Austria

The cross-modal grounding of LLMs has recently garnered significant attention, while grounding them in textual interactions has been less explored. As the first of its kind, the GLAM framework utilises LLMs as agents in interactive text-based games to investigate their grounding capabilities. However, it faces the challenge of low computational efficiency, which hinders further experiments. This paper proposes the use of Lookahead models for action selection, demonstrating through empirical results that the approach can substantially improve training speed, achieving performance gains relative to the size of the action space.

ACL 2025

Interactive Text Games: Lookahead Is All You Need!

multi-token llms

lookahead llms

conversational grounding

text-based games

grounded language learning

large language models

language grounding

poster

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

Generation Alpha (born 2010-2024) is the first generation fully raised within the digital ecosystem. They exhibit unique linguistic behaviours influenced by rampant online communication and platform-specific cultures. This study examines the rapid evolution of Gen Alpha slang through a comparative analysis of Millennial and Gen Z vernacular. We identify three core linguistic patterns: extreme lexical compression, digital culture-driven semantic shifts and part-of-speech conversion. We construct a comprehensive slang corpus sourced from online platforms and evaluate the performance of four AI translation systems (viz. Google Translate, ChatGPT 4, Gemini 1.0, DeepSeek v3) on over 100 slang terms. Our results reveal significant translation challenges rooted in culturally-bound terms from gaming, meme culture, and mental health discourse. Most errors are the result of inadequate cultural contextualization, with literal translations dominating the error patterns. Our findings highlight the critical limitations in current language models and emphasize the need for adaptive, culturally sensitive and context-aware frameworks that can handle the dynamic lexicon of evolving youth vernacular.

The Evolution of Gen Alpha Slang: Linguistic Patterns and AI Translation Challenges

We propose a simple and light-weight, yet effective hallucination detection method for conditional text generation. 
Hallucinated outputs include information that is either absent from and/or difficult to infer from the input context. 
Leveraging this feature, we add contrastive learning to the hallucination detection classifier to pull faithful outputs and input contexts together while pushing hallucinated outputs apart. 
Experimental results confirm that our method on top of RoBERTa improves binary hallucination detection performance, outperforming much larger GPT-$4$o prompting. 
Remarkably, our method shows higher performance for outputs where hallucinated spans are sparse.

Light-Weight Hallucination Detection using Contrastive Learning for Conditional Text Generation

Poetry has always distinguished itself from other literary genres in many ways, including grammatically and syntactically. These differences are evident not only in modern literature but also in earlier stages. Linguistic analysis tools struggle to address these differences. This paper focuses on the dichotomy between Old English poetry and prose, specifically in the context of the POS tagging task.
Two annotated corpora representing each genre were analyzed to show that there are several types of structural differences between Old English poetry and prose. For POS tagging, we conduct experiments on both a detailed tag set with over 200 tags and a mapping to the UPOS tag set with 17 tags. We establish a baseline and conduct two cross-genre experiments to investigate the effect of different proportions of prose and poetry data. Across both tag sets, our results indicate that if the divergence between two genres is substantial, simply increasing the quantity of training data from the support genre does not necessarily improve prediction accuracy. However, incorporating even a small amount of target data can lead to better performance compared to excluding it entirely. This study not only highlights the linguistic differences between Old English poetry and prose but also emphasizes the importance of developing effective NLP tools for underrepresented historical languages across all genres.

Cross-Genre Learning for Old English Poetry POS Tagging

This Ph.D. proposal introduces a plan to develop a computational framework to identify Self-aspects in text. The Self is a multifaceted construct and it is reflected in language. While it is described across disciplines like cognitive science and phenomenology, it remains underexplored in natural language processing (NLP). Many of the aspects of the Self align with psychological and other well-researched phenomena (e.g., those related to mental health), highlighting the need for systematic NLP-based analysis. In line with this, we plan to introduce an ontology of Self-aspects and a gold-standard annotated dataset. Using this foundation, we will develop and evaluate conventional discriminative models, generative large language models, and embedding-based retrieval approaches against four main criteria: interpretability, ground-truth adherence, accuracy, and computational efficiency. Top-performing models will be applied in case studies in mental health and empirical phenomenology.

A Computational Framework to Identify Self-Aspects in Text

Agents controlled by large language models (LLMs) have the potential to assist humans in natural language tasks across various domains and applications, if they are provided with access to confidential data of their principal. When such digital assistants interact with their potentially adversarial environment, confidentiality of the data is at stake. Given a natural language request, we investigate whether an LLM-controlled agent can control access to internal data by considering confidentiality in its response, in a manner similar to humans. For evaluation, we created a synthetic dataset consisting of confidentiality-aware planning and deduction tasks in an organizational access control setting. The dataset was developed from human input, LLM-generated content, and existing datasets. It includes various everyday scenarios in which access to confidential or private information is requested. We utilize our dataset to evaluate the ability to infer confidentiality-aware behavior in such scenarios by differentiating between legitimate and illegitimate access requests. We compare a prompting-based and a fine-tuning-based approach, to evaluate the performance of Llama 3 and GPT-4o-mini in this domain. In addition, we conducted a user study to establish a baseline for human evaluation performance in these tasks. We find humans reached an accuracy of up to 79%. Prompting techniques, such as chain-of-thought and few-shot prompting, yield promising results, but still fall short of real-world applicability and do not surpass human baseline performance. However, we find that fine-tuning significantly improves the agent’s ability to make access decisions up to an accuracy of 98%, making it a promising approach for future confidentiality-aware applications when data is available.

Can a Large Language Model Keep My Secrets? A Study on LLM-Controlled Agents

We present a new dataset for chart question answering (CQA) constructed from visualization notebooks. The dataset features real-world, multi-view charts paired with natural language questions grounded in analytical narratives. Unlike prior benchmarks, our data reflects ecologically valid reasoning workflows. Benchmarking state-of-the-art multimodal large language models reveals a significant performance gap, with GPT-4.1 achieving an accuracy of 69.3\%, underscoring the challenges posed by this more authentic CQA setting.

Chart Question Answering from Real-World Analytical Narratives

As Large Language Models (LLMs) become increasingly widespread, understanding how specific training data shapes their outputs is crucial for transparency, accountability, privacy, and fairness. To explore how LLMs recall and replicate learned information, we introduce a systematic approach centered on analyzing low-perplexity sequences—high-probability text spans generated by the model. Our pipeline reliably extracts such long sequences across diverse topics while avoiding degeneration, then traces them back to their sources in the training data. Surprisingly, we find that a substantial portion of these low-perplexity spans cannot be mapped to the corpus. For those that do match, we analyze the types of memorization involved and present the distribution of unique documents contributing to these mappings, highlighting the extent of verbatim recall.

Low-Perplexity LLM-Generated Sequences and Where To Find Them

Tables are extensively utilized to represent and store data, however, they often lack explicit semantics necessary for machine interpretation of their contents. Semantic table interpretation is essential for integrating structured data with knowledge graphs, yet existing methods face challenges with Russian-language tables due to limited labeled data and linguistic peculiarities. This paper introduces a contrastive learning approach to minimize reliance on manual labeling and enhance the accuracy of column annotation for rare semantic types. The proposed method adapts contrastive learning for tabular data through augmentations and employs a distilled multilingual BERT model trained on the unlabeled RWT corpus (comprising 7.4 million columns). The resulting table representations are incorporated into the RuTaBERT pipeline, reducing computational overhead. Experimental results demonstrate a micro-F1 score of 97% and a macro-F1 score of 92%, surpassing several baseline approaches. These findings emphasize the efficiency of the proposed method in addressing data sparsity and handling unique features of the Russian language. The results further confirm that contrastive learning effectively captures semantic similarities among columns without explicit supervision, which is particularly vital for rare data types.

CoLeM: A framework for semantic interpretation of Russian-language tables based on contrastive learning

Large Language Models (LLMs) demonstrate strong performance on different language tasks, but tend to hallucinate -- generate plausible but factually incorrect outputs. Recently, several approaches to integrate Knowledge Graphs (KGs) into LLM inference were published to reduce hallucinations. This paper presents a systematic literature review (SLR) of such approaches. Following established SLR methodology, we identified relevant work by systematically search in different academic online libraries and applying a selection process. Nine publications were chosen for in-depth analysis. Our synthesis reveals differences and similarities of how the KG is accessed, traversed, and how the context is finally assembled. KG integration can significantly improve LLM performance on benchmark datasets and additionally to mitigate hallucination enhance reasoning capabilities, explainability, and access to domain-specific knowledge. We also point out current limitations and outline directions for future work.

Mitigating Hallucination by Integrating Knowledge Graphs into LLM Inference -- a Systematic Literature Review

Due to the sensitive nature of personally identifiable information (PII), its owners may have the authority to control its inclusion or request its removal from large-language model (LLM) training. 
Beyond this, PII may be added or removed from training datasets due to evolving dataset curation techniques, because they were newly scraped for retraining, or because they were included in a new downstream fine-tuning stage. We find that the amount and ease of PII memorization is a dynamic property of a model that evolves throughout training pipelines and depends on commonly altered design choices. We characterize three such novel phenomena: (1) similar-appearing PII seen later in training can elicit memorization of earlier-seen sequences in what we call assisted memorization, and this is a significant factor (in our settings, up to 1/3); (2) adding PII can increase memorization of other PII; and (3) removing PII can lead to other PII being memorized.

Interactive Text Games: Lookahead Is All You Need!

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The Evolution of Gen Alpha Slang: Linguistic Patterns and AI Translation Challenges

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