GenAI4ELab/papercli-papers huggingface dataset:
Cross-Genre Learning for Old English Poetry POS Tagging
Authors: Irene Miani; Sara Stymne; Gregory R. Darwin
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: 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.
A Computational Framework to Identify Self-Aspects in Text
Authors: Jaya Caporusso; Matthew Purver; Senja Pollak
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: 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.
Prompting the Muse: Generating Prosodically-Correct Latin Speech with Large Language Models
Authors: Michele Ciletti
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: This paper presents a workflow that compels an audio-enabled large language model to recite Latin poetry with metrically accurate stress. One hundred hexameters from the Aeneid and the opening elegiac epistula of Ovid’s Heroides constitute the test bed, drawn from the Pedecerto XML corpus, where ictic syllables are marked. A preprocessing pipeline syllabifies each line, converts alien graphemes into approximate English-Italian counterparts, merges obligatory elisions, adds commas on caesurae, upper-cases every ictic syllable, and places a grave accent on its vowel. Verses are then supplied, one at a time, to an LLM-based Text-to-Speech model under a compact system prompt that instructs slow, articulated delivery. From ten stochastic realisations per verse, a team of Latin experts retained the best; at least one fully correct file was found for 91% of the 216 lines. Upper-casing plus accent marking proved the strongest cue, while hyphenating syllables offered no benefit. Remaining errors cluster around cognates where the model inherits a Romance or English stress template. The corpus of validated audio and all scripts are openly released on Zenodo, opening avenues for pedagogy, accessibility, and prosodic research.
Can a Large Language Model Keep My Secrets? A Study on LLM-Controlled Agents
Authors: Niklas Hemken; Sai Koneru; Florian Jacob; Hannes Hartenstein; Jan Niehues
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Agents controlled by Large Language Models (LLMs) can assist with natural language tasks across domains and applications when given access to confidential data.When such digital assistants interact with their potentially adversarial environment, confidentiality of the data is at stake.We investigated whether an LLM-controlled agent can, in a manner similar to humans, consider confidentiality when responding to natural language requests involving internal data.For evaluation, we created a synthetic dataset consisting of confidentiality-aware planning and deduction tasks in organizational access control.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 utilized our dataset to evaluate the ability to infer confidentiality-aware behavior in such scenarios by differentiating between legitimate and illegitimate access requests.We compared 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 found humans reached an accuracy of up to 79%.Prompting techniques, such as chain-of-thought and few-shot prompting, yielded promising results, but still fell short of real-world applicability and do not surpass human baseline performance. However, we found that fine-tuning significantly improves the agent’s access decisions, reaching up to 98% accuracy, making it promising for future confidentiality-aware applications when data is available.
Chart Question Answering from Real-World Analytical Narratives
Authors: Maeve Hutchinson; Radu Jianu; Aidan Slingsby; Jo Wood; Pranava Madhyastha
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: 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.
Low-Perplexity LLM-Generated Sequences and Where To Find Them
Authors: Arthur Wuhrmann; Andrei Kucharavy; Anastasiia Kucherenko
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: 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 leverage and replicate their training data, 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 quantify the distribution of occurrences across source documents, highlighting the scope and nature of verbatim recall and paving a way toward better understanding of how LLMs training data impacts their behavior.
CoLeM: A framework for semantic interpretation of Russian-language tables based on contrastive learning
Authors: Kirill Tobola; Nikita Dorodnykh
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: 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.
Mitigating Hallucination by Integrating Knowledge Graphs into LLM Inference – a Systematic Literature Review
Authors: Robin Wagner; Emanuel Kitzelmann; Ingo Boersch
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: 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.
Semantic alignment in hyperbolic space for fine-grained emotion classification
Authors: Ashish Kumar; Durga Toshniwal
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Existing approaches to fine-grained emotion classification (FEC) often operate in Euclidean space, where the flat geometry limits the ability to distinguish semantically similar emotion labels (e.g., *annoyed* vs. *angry*). While prior research has explored hyperbolic geometry to capture fine-grained label distinctions, it typically relies on predefined hierarchies and ignores semantically similar negative labels that can mislead the model into making incorrect predictions. In this work, we propose HyCoEM (Hyperbolic Contrastive Learning for Emotion Classification), a semantic alignment framework that leverages the Lorentz model of hyperbolic space. Our approach embeds text and label representations into hyperbolic space via the exponential map, and employs a contrastive loss to bring text embeddings closer to their true labels while pushing them away from adaptively selected, semantically similar negatives. This enables the model to learn label embeddings without relying on a predefined hierarchy and better captures subtle distinctions by incorporating information from both positive and challenging negative labels. Experimental results on two benchmark FEC datasets demonstrate the effectiveness of our approach over baseline methods.
I Speak for the Árboles: Developing a Dependency Treebank for Spanish L2 and Heritage Speakers
Authors: Emiliana Pulido; Robert Pugh; Zoey Liu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: We introduce the first dependency treebank containing Universal Dependencies (UD) annotations for Spanish learner writing from the UC Davis COWSL2H corpus. Our annotations include lemmatization, POS tagging, and syntactic dependencies. We adapt the existing UD framework for Spanish L1 to account forlearner-specific features such as code-switching and non-canonical syntax. A suite of parsing evaluation experiments shows that parsers trained on learner data together with moderate sizes of Spanish L1 data can yield reasonable performance. Our annotations are openly accessible to motivate future development of learner-oriented language technologies.
Evaluating Tokenizer Adaptation Methods for Large Language Models on Low-Resource Programming Languages
Authors: Georgy Andryushchenko; Vladimir V. Ivanov
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Large language models (LLMs), which are primarily trained on high-resource programming languages (HRPLs), tend to perform sub-optimally for low-resource programming languages (LRPLs). This study investigates the impact of tokenizer adaptation methods on improving code generation for LRPLs. StarCoder 2 and DeepSeek-Coder models adapted to Elixir and Racket using methods such as Fast Vocabulary Transfer (FVT), FOCUS, and Zero-shot Tokenizer Transfer (ZeTT) are evaluated and compared with the original and fine-tuned models. Our experiments reveal that ZeTT outperforms other methods, achieving significant improvements in handling syntax, program logic, and data types for LRPLs. However, we also highlight performance declines in non-target languages like Python after tokenizer adaptation. The study approves the positive impact of tokenizer adaptation in enhancing LRPL code generation and suggests directions for future research, including token embeddings improvement.
Learning and Enforcing Context-Sensitive Control for LLMs
Authors: Mohammad Albinhassan; Pranava Madhyastha; Mark Law; Alessandra Russo
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limitations of Context-Free Grammars (CFGs) in guaranteeing generation validity. However, such constraints typically require manual specification—a significant barrier demanding specialized expertise. We introduce a framework that automatically learns context-sensitive constraints from LLM interactions through a two-phase process: syntactic exploration to gather diverse outputs for constraint learning, followed by constraint exploitation to enforce these learned rules during generation. Experiments demonstrate that our method enables even small LLMs (1B parameters) to learn and generate with perfect constraint adherence, outperforming larger counterparts and state-of-the-art reasoning models. This work represents the first integration of context-sensitive grammar learning with LLM generation, eliminating manual specification while maintaining generation validity.
When Will the Tokens End? Graph-Based Forecasting for LLMs Output Length
Authors: Grzegorz Piotrowski; Mateusz Bystroński; Mikołaj Hołysz; Jakub Binkowski; Grzegorz Chodak; Tomasz Jan Kajdanowicz
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Large Language Models (LLMs) are typically trained to predict the next token in a sequence. However, their internal representations often encode signals that go beyond immediate next-token prediction. In this work, we investigate whether these hidden states also carry information about the remaining length of the generated output—an implicit form of foresight (CITATION). We formulate this as a regression problem where, at generation step t, the target is the number of remaining tokens yt = T - t, with T as the total output length.We propose two approaches: (1) an aggregation-based model that combines hidden states from multiple transformer layers ℓ ∈ {8, …, 15} using element-wise operations such as mean or sum, and (2) a Layerwise Graph Regressor that treats layerwise hidden states as nodes in a fully connected graph and applies a Graph Neural Network (GNN) to predict yt. Both models operate on frozen LLM embeddings without requiring end-to-end fine-tuning.Accurately estimating remaining output length has both theoretical and practical implications. From an interpretability standpoint, it suggests that LLMs internally track their generation progress. From a systems perspective, it enables optimizations such as output-length-aware scheduling (CITATION). Our graph-based model achieves state-of-the-art performance on the Alpaca dataset using LLaMA-3-8B-Instruct, reducing normalized mean absolute error (NMAE) by over 50% in short-output scenarios.
Only for the Unseen Languages, Say the Llamas: On the Efficacy of Language Adapters for Cross-lingual Transfer in English-centric LLMs
Authors: Julian Schlenker; Jenny Kunz; Tatiana Anikina; Günter Neumann; Simon Ostermann
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Most state-of-the-art large language models (LLMs) are trained mainly on English data, limiting their effectiveness on non-English, especially low-resource, languages. This study investigates whether language adapters can facilitate cross-lingual transfer in English-centric LLMs. We train language adapters for 13 languages using Llama 2 (7B) and Llama 3.1 (8B) as base models, and evaluate their effectiveness on two downstream tasks (MLQA and SIB-200) using either task adapters or in-context learning. Our results reveal that language adapters improve performance for languages not seen during pretraining, but provide negligible benefit for seen languages. These findings highlight the limitations of language adapters as a general solution for multilingual adaptation in English-centric LLMs.
HyILR: Hyperbolic Instance-Specific Local Relationships for Hierarchical Text Classification
Authors: Ashish Kumar; Durga Toshniwal
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Recent approaches to Hierarchical Text Classification (HTC) rely on capturing the global label hierarchy, which contains static and often redundant relationships. Instead, the hierarchical relationships within the instance-specific set of positive labels are more important, as they focus on the relevant parts of the hierarchy. These localized relationships can be modeled as a semantic alignment between the text and its positive labels within the embedding space. However, without explicitly encoding the global hierarchy, achieving this alignment directly in Euclidean space is challenging, as its flat geometry does not naturally support hierarchicalrelationships. To address this, we propose Hyperbolic Instance-Specific Local Relationships (HyILR), which models instance-specific relationships using the Lorentz model of hyperbolic space. Text and label features are projected into hyperbolic space, where a contrastive loss aligns text with its labels. This loss is guided by a hierarchy-aware negative sampling strategy, ensuring the selection of structurally and semantically relevant negatives. By leveraging hyperbolic geometry for this alignment, our approach inherently captures hierarchical relationships and eliminates the need for global hierarchy encoding. Experimental results on four benchmark datasets validate the superior performance of HyILR over baseline methods.
Are LLMs Truly Graph-Savvy? A Comprehensive Evaluation of Graph Generation
Authors: Ege Demirci; Rithwik Kerur; Ambuj Singh
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: While large language models (LLMs) have demonstrated impressive capabilities across diverse tasks, their ability to generate valid graph structures remains underexplored. We evaluate fifteen state-of-the-art LLMs on five specialized graph generation tasks spanning delivery networks, social networks, quantum circuits, gene-disease networks, and transportation systems. We also test the LLMs using 3 different prompt types: direct, iterative feedback, and program-augmented. Models supported with explicit reasoning modules (o3-mini-high, o1, Claude 3.7 Sonnet, DeepSeek-R1) solve more than twice as many tasks as their general-purpose peers, independent of parameter count. Error forensics reveals two recurring failure modes: smaller parameter size Llama models often violate basic structural constraints, whereas Claude models respect topology but mismanage higher-order logical rules. Allowing models to refine their answers iteratively yields uneven gains, underscoring fundamental differences in error-correction capacity. This work demonstrates that graph competence stems from specialized training methodologies rather than scale, establishing a framework for developing truly graph-savvy language models. Results and verification scripts available at https://github.com/egedemirci/Are-LLMs-Truly-Graph-Savvy-A-Comprehensive-Evaluation-of-Graph-Generation.
Pragmatic Perspective on Assessing Implicit Meaning Interpretation in Sentiment Analysis Models
Authors: Rashid Mustafin
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Drawing on pragmatic theories of implicature by Grice (1975) and Levinson (1983), according to which speakers often convey more than it is explicitly said, the paper argues that interpreting texts with implicit meaning correctly is essential for precise natural language understanding. To illustrate the challenges in computational interpretation of implicatures, the study introduces a series of illustrative micro-experiments with the use of four transformer models fine-tuned for sentiment analysis. In these micro-experiments, the models classified sentences specifically designed to expose difficulties in handling implicit meaning. The study demonstrates that contrasting qualitative pragmatic analysis with the models’ tendency to focus on formal linguistic markers can reveal the limitations of supervised machine learning methods in detecting implicit sentiments.