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Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of usages. Then, these labels are represented in a graph on which Word Sense Induction (WSI) is applied to derive sense clusters. Finally, LSCD labels are derived by comparing sense clusters over time. This modularity is reflected in most LSCD datasets and models. It also leads to a large heterogeneity in modeling options and task definitions, which is exacerbated by a variety of dataset versions, preprocessing options and evaluation metrics. This heterogeneity makes it difficult to evaluate models under comparable conditions, to choose optimal model combinations or to reproduce results. Hence, we provide a benchmark repository standardizing LSCD evaluation. Through transparent implementation results become easily reproducible and by standardization different components can be freely combined. The repository reflects the task&#39;s modularity by allowing model evaluation for WiC, WSI and LSCD. This allows for careful evaluation of increasingly complex model components providing new ways of model optimization. We use the implemented benchmark to conduct a number of experiments with recent models and systematically improve the state-of-the-art.

ACL 2026

The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks

lexical semantic change detection

word sense induction

word-in-context

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of usages. Then, these labels are represented in a graph on which Word Sense Induction (WSI) is applied to derive sense clusters. Finally, LSCD labels are derived by comparing sense clusters over time. This modularity is reflected in most LSCD datasets and models. It also leads to a large heterogeneity in modeling options and task definitions, which is exacerbated by a variety of dataset versions, preprocessing options and evaluation metrics. This heterogeneity makes it difficult to evaluate models under comparable conditions, to choose optimal model combinations or to reproduce results. Hence, we provide a benchmark repository standardizing LSCD evaluation. Through transparent implementation results become easily reproducible and by standardization different components can be freely combined. The repository reflects the task's modularity by allowing model evaluation for WiC, WSI and LSCD. This allows for careful evaluation of increasingly complex model components providing new ways of model optimization. We use the implemented benchmark to conduct a number of experiments with recent models and systematically improve the state-of-the-art.

workshop paper

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Making texts clear and comprehensible has become an increasingly important topic in NLP. A possible strategy to enhance text comprehension is to make implicitly conveyed meaning explicit. To explore the role of explicit vs. implied meaning, we study cases of so-called explicitations, i.e. revisions of text in which implicitly conveyed content is made explicit. Using revision histories from wikiHow, we propose a rule-based approach to extract candidate explicitations and curate a human-annotated dataset in which explicitations are distinguished from insertions of new information. Our analyses show that while the extraction method is effective in retrieving relevant cases, distinguishing explicitations from new information is a challenging and often subjective task, reflecting differences in background knowledge and reasoning. Experimentally, we find off-the-shelf LLMs to achieve promising performance, with inconsistent gains from few-shot prompting and fine-tuning. In contrast, fine-tuned NLI models benefit consistently from supervised training and show stronger robustness under distribution shift. In sum, our findings show that the task is challenging, but also indicate that our annotated dataset contains informative signals that models can learn from, paving the way for further research on explicitations.

Can You Be More Explicit? A Task and Dataset on Explicitations of Implicit Meaning

Evaluating the quality of automatically generated text often relies on LLM-as-a-judge (LLM-judge) methods. While effective, these approaches are computationally expensive and require post-processing. To address these limitations, we build upon ParaPLUIE, a perplexity-based LLM-judge metric that estimates confidence over “Yes/No” answers without generating text. We introduce *-PLUIE, task-specific prompting variants of ParaPLUIE and evaluate their alignment with human judgement. Our experiments show that personalised *-PLUIE achieves stronger correlations with human ratings while maintaining low computational cost.

*-PLUIE: Personalisable metric with Llm Used for Improved Evaluation

Stance detection seeks to determine whether a text expresses a position in favor of, against, or neutral toward a target. Despite advances in neural architectures, performance remains inconsistent across datasets. To better understand these disparities, we analyze over 75K samples from four benchmark datasets using six neural models, focusing on stylistic and 
pragmatic language features rather than architectures or external knowledge. We extract 43 features spanning lexical 
richness, syntactic complexity, affective tone, and hedging, and assess their impact through both Logistic Regression and 
SHAP analyses. Our findings reveal distinct stylistic profiles for each stance: favor is best detected when expressed 
concisely with minimal hedging; against when paired with strong negative emotions and greater lexical variety; and none 
when texts are lexically simple and emotionally neutral. Across classes, errors arise from excessive complexity, mixed 
emotional signals, and overuse of hedging. These results advance understanding of what drives success and failure in 
stance detection.

Understanding the Linguistic Cues Behind Stance Detection

A large body of research has examined the linguistic abilities of language models (LMs) across various languages. However, conclusive evidence regarding their semantic competence and world knowledge remains limited, especially for low-resource languages. In this study, we explore the semantic competence of Italian BabyLMs, focusing on their sensitivity to semantic violations. To this end, we adapt a minimal pair benchmark targeting semantic violations to evaluate the semantic abilities of BAMBI, a family of small-scale models trained on progressively larger and more complex datasets. We further compare their performance, assessed through accuracy, mean log-likelihood offset, and expected calibration error, with that of three larger Italian LMs. Our findings shed light on this aspect of semantic competence in small-scale models and how this is affected by data scale and training strategies.

Can a Remedy Find a Researcher? Exploring the Development of Semantic Knowledge in Italian BabyLMs

Long-context language models face efficiency challenges as context lengths expand. Traditional tokenization methods like BPE operate on frequency statistics, ignoring semantic structure and over-tokenizing redundant spans. We propose \textbf{SemToken}, a semantic-aware tokenization framework that adaptively compresses token sequences based on semantic density. SemToken uses lightweight encoders to identify and merge semantically equivalent spans, allocates variable granularity based on local semantic density, and dynamically adjusts token budgets during generation. Evaluations on WikiText-103, LongBench, and BookSum demonstrate 2.4$\times$ token reduction, 1.9$\times$ inference speedup, and 67\% memory reduction while preserving or improving model quality. SemToken integrates seamlessly with existing models and achieves multiplicative benefits when combined with FlashAttention (up to 2.7$\times$ total speedup).

SemToken: Semantic-Aware Tokenization for Efficient Long-Context Language Models

Hallucinations are a persistent challenge in natural language generation, including data-to-text. van Deemter (2024) introduced a framework based on the relation of logical consequence ("follows from"), which divides all data-to-text hallucinations into seven disjoint categories. We examine whether human annotators and large language models are able to apply the framework, in two data-to-text domains. Results suggest that the framework is applicable, although there are significant domain-dependent variations, as well as discrepancies between human and model judgments. We also uncover several issues that should inform future work on hallucination.

A Logic-Based Approach to Hallucinations in Data-to-Text NLG: Experiments with Human and LLM Annotators

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.

A framework for annotating and modelling intentions behind metaphor use

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.

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