline based on nearest-neighbor search over cross-lingual embeddings of glosses and usages. We find that usages contribute more strongly than glosses, with substantial variation across language pairs and discuss task-specific challenges with regards to target language polysemy and future directions such as transfer to real-world scenarios.

Towards Sense-level Bilingual Dictionary Induction

Suicide ideation detection models are typically evaluated using aggregate performance metrics, yet little is known about how they internally represent psychologically meaningful risk factors. In high-stakes mental health applications, understanding these internal representations is essential for safety, transparency, and responsible deployment. In this work, we move beyond accuracy and analyze how suicide detection models trained on original and topic-augmented datasets encode psychological risk factors in their internal representation space. Using visualization and geometric analysis, we examine the coherence and separability of topic-related features. Our results show that topic-aware augmentation increases the clarity and distinctness of underrepresented psychosocial risk factors such as immigration, family issues, and financial crisis. These findings suggest that augmentation not only improves model performance but also leads to more structured and interpretable internal representations.

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