GenAI4ELab/papercli-papers huggingface dataset:
From Stories to Statistics: Methodological Biases in LLM-Based Narrative Flow Quantification
Authors: Amal Sunny; Advay Gupta; Yashashree Chandak; Vishnu Sreekumar
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: Large Language Models (LLMs) have made significant contributions to cognitive science research. One area of application is narrative understanding. Sap et al. (2022) introduced sequentiality, an LLM-derived measure that assesses the coherence of a story based on word probability distributions. They reported that recalled stories flowed less sequentially than imagined stories. However, the robustness and generalizability of this narrative flow measure remain unverified. To assess generalizability, we apply sequentiality derived from three different LLMs to a new dataset of matched autobiographical and biographical paragraphs. Contrary to previous results, we fail to find a significant difference in narrative flow between autobiographies and biographies. Further investigation reveals biases in the original data collection process, where topic selection systematically influences sequentiality scores. Adjusting for these biases substantially reduces the originally reported effect size. A validation exercise using LLM-generated stories with “good” and “poor” flow further highlights the flaws in the original formulation of sequentiality. Our findings suggest that LLM-based narrative flow quantification is susceptible to methodological artifacts. Finally, we provide some suggestions for modifying the sequentiality formula to accurately capture narrative flow.
Components of Creativity: Language Model-based Predictors for Clustering and Switching in Verbal Fluency
Authors: Sina Zarrieß; Simeon Junker; Judith Sieker; Özge Alacam
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: Verbal fluency is an experimental paradigm used to examine human knowledge retrieval, cognitive performance and creative abilities. This work investigates the psychometric capacities of LMs in this task. We focus on switching and clustering patterns and seek evidence to substantiate them as two distinct and separable components of lexical retrieval processes in LMs.We prompt different transformer-based LMs with verbal fluency items and ask whether metrics derived from the language models’ prediction probabilities or internal attention distributions offer reliable predictors of switching/clustering behaviors in verbal fluency. We find that token probabilities, but especially attention-based metrics have strong statistical power when separating between cases of switching and clustering, in line with prior research on human cognition.
An Appraisal Theoretic Approach to Modelling Affect Flow in Conversation Corpora
Authors: Alok Debnath; Yvette Graham; Owen Conlan
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: This paper presents a model of affect in conversations by leveraging Appraisal Theory as a generalizable framework. We propose that the multidimensional cognitive model of Appraisal Theory offers significant advantages for analyzing emotions in conversational contexts, addressing the current challenges of inconsistent annotation methodologies across corpora. To demonstrate this, we present AppraisePLM, a regression and classification model trained on the crowd-EnVent corpus that outperforms existing models in predicting 21 appraisal dimensions including pleasantness, self-control, and alignment with social norms. We apply AppraisePLM to diverse conversation datasets spanning task-oriented dialogues, general-domain chit-chat, affect-specific conversations, and domain-specific affect analysis. Our analysis reveals that AppraisePLM successfully extrapolates emotion labels across datasets, while capturing domain-specific patterns in affect flow – change in conversational emotion over the conversation. This work highlights the entangled nature of affective phenomena in conversation and positions affect flow as a promising model for holistic emotion analysis, offering a standardized approach to evaluate and benchmark affective capabilities in conversational agents.
Principal Parts Detection for Computational Morphology: Task, Models and Benchmark
Authors: Dorin Keshales; Omer Goldman; Reut Tsarfaty
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: Principal parts of an inflectional paradigm, defined as the minimal set of paradigm cells required to deduce all others, constitute an important concept in theoretical morphology. This concept, which outlines the minimal memorization needed for a perfect inflector, has been largely overlooked in computational morphology despite impressive advances in the field over the last decade. In this work, we posit Principal Parts Detection as a computational task and construct a multilingual dataset of verbal principal parts covering ten languages, based on Wiktionary entries. We evaluate an array of Principal Parts Detection methods, all of which follow the same schema: characterize the relationships between each pair of inflectional categories, cluster the resulting vector representations, and select a representative of each cluster as a predicted principal part. Our best-performing model, based on Edit Script between inflections and using Hierarchical K-Means, achieves an F1 score of 55.05%, significantly outperforming a random baseline of 21.20%. While our results demonstrate that some success is achievable, further work is needed to thoroughly solve Principal Parts Detection, a task that may be used to further optimize inputs for morphological inflection, and to promote research into the theoretical and practical importance of a compact representation of morphological paradigms.
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review
Authors: Neha Prakriya; Jui-Nan Yen; Cho-Jui Hsieh; Jason Cong
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: We introduce an effective and scalable data selection technique to accelerate the pretraining of large language models (LLMs). Given the variation in quality and informativeness of web-scale corpora, we present the Learn-Focus-Review (LFR) paradigm-a dynamic training approach that adapts to the model’s learning progress. Inspired by human learning techniques like spaced repetition, LFR tracks the model’s learning performance across data instances and prioritizes revisiting challenging and diverse regions of the dataset that are more prone to being forgotten, enabling better retention and more efficient learning. Through experiments spanning over 2200 GPU hours, we show that LFR significantly enhances data efficiency in pretraining while improving downstream performance across commonsense reasoning, question answering, problem-solving, language modeling, and translation tasks. LFR consistently achieves lower perplexity and higher accuracy using just 5%–19% of the training tokens as models trained on the full dataset. Notably, LFR matches the performance of industry-standard Pythia models with up to 2× the parameter count while requiring only 3.2% of the training tokens. Unlike prior work on data selection, LFR models are Chinchilla-optimal demonstrating the effectiveness of our training methodology.
What does memory retrieval leave on the table? Modelling the Cost of Semi-Compositionality with MINERVA2 and sBERT
Authors: Sydelle de Souza; Ivan Vegner; Francis Mollica; Leonidas A. A. Doumas
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: Despite being ubiquitous in natural language, collocations (e.g., kick+habit) incur a unique processing cost, compared to compositional phrases (kick+door) and idioms (kick+bucket). We confirm this cost with behavioural data as well as MINERVA2, a memory model, suggesting that collocations constitute a distinct linguistic category. While the model fails to fully capture the observed human processing patterns, we find that below a specific item frequency threshold, the model’s retrieval failures align with human reaction times across conditions. This suggests an alternative processing mechanism that activates when memory retrieval fails.
Polarity inversion operators in PLM
Authors: David Kletz; Pascal Amsili; Marie Candito
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: From a linguistic perspective, negation is a unique and inherently compositional operator. In this study, we investigate whether the bert-large-cased Pretrained Language Model (PLM) properly encodes this compositional aspect of negation when embedding a token that falls within the scope of negation.To explore this, we train two external Multi-Layer Perceptrons to modify contextual embeddings in a controlled manner. The goal is to reverse the polarity information encoded in the embedding while preserving all other token-related information. The first MLP, called the Negator, transforms a negative polarity into a positive one, while the second, the Affirmator, performs the reverse transformation.We then conduct a series of evaluations to assess the effectiveness of these operators. Our results indicate that while the Negator/Affirmator is functional, it only partially simulates the negation operator. Specifically, applying it recursively does not allow us to recover the original polarity, suggesting an incomplete representation of negation within the PLM’s embeddings.In addition, a downstream evaluation on the Negated LAMA dataset reveals that the modifications introduced by the Negator/Affirmator lead to a slight improvement in the model’s ability to account for negation in its predictions. However, applying the Negator/Affirmator recursively results in degraded representations, further reinforcing the idea that negation is not fully compositional within PLM embeddings.
Dynamic Epistemic Friction in Dialogue
Authors: Timothy Obiso; Kenneth Lai; Abhijnan Nath; Nikhil Krishnaswamy; James Pustejovsky
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: Recent developments in aligning Large Language Models (LLMs) with human preferences have significantly enhanced their utility in human-AI collaborative scenarios. However, such approaches often neglect the critical role of “epistemic friction,” or the inherent resistance encountered when updating beliefs in response to new, conflicting, or ambiguous information. In this paper, we define *dynamic epistemic friction* as the resistance to epistemic integration, characterized by the misalignment between an agent’s current belief state and new propositions supported by external evidence. We position this within the framework of Dynamic Epistemic Logic, where friction emerges as nontrivial belief-revision during the interaction. We then present analyses from a situated collaborative task that demonstrate how this model of epistemic friction can effectively predict belief updates in dialogues, and we subsequently discuss how the model of belief alignment as a measure of epistemic resistance or friction can naturally be made more sophisticated to accommodate the complexities of real-world dialogue scenarios.
A Three-Tier LLM Framework for Forecasting Student Engagement from Qualitative Longitudinal Data
Authors: Ahatsham Hayat; Helen Martinez; Bilal Khan; Mohammad Rashedul Hasan
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: Forecasting nuanced shifts in student engagement from longitudinal experiential (LE) data—multi-modal, qualitative trajectories of academic experiences over time—remains challenging due to high dimensionality and missingness. We propose a natural language processing (NLP)-driven framework using large language models (LLMs) to forecast binary engagement levels across four dimensions: Lecture Engagement Disposition, Academic Self-Efficacy, Performance Self-Evaluation, and Academic Identity and Value Perception. Evaluated on 960 trajectories from 96 first-year STEM students, our three-tier approach—LLM-informed imputation to generate textual descriptors for missing-not-at-random (MNAR) patterns, zero-shot feature selection via ensemble voting, and fine-tuned LLMs—processes textual non-cognitive responses. LLMs substantially outperform numeric baselines (e.g., Random Forest, LSTM) by capturing contextual nuances in student responses. Encoder-only LLMs surpass decoder-only variants, highlighting architectural strengths for sparse, qualitative LE data. Our framework advances NLP solutions for modeling student engagement from complex LE data, excelling where traditional methods struggle.
Bridging the Socioeconomic Gap in Education: A Hybrid AI and Human Annotation Approach
Authors: Nahed Abdelgaber; Labiba Jahan; Arham Vinit Doshi; Rishi Suri; Hamza Reza Pavel; Jia Zhang
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: Students’ academic performance is influenced by various demographic factors, with socioeconomic class being a prominently researched and debated factor. Computer Science research traditionally prioritizes computationally definable problems, yet challenges such as the scarcity of high-quality labeled data and ethical concerns surrounding the mining of personal information can pose barriers to exploring topics like the impact of SES on students’ education. Overcoming these barriers may involve automating the collection and annotation of high-quality language data from diverse social groups through human collaboration. Therefore, our focus is on gathering unstructured narratives from Internet forums written by students with low socioeconomic status (SES) using machine learning models and human insights. We developed a hybrid data collection model that semi-automatically retrieved narratives from the Reddit website and created a dataset five times larger than the seed dataset. Additionally, we compared the performance of traditional ML models with recent large language models (LLMs) in classifying narratives written by low-SES students, and analyzed the collected data to extract valuable insights into the socioeconomic challenges these students encounter and the solutions they pursue.
Construction Identification and Disambiguation Using BERT: A Case Study of NPN
Authors: Wesley Scivetti; Nathan Schneider
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: Construction Grammar hypothesizes that knowledge of a language consists chiefly of knowledge of form–meaning pairs (“constructions”) that include vocabulary, general grammar rules, and even idiosyncratic patterns. Recent work has shown that transformer language models represent at least some constructional patterns, including ones where the construction is rare overall. In this work, we probe BERT’s representation of the form and meaning of a minor construction of English, the NPN (noun–preposition–noun) construction—exhibited in such expressions as face to face and day to day—which is known to be polysemous. We construct a benchmark dataset of semantically annotated corpus instances (including distractors that superficially resemble the construction). With this dataset, we train and evaluate probing classifiers. They achieve decent discrimination of the construction from distractors, as well as sense disambiguation among true instances of the construction, revealing that BERT embeddings carry indications of the construction’s semantics. Moreover, artificially permuting the word order of true construction instances causes them to be rejected, indicating sensitivity to matters of form. We conclude that BERT does latently encode at least some knowledge of the NPN construction going beyond a surface syntactic pattern and lexical cues.
Evidence of Generative Syntax in Large Language Models
Authors: Mary Katie Kennedy
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: The syntactic probing literature has been largely limited to shallow structures like dependency trees, which are unable to capture the subtle differences in sub-surface syntactic structures that yield semantic nuances. These structures are captured by theories of syntax like generative syntax, but have not been researched in the LLM literature due to the difficulties in probing these complex structures with many silent, covert nodes. Our work presents a method for overcoming this limitation by deploying Hewitt and Manning’s (2019) dependency-trained probe on sentence constructions whose structural representation is identical in a dependency parse, but differs in theoretical syntax. If a pretrained language model has captured the theoretical syntax structure, then the probe’s predicted distances should vary in syntactically-predicted ways. Using this methodology and a novel dataset, we find evidence that LLMs have captured syntactic structures far richer than previously realized, indicating LLMs are able to capture the nuanced meanings that result from sub-surface differences in structural form.
Timestep Embeddings Trigger Collapse in Diffusion Text Generation
Authors: Ryota Nosaka; Takuya Matsuzaki
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: Diffusion models have achieved remarkable success in various generative tasks, particularly in image and audio synthesis, which work by iteratively refining random noise into realistic data. Recent studies have highlighted the potential of diffusion models for text generation, but several challenges remain unresolved. One significant issue is that the model begins to degrade a previous sample rather than improve it after a certain timestep in the generation process, resulting in broken text. In this paper, we reveal that timestep embeddings are a principal cause of the collapse problem by analyzing their interactions with word embeddings. Further, we propose two key methods: (a) a simple lightweight word embedding technique that enhances model analyzability as well as learning efficiency; (b) a novel regularization on both word and timestep embeddings. Experimental results demonstrate that our approach effectively mitigates the collapse problem and can lead to a considerable improvement in the quality of generated text.
Investigating Psychometric Predictive Power of Syntactic Attention
Authors: Ryo Yoshida; Yushi Sugimoto; Yohei Oseki
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: In computational psycholinguistics, Merkx and Frank (2021) demonstrated that surprisal values from Transformers exhibit a closer fit to measures of human reading effort than those from Recurrent Neural Networks (RNNs), suggesting that Transformers’ attention mechanisms may capture cue-based retrieval-like operations in human sentence processing. Meanwhile, explicit integration of syntactic structures has been shown to improve language models’ ability to model human sentence processing—for example, Hale et al. (2018) demonstrated that Recurrent Neural Network Grammars (RNNGs), which integrate RNNs with explicit syntactic structures, account for human brain activities that vanilla RNNs cannot capture. In this paper, we investigate the psychometric predictive power of Composition Attention Grammars (CAGs), which integrate Transformers with explicit syntactic structures, to test whether they provide a better fit to human reading times than both vanilla Transformers and RNNGs. We hypothesized that CAGs’ syntactic attention mechanisms capture cue-based retrieval-like operations over syntactic memory representations—operations that may be involved in human sentence processing. The results of our strictly controlled experiments demonstrate that CAGs outperformed vanilla Transformers and RNNGs, suggesting that the syntactic attention mechanisms of CAGs may serve as a mechanistic implementation of cue-based retrieval from syntactic memory.
A Continuous Approach to Metaphorically Motivated Regular Polysemy in Language Models
Authors: Anna Temerko; Marcos Garcia; Pablo Gamallo
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: Linguistic accounts show that a word’s polysemy structure is largely governed by systematic sense alternations that form overarching patterns across the vocabulary. While psycholinguistic studies confirm the psychological validity of regularity in human language processing, in the research on large language models (LLMs) this phenomenon remains largely unaddressed. Revealing models’ sensitivity to systematic sense alternations of polysemous words can give us a better understanding of how LLMs process ambiguity and to what extent they emulate representations in the human mind. For this, we employ the measures of surprisal and semantic similarity as proxies of human judgment on the acceptability of novel senses. We focus on two aspects that have not received much attention previously —metaphorically motivated patterns and the continuous nature of regularity. We find evidence that surprisal from language models represents regularity of polysemic extensions in a human-like way, discriminating between different types of senses and varying regularity degrees, and overall strongly correlating with human acceptability scores.
Is Incremental Structure Prediction Process Universal across Languages?: Revisiting Parsing Strategy through Speculation
Authors: Taiga Ishii; Yusuke Miyao
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: While natural language is processed incrementally, it is unclear whether the syntactic structure prediction process is universal across languages or language-specific. This study investigates this question by revisiting parsing strategies of syntactic language models that incrementally predict both the next token and the associated syntactic structure. Unlike previous studies that have focused on a few strategies, we examine a wide range of strategies by introducing different parameterizations of “speculation”, which quantifies the degree to which a model predicts syntactic structure before encountering the corresponding tokens. The experiments with 10 typologically diverse languages reveal that the optimal strategy differs depending on the language and the beam size.
Lost in Variation? Evaluating NLI Performance in Basque and Spanish Geographical Variants
Authors: Jaione Bengoetxea; Itziar Gonzalez-Dios; Rodrigo Agerri
Venue: ACL 2025 (Proceedings of the 29th Conference on Computational Natural Language Learning)

Abstract: In this paper, we evaluate the capacity of current language technologies to understand Basque and Spanish language varieties. We use Natural Language Inference (NLI) as a pivot task and introduce a novel, manually-curated parallel dataset in Basque and Spanish, along with their respective variants. Our empirical analysis of crosslingual and in-context learning experiments using encoder-only and decoder-based Large Language Models (LLMs) shows a performance drop when handling linguistic variation, especially in Basque. Error analysis suggests that this decline is not due to lexical overlap, but rather to the linguistic variation itself. Further ablation experiments indicate that encoder-only models particularly struggle with Western Basque, which aligns with linguistic theory that identifies peripheral dialects (e.g., Western) as more distant from the standard. All data and code are publicly available.