GenAI4ELab/papercli-papers huggingface dataset:
CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues
Authors: Disha Sheshanarayana; Tanishka Magar; Ayushi Mittal; Neelam Chaplot
Venue: ACL 2025 (Proceedings of the Third Workshop on Social Influence in Conversations (SICon 2025))

Abstract: Courtrooms are places where lives are determined and fates are sealed, yet they are not impervious to manipulation. Strategic use of manipulation in legal jargon can sway the opinions of judges and affect the decisions. Despite the growing advancements in NLP, its application in detecting and analyzing manipulation within the legal domain remains largely unexplored. Our work addresses this gap by introducing LegalCon, a dataset of 1,063 annotated courtroom conversations labeled for manipulation detection, identification of primary manipulators, and classification of manipulative techniques, with a focus on long conversations. Furthermore, we propose CLAIM, a two-stage, Intent-driven Multi-agent framework designed to enhance manipulation analysis by enabling context-aware and informed decision-making. Our results highlight the potential of incorporating agentic frameworks to improve fairness and transparency in judicial processes. We hope that this contributes to the broader application of NLP in legal discourse analysis and the development of robust tools to support fairness in legal decision-making. Our code and data are available at CLAIM.
Steering Conversational Large Language Models for Long Emotional Support Conversations
Authors: Navid Madani; Rohini Srihari
Venue: ACL 2025 (Proceedings of the Third Workshop on Social Influence in Conversations (SICon 2025))

Abstract: In this study, we address the challenge of consistently following emotional support strategies in long conversations by large language models (LLMs). We introduce the Strategy-Relevant Attention (SRA) metric, a model-agnostic measure designed to evaluate the effectiveness of LLMs in adhering to strategic prompts in emotional support contexts. By analyzing conversations within the Emotional Support Conversations dataset (ESConv) using LLaMA models, we demonstrate that SRA is significantly correlated with a model’s ability to sustain the outlined strategy throughout the interactions. Our findings reveal that the application of SRA-informed prompts leads to enhanced strategic adherence, resulting in conversations that more reliably exhibit the desired emotional support strategies over longer conversations. Furthermore, we contribute a comprehensive, multi-branch synthetic conversation dataset for ESConv, featuring a variety of strategy continuations informed by our optimized prompting method. The code and data are publicly available on our Github.
Text Overlap: An LLM with Human-like Conversational Behaviors
Authors: JiWoo Kim; Minsuk Chang; JinYeong Bak
Venue: ACL 2025 (Proceedings of the Third Workshop on Social Influence in Conversations (SICon 2025))

Abstract: Traditional text-based human-AI interactions typically follow a strict turn-taking approach. This rigid structure limits conversational flow, unlike natural human conversations, which can freely incorporate overlapping speech. However, our pilot study suggests that even in text-based interfaces, overlapping behaviors such as backchanneling and proactive responses lead to more natural and functional exchanges. Motivated by these findings, we introduce text-based overlapping interactions as a new challenge in human-AI communication, characterized by real-time typing, diverse response types, and interruptions. To enable AI systems to handle such interactions, we define three core tasks: deciding when to overlap, selecting the response type, and generating utterances. We construct a synthetic dataset for these tasks and train OverlapBot, an LLM-driven chatbot designed to engage in text-based overlapping interactions. Quantitative and qualitative evaluations show that OverlapBot increases turn exchanges compared to traditional turn-taking systems, with users making 72% more turns and the chatbot 130% more turns, which is perceived as efficient by end-users. This finding supports overlapping interactions and enhances communicative efficiency and engagement.
Investment-Driven Social Influence: A Statistical Physics Approach to Advertising Response
Authors: Javier Marín
Venue: ACL 2025 (Proceedings of the Third Workshop on Social Influence in Conversations (SICon 2025))

Abstract: This paper explores social influence in consumer responses to advertising through investment-mediated conversational dynamics. We implement conversational engagement via advertising expenditure patterns, recognizing that marketing spend directly translates into conversational volume and reach across multi-channel ecosystems. Our approach integrates social psychology frameworks with statistical physics analogies as epistemic scaffolding following Ruse’s änalogy as heuristic” idea. The model introduces three parameters—Marketing Sensitivity, Response Sensitivity, and Behavioral Sensitivity—quantifying emergent properties of investment-driven influence networks. Validation against three real-world datasets shows competitive performance compared to conventional approaches of modeling the consumer response curve like Michaelis-Menten and Hill equations, with context-dependent advantages in network-driven scenarios. These findings illustrate how advertising ecosystems operate as complex adaptive systems (CAS) where influence propagates through investment-amplified conversational networks.
Extended Abstract: Probing-Guided Parameter-Efficient Fine-Tuning for Balancing Linguistic Adaptation and Safety in LLM-based Social Influence Systems
Authors: Manyana Tiwari
Venue: ACL 2025 (Proceedings of the Third Workshop on Social Influence in Conversations (SICon 2025))

Abstract: Designing effective LLMs for social influence (SI) tasks demands controlling linguistic output such that it adapts to context (such as user attributes, history etc.) while upholding ethical guardrails. Standard Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA struggle to manage the trade-off between adaptive linguistic expression and safety and they optimize based on overall objectives without differentiating the functional roles of internal model components. Therefore, we introduce Probing-Guided PEFT (PG-PEFT), a novel fine-tuning strategy which utilizes interpretability probes to identify LLM components associated with context-driven linguistic variations versus those linked to safety violations (e.g., toxicity, bias). This functional map then guides LoRA updates, enabling more targeted control over the model’s linguistic output. We evaluate PG-PEFT on SI tasks (persuasion, negotiation) and linguistic adaptability with safety benchmarks against standard PEFT.
InstructionCP: A Simple yet Effective Approach for Transferring Large Language Models to Target Languages
Authors: Kuang-Ming Chen; Jenq-Neng Hwang; Hung-yi Lee
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: The rapid development of large language models (LLMs) in recent years has largely focused on English, resulting in models that respond exclusively in English. To adapt these models to other languages, continual pre-training (CP) is often employed, followed by supervised fine-tuning (SFT) to maintain conversational abilities. However, CP and SFT can reduce a model’s ability to filter harmful content. We propose Instruction Continual Pre-training (InsCP), which integrates instruction tags—also known as chat templates—into the CP process to prevent loss of conversational proficiency while acquiring new languages. Our experiments demonstrate that InsCP retains conversational and Reinforcement Learning from Human Feedback (RLHF) abilities. Empirical evaluations on language alignment, reliability, and knowledge benchmarks confirm the efficacy of InsCP. Notably, this approach requires only 0.1 billion tokens of high-quality instruction-following data, thereby reducing resource consumption.
Analyzing the Linguistic Priors of Language Models with Synthetic Languages
Authors: Alessio Tosolini; Terra Blevins
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: While modern language model architectures are often assumed to be language-agnostic, there is limited evidence as to whether these models actually process the wide diversity of natural languages equally well. We investigate this question by analyzing how well LMs learn carefully constructed artificial languages containing a variety of verbal complexity, ranging from simple paradigms to covering far more verb classes than occur in natural languages. Rather than learning all languages equally efficiently, models trained on these languages show strict preferences for processing simpler languages. Furthermore, while some observed behaviors mimic human linguistic priors, we find that they indicate the model memorizes its training data rather than generalizes from it.
Unstable Grounds for Beautiful Trees? Testing the Robustness of Concept Translations in the Compilation of Multilingual Wordlists
Authors: David Snee; Luca Ciucci; Arne Rubehn; Kellen Parker van Dam; Johann-Mattis List
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: Multilingual wordlists play a crucial role in comparative linguistics. While many studies have been carried out to test the power of computational methods for language subgrouping or divergence time estimation, few studies have put the data upon which these studies are based to a rigorous test. Here, we conduct a first experiment that tests the robustness of concept translation as an integral part of the compilation of multilingual wordlists. Investigating the variation in concept translations in independently compiled wordlists from 10 dataset pairs covering 9 different language families, we find that on average, only 83% of all translations yield the same word form, while identical forms in terms of phonetic transcriptions can only be found in 23% of all cases. Our findings can prove important when trying to assess the uncertainty of phylogenetic studies and the conclusions derived from them.
Annotating and Inferring Compositional Structures in Numeral Systems Across Languages
Authors: Arne Rubehn; Christoph Rzymski; Luca Ciucci; Katja Bocklage; Alžběta Kučerová; David Snee; Abishek Stephen; Kellen Parker van Dam; Johann-Mattis List
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: Numeral systems across the world’s languages vary in fascinating ways, both regarding their synchronic structure and the diachronic processes that determined how they evolved in their current shape. For a proper comparison of numeral systems across different languages, however, it is important to code them in a standardized form that allows for the comparison of basic properties. Here, we present a simple but effective coding scheme for numeral annotation, along with a workflow that helps to code numeral systems in a computer-assisted manner, providing sample data for numerals from 1 to 40 in 25 typologically diverse languages. We perform a thorough analysis of the sample, focusing on the systematic comparison between the underlying and the surface morphological structure. We further experiment with automated models for morpheme segmentation, where we find allomorphy as the major reason for segmentation errors. Finally, we show that subword tokenization algorithms are not viable for discovering morphemes in low-resource scenarios.
Beyond the Data: The Impact of Annotation Inconsistencies in UD Treebanks on Typological Universals and Complexity Assessment
Authors: Antoni Brosa-Rodríguez; M. Dolores Jiménez-López
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: This study explores the impact of annotation inconsistencies in Universal Dependencies (UD) treebanks on typological research in computational linguistics. UD provides a standardized framework for cross-linguistic annotation, facilitating large-scale empirical studies on linguistic diversity and universals. However, despite rigorous guidelines, annotation inconsistencies persist across treebanks. The objective of this paper is to assess how these inconsistencies affect typological universals, linguistic descriptions, and complexity metrics. We analyze systematic annotation errors in multiple UD treebanks, focusing on morphological features. Case studies on Spanish and Dutch demonstrate how differing annotation decisions within the same language create contradictory typological profiles. We classify the errors into two main categories: overgeneration errors (features incorrectly annotated, since do not actually exist in a language) and data omission errors (inconsistent or incomplete annotation of features that do exist). Our results show that these inconsistencies significantly distort typological analyses, leading to false generalizations and miscalculations of linguistic complexity. We propose methodological safeguards for typological research using UD data. Our findings highlight the need for methodological improvements to ensure more reliable cross-linguistic generalizations in computational typology.
Beyond cognacy
Authors: Gerhard Jäger
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: Computational phylogenetics has become an established tool in historical linguistics, with many language families now analyzed using likelihood-based inference. However, standard approaches rely on expert-annotated cognate sets, which are sparse, labor-intensive to produce, and limited to individual language families. This paper explores alternatives by comparing the established method to two fully automated methods that extract phylogenetic signal directly from lexical data. One uses automatic cognate clustering with unigram/concept features; the other applies multiple sequence alignment (MSA) derived from a pair-hidden Markov model. Both are evaluated against expert classifications from Glottolog and typological data from Grambank. Also, the intrinsic strengths of the phylogenetic signal in the characters are compared. Results show that MSA-based inference yields trees more consistent with linguistic classifications, better predicts typological variation, and provides a clearer phylogenetic signal, suggesting it as a promising, scalable alternative to traditional cognate-based methods. This opens new avenues for global-scale language phylogenies beyond expert annotation bottlenecks.
SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods
Authors: Roksana Goworek; Harpal Singh Karlcut; Hamza Shezad; Nijaguna Darshana; Abhishek Mane; Syam Bondada; Raghav Sikka; Ulvi Mammadov; Rauf Allahverdiyev; Sriram Satkirti Purighella; Paridhi Gupta; Muhinyia Ndegwa; Bao Khanh Tran; Haim Dubossarsky
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: This paper addresses the critical need for high-quality evaluation datasets in low-resource languages to advance cross-lingual transfer. While cross-lingual transfer offers a key strategy for leveraging multilingual pretraining to expand language technologies to understudied and typologically diverse languages, its effectiveness is dependent on quality and suitable benchmarks. We release new sense-annotated datasets of sentences containing polysemous words, spanning nine low-resource languages across diverse language families and scripts. To facilitate dataset creation, the paper presents a demonstrably beneficial semi-automatic annotation method. The utility of the datasets is demonstrated through Word-in-Context (WiC) formatted experiments that evaluate transfer on these low-resource languages. Results highlight the importance of targeted dataset creation and evaluation for effective polysemy disambiguation in low-resource settings and transfer studies. The released datasets and code aim to support further research into fair, robust, and truly multilingual NLP.
XCOMPS: A Multilingual Benchmark of Conceptual Minimal Pairs
Authors: Linyang He; Ercong Nie; Sukru Samet Dindar; Arsalan Firoozi; Adrian Florea; Van Nguyen; Corentin Puffay; Riki Shimizu; Haotian Ye; Jonathan Brennan; Helmut Schmid; Hinrich Schütze; Nima Mesgarani
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: In this work, we introduce XCOMPS, a multilingual conceptual minimal pair dataset that covers 17 languages.Using this dataset, we evaluate LLMs’ multilingual conceptual understanding through metalinguistic prompting, direct probability measurement, and neurolinguistic probing. We find that: 1) LLMs exhibit weaker conceptual understanding for low-resource languages, and accuracy varies across languages despite being tested on the same concept sets. 2) LLMs excel at distinguishing concept-property pairs that are visibly different but exhibit a marked performance drop when negative pairs share subtle semantic similarities. 3) More morphologically complex languages yield lower concept understanding scores and require deeper layers for conceptual reasoning.
Tone in Perspective: A Computational Typological Analysis of Tone Function in ASR
Authors: Siyu Liang; Gina-Anne Levow
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: This study investigates the impact of pitch flattening on automatic speech recognition (ASR) performance across tonal and non-tonal languages. Using vocoder-based signal processing techniques, we created pitch-flattened versions of speech recordings and compared ASR performance against original recordings. Results reveal that tonal languages experience substantially larger performance degradation than non-tonal languages. Analysis of tone confusion matrices shows systematic patterns of misidentification where contour tones collapse toward level tones when pitch information is removed. Calculation of tone’s functional load at syllable and word levels demonstrates that syllable-level functional load strongly predicts ASR vulnerability to pitch flattening, while word-level patterns reflect each language’s morphological structure. These findings illuminate the differential importance of pitch information across languages and suggest that ASR systems for languages with high syllable-level functional load require more robust pitch modeling.
A discovery procedure for synlexification patterns in the world’s languages
Authors: Hannah S. Rognan; Barend Beekhuizen
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: Synlexification is the pattern of crosslinguistic lexical semantic variation whereby what is expressed in a single word in one language, is expressed in multiple words in another (e.g., French ‘monter’ vs. English ‘go+up’). We introduce a computational method for automatically extracting instances of synlexification from a parallel corpus at a large scale (many languages, many domains). The method involves debiasing the seed language by splitting up synlexifications in the seed language where other languages consistently split them. The method was applied to a massively parallel corpus of 198 Bible translations. We validate it on a broad sample of cases, and demonstrate its potential for typological research.
Construction-Based Reduction of Translationese for Low-Resource Languages: A Pilot Study on Bavarian
Authors: Peiqin Lin; Marion Thaler; Daniela Goschala; Amir Hossein Kargaran; Yihong Liu; André F. T. Martins; Hinrich Schütze
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: When translating into a low-resource language, a language model can have a tendency to produce translations that are close to the source (e.g., word-by-word translations) due to a lack of rich low-resource training data in pretraining. Thus, the output often is translationese that differs considerably from what native speakers would produce naturally. To remedy this, we synthetically create a training set in which the frequency of a construction unique to the low-resource language is artificially inflated. For the case of Bavarian, we show that, after training, the language model has learned the unique construction and that native speakers judge its output as more natural. Our pilot study suggests that construction-based mitigation of translationese is a promising approach. Code and artifacts are available at https://github.com/cisnlp/BayernGPT.
High-Dimensional Interlingual Representations of Large Language Models
Authors: Bryan Wilie; Samuel Cahyawijaya; Junxian He; Pascale Fung
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: Large language models (LLMs) trained on massive multilingual datasets hint at the formation of interlingual constructs–a shared region in the representation space. However, evidence regarding this phenomenon is mixed, leaving it unclear whether these models truly develop unified interlingual representations, or present a partially aligned constructs. We explore 31 diverse languages varying on their resource-levels, typologies, and geographical regions; and find that multilingual LLMs exhibit inconsistent cross-lingual alignments. To address this, we propose an interlingual representation framework identifying both the shared interlingual semantic region and fragmented components, existed due to representational limitations. We introduce Interlingual Local Overlap (ILO) score to quantify interlingual alignment by comparing the local neighborhood structures of high-dimensional representations. We utilize ILO to investigate the impact of single-language fine-tuning on the interlingual alignment in multilingual LLMs. Our results indicate that training exclusively on a single language disrupts the alignment in early layers, while freezing these layers preserves the alignment of interlingual representations, leading to improved cross-lingual generalization. These results validate our framework and metric for evaluating interlingual representation, and further underscore that interlingual alignment is crucial for scalable multilingual learning.
Domain Meets Typology: Predicting Verb-Final Order from Universal Dependencies for Financial and Blockchain NLP
Authors: Zichao Li; Zong Ke
Venue: ACL 2025 (Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP)

Abstract: This paper introduces a domain-adapted approach for verb-order prediction across general and specialized texts (financial/blockchain), combining Universal Dependencies syntax with novel features (AVAR, DLV) and dynamic threshold calibration. We evaluate on 53 languages from UD v2.11, 12K financial sentences (FinBench), and 1,845 blockchain whitepapers (CryptoUD), outperforming four baselines by 6-19% F1. Key findings include: (1) 62% SOV prevalence in SEC filings (+51% over general English), (2) 88% technical whitepaper alignment with Solidity’s SOV patterns, and (3) 9% gains from adaptive thresholds. The system processes 1,150 sentences/second - 2.4× faster than XLM-T - while maintaining higher accuracy, demonstrating that lightweight feature-based methods can surpass neural approaches for domain-specific syntactic analysis.