GenAI4ELab/papercli-papers huggingface dataset:
Towards Multi-Perspective NLP Systems: A Thesis Proposal
Authors: Benedetta Muscato
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: In the field of Natural Language Processing (NLP), a common approach for resolving human disagreement involves establishing a consensus among multiple annotators. However, previous research shows that overlooking individual opinions can result in the marginalization of minority perspectives, particularly in subjective tasks, where annotators may systematically disagree due to their personal preferences. Emerging Multi-Perspective approaches challenge traditional methodologies that treat disagreement as mere noise, instead recognizing it as a valuable source of knowledge shaped by annotators’ diverse backgrounds, life experiences, and values.This thesis proposal aims to (1) identify the challenges of designing disaggregated datasets i.e., preserving individual labels in human-annotated datasets for subjective tasks (2) propose solutions for developing Perspective-Aware by design systems and (3) explore the correlation between human disagreement and model uncertainty leveraging eXplainable AI techniques (XAI).Our long-term goal is to create a framework adaptable to various subjective NLP tasks to promote the development of more responsible and inclusive models.
Enhancing Software Requirements Engineering with Language Models and Prompting Techniques: Insights from the Current Research and Future Directions
Authors: Moemen Ebrahim; Shawkat Guirguis; Christine Basta
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) offer transformative potential for Software Requirements Engineering (SRE), yet critical challenges, including domain ignorance, hallucinations, and high computational costs, hinder their adoption. This paper proposes a conceptual framework that integrates Small Language Models (SLMs) and Knowledge-Augmented LMs (KALMs) with LangChain to address these limitations systematically. Our approach combines: (1) SLMs for efficient, locally deployable requirements processing, (2) KALMs enhanced with Retrieval-Augmented Generation (RAG) to mitigate domain-specific gaps, and (3) LangChain for structured, secure workflow orchestration. We identify and categorize six technical challenges and two research gaps through a systematic review of LLM applications in SRE. To guide practitioners, we distill evidence-based prompt engineering guidelines (Context, Language, Examples, Keywords) and propose prompting strategies (e.g., Chain-of-Verification) to improve output reliability. The paper establishes a theoretical foundation for scalable, trustworthy AI-assisted SRE and outlines future directions, including domain-specific prompt templates and hybrid validation pipelines.
Question Decomposition for Retrieval-Augmented Generation
Authors: Paul J. L. Ammann; Jonas Golde; Alan Akbik
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Grounding large language models (LLMs) in verifiable external sources is a well-established strategy for generating reliable answers. Retrieval-augmented generation (RAG) is one such approach, particularly effective for tasks like question answering: it retrieves passages that are semantically related to the question and then conditions the model on this evidence. However, multi-hop questions, such as “Which company among NVIDIA, Apple, and Google made the biggest profit in 2023?,” challenge RAG because relevant facts are often distributed across multiple documents rather than co-occurring in one source, making it difficult for standard RAG to retrieve sufficient information. To address this, we propose a RAG pipeline that incorporates question decomposition: (i) an LLM decomposes the original query into sub-questions, (ii) passages are retrieved for each sub-question, and (iii) the merged candidate pool is reranked to improve the coverage and precision of the retrieved evidence. We show that question decomposition effectively assembles complementary documents, while reranking reduces noise and promotes the most relevant passages before answer generation. We evaluate our approach on the MultiHop-RAG and HotpotQA, showing gains in retrieval (MRR@10: +36.7%) and answer accuracy (F1: +11.6%) over standard RAG baselines. The pipeline is a practical, drop-in enhancement requiring no task-specific training or specialized indexing.
Neural Machine Translation for Agglutinative Languages via Data Rejuvenation
Authors: Chen Zhao; Yatu Ji; Ren Qing-Dao-Er-Ji; Nier Wu; Lei Shi; Fu Liu; Yepai Jia
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: In Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora. Within the context of China’s Belt and Road Initiative, there is increasing demand for improving translation quality from agglutinative languages (e.g., Mongolian, Arabic) to Chinese. However, the translation scenarios for agglutinative languages (which form words by concatenating morphemes with clear boundaries) face significant challenges including data sparsity, quality imbalance, and inactive sample proliferation due to their morphological complexity and syntactic flexibility. This study presents a systematic analysis of data distribution characteristics in agglutinative languages and proposes a dual-module framework combining fine-grained inactive sample identification with target-side rejuvenation. Our framework first establishes a multi-dimensional evaluation system to accurately identify samples exhibiting low-frequency morphological interference or long-range word order mismatches. Subsequently, the target-side rejuvenation mechanism generates diversified noise-resistant translations through iterative optimization of sample contribution weights. Experimental results on four low-resource agglutinative language tasks demonstrate significant performance improvements (BLEU +2.1–3.4) across mainstream NMT architectures. Architecture-agnostic validation further confirms the framework’s generalizability.
StRuCom: A Novel Dataset of Structured Code Comments in Russian
Authors: Maria Dziuba; Valentin Malykh
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Structured code comments in docstring format are essential for code comprehension and maintenance, but existing machine learning models for their generation perform poorly for Russian compared to English. To bridge this gap, we present StRuCom — the first large-scale dataset (153K examples) specifically designed for Russian code documentation. Unlike machine-translated English datasets that distort terminology (e.g., technical loanwords vs. literal translations) and docstring structures, StRuCom combines human-written comments from Russian GitHub repositories with synthetically generated ones, ensuring compliance with Python, Java, JavaScript, C#, and Go standards through automated validation.
A Semantic Uncertainty Sampling Strategy for Back-Translation in Low-Resources Neural Machine Translation
Authors: Yepai Jia; Yatu Ji; Xiang Xue; Lei Shi; Qing-Dao-Er-Ji Ren; Nier Wu; Na Liu; Chen Zhao; Fu Liu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Back-translation has been proven effective in enhancing the performance of Neural Machine Translation (NMT), with its core mechanism relying on synthesizing parallel corpora to strengthen model training. However, while traditional back-translation methods alleviate the data scarcity in low-resource machine translation, their dependence on random sampling strategies ignores the semantic quality of monolingual data. This results in the contamination of model training through the inclusion of substantial low-quality samples in the generated corpora. To mitigate noise interference, additional training iterations or model scaling are required, significantly increasing computational costs. To address this challenge, this study proposes a Semantic Uncertainty Sampling strategy, which prioritizes sentences with higher semantic uncertainty as training samples by computationally evaluating the complexity of unannotated monolingual data. Experiments were conducted on three typical low-resource agglutinative language pairs: Mongolian-Chinese, Uyghur-Chinese, and Korean-Chinese. Results demonstrate an average BLEU score improvement of +1.7 on test sets across all three translation tasks, confirming the method’s effectiveness in enhancing translation accuracy and fluency. This approach provides a novel pathway for the efficient utilization of unannotated data in low-resource language scenarios.
Spanish Dialect Classification: A Comparative Study of Linguistically Tailored Features, Unigrams and BERT Embeddings
Authors: Laura Zeidler; Chris Jenkins; Filip Miletić; Sabine Schulte Im Walde
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: The task of automatic dialect classification is typically tackled using traditional machine-learning models with bag-of-words unigram features. We explore two alternative methods for distinguishing dialects across 20 Spanish-speaking countries:(i) Support vector machine and decision tree models were trained on dialectal features tailored to the Spanish dialects, combined with standard unigrams. (ii) A pre-trained BERT model was fine-tuned on the task.Results show that the tailored features generally did not have a positive impact on traditional model performance, but provide a salient way of representing dialects in a content-agnostic manner. The BERT model wins over traditional models but with only a tiny margin, while sacrificing explainability and interpretability.
SequentialBreak: Large Language Models Can be Fooled by Embedding Jailbreak Prompts into Sequential Prompt Chains
Authors: Bijoy Ahmed Saiem; MD Sadik Hossain Shanto; Rakib Ahsan; Md Rafi Ur Rashid
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: As the use of Large Language Models (LLMs) expands, so do concerns about their vulnerability to jailbreak attacks. We introduce SequentialBreak, a novel single-query jailbreak technique that arranges multiple benign prompts in sequence with a hidden malicious instruction among them to bypass safety mechanisms. Sequential prompt chains in a single query can lead LLMs to focus on certain prompts while ignoring others. By embedding a malicious prompt within a prompt chain, we show that LLMs tend to ignore the harmful context and respond to all prompts including the harmful one. We demonstrate the effectiveness of our attack across diverse scenarios—including Q&A systems, dialogue completion tasks, and levelwise gaming scenario—highlighting its adaptability to varied prompt structures. The variability of prompt structures shows that SequentialBreak is adaptable to formats beyond those discussed here. Experiments show that SequentialBreak only uses a single query to significantly outperform existing baselines on both open-source and closed-source models. These findings underline the urgent need for more robust defenses against prompt-based attacks. The Results and website are available on https://anonymous.4open.science/r/JailBreakAttack-4F3B/.
A Dual-Layered Evaluation of Geopolitical and Cultural Bias in LLMs
Authors: Sean Kim; Hyuhng Joon Kim
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) are increasingly deployed across diverse linguistic and cultural contexts, understanding their behavior in both factual and disputable scenarios is essential—especially when their outputs may shape public opinion or reinforce dominant narratives. In this paper, we define two types of bias in LLMs: model bias (bias stemming from model training) and inference bias (bias induced by the language of the query), through a two-phase evaluation.Phase 1 evaluates LLMs on factual questions where a single verifiable answer exists, assessing whether models maintain consistency across different query languages. Phase 2 expands the scope by probing geopolitically sensitive disputes, where responses may reflect culturally embedded or ideologically aligned perspectives. We construct a manually curated dataset spanning both factual and disputable QA, across four languages and question types. The results show that Phase 1 exhibits query language-induced alignment, while Phase 2 reflects an interplay between the model’s training context and query language. This paper offers a structured framework for evaluating LLM behavior across neutral and sensitive topics, providing insights for future LLM deployment and culturally-aware evaluation practices in multilingual contexts.WARNING: this paper covers East Asian issues which may be politically sensitive.
MA-COIR: Leveraging Semantic Search Index and Generative Models for Ontology-Driven Biomedical Concept Recognition
Authors: Shanshan Liu; Noriki Nishida; Rumana Ferdous Munne; Narumi Tokunaga; Yuki Yamagata; Kouji Kozaki; Yuji Matsumoto
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Recognizing biomedical concepts in the text is vital for ontology refinement, knowledge graph construction, and concept relationship discovery. However, traditional concept recognition methods, relying on explicit mention identification, often fail to capture complex concepts not explicitly stated in the text. To overcome this limitation, we introduce MA-COIR, a framework that reformulates concept recognition as an indexing-recognition task. By assigning semantic search indexes (ssIDs) to concepts, MA-COIR resolves ambiguities in ontology entries and enhances recognition efficiency. Using a pretrained BART-based model fine-tuned on small datasets, our approach reduces computational requirements to facilitate adoption by domain experts. Furthermore, we incorporate large language model (LLM)-generated queries and synthetic data to improve recognition in low-resource settings. Experimental results on three scenarios (CDR, HPO, and HOIP) highlight the effectiveness of MA-COIR in recognizing both explicit and implicit concepts without the need for mention-level annotations during inference, advancing ontology-driven concept recognition in biomedical domain applications. Our code and constructed data are available at https://github.com/sl-633/macoir-master.
LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries
Authors: Zekun Wu; Seonglae Cho; Umar Mohammed; Cristian Enrique Munoz Villalobos; Kleyton Da Costa; Xin Guan; Theo King; Ze Wang; Emre Kazim; Adriano Koshiyama
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Open-source AI libraries are foundational to modern AI systems, yet they present significant, underexamined risks spanning security, licensing, maintenance, supply chain integrity, and regulatory compliance. We introduce LibVulnWatch, a system that leverages recent advances in large language models and agentic workflows to perform deep, evidence-based evaluations of these libraries. Built on a graph-based orchestration of specialized agents, the framework extracts, verifies, and quantifies risk using information from repositories, documentation, and vulnerability databases. LibVulnWatch produces reproducible, governance-aligned scores across five critical domains, publishing results to a public leaderboard for ongoing ecosystem monitoring. Applied to 20 widely used libraries—including ML frameworks, LLM inference engines, and agent orchestration tools—our approach covers up to 88% of OpenSSF Scorecard checks while surfacing up to 19 additional risks per library, such as critical RCE vulnerabilities, missing SBOMs, and regulatory gaps. By integrating advanced language technologies with the practical demands of software risk assessment, this work demonstrates a scalable, transparent mechanism for continuous supply chain evaluation and informed library selection.
Interactive Text Games: Lookahead Is All You Need!
Authors: Hosein Rezaei; James Alfred Walker; Frank Soboczenski
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: 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.
Evaluating Credibility and Political Bias in LLMs for News Outlets in Bangladesh
Authors: Tabia Tanzin Prama; Md. Saiful Islam
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 widelyused in search engines to provide direct an-swers, while AI chatbots retrieve updated infor-mation from the web. As these systems influ-ence how billions access information, evaluat-ing the credibility of news outlets has becomecrucial. We audit nine LLMs from OpenAI,Google, and Meta to assess their ability to eval-uate the credibility and political bias of the top20 most popular news outlets in Bangladesh.While most LLMs rate the tested outlets, largermodels often refuse to rate sources due to in-sufficient information, while smaller modelsare more prone to hallucinations. We create adataset of credibility ratings and political iden-tities based on journalism experts’ opinions andcompare these with LLM responses. We findstrong internal consistency in LLM credibil-ity ratings, with an average correlation coeffi-cient (ρ) of 0.72, but moderate alignment withexpert evaluations, with an average ρ of 0.45.Most LLMs (GPT-4, GPT-4o-mini, Llama 3.3,Llama-3.1-70B, Llama 3.1 8B, and Gemini 1.5Pro) in their default configurations favor theleft-leaning Bangladesh Awami League, givinghigher credibility ratings, and show misalign-ment with human experts. These findings high-light the significant role of LLMs in shapingnews and political information
The Evolution of Gen Alpha Slang: Linguistic Patterns and AI Translation Challenges
Authors: Ishita; Radhika Mamidi
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: 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.
Light-Weight Hallucination Detection using Contrastive Learning for Conditional Text Generation
Authors: Miyu Yamada; Yuki Arase
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: 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-4o prompting. Remarkably, our method shows higher performance for outputs where hallucinated spans are sparse.
Fact from Fiction: Finding Serialized Novels in Newspapers
Authors: Pascale Feldkamp; Alie Lassche; Katrine Frøkjær Baunvig; Kristoffer Nielbo; Yuri Bizzoni
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop))

Abstract: Digitized literary corpora of the 19th century favor canonical and novelistic forms, sidelining a broader and more diverse literary production. Serialized fiction – widely read but embedded in newspapers – remains especially underexplored, particularly in low-resource languages like Danish. This paper addresses this gap by developing methods to identify fiction in digitized Danish newspapers (1818–1848).We (1) introduce a manually annotated dataset of 1,394 articles and (2) evaluate classification pipelines using both selected linguistic features and embeddings, achieving F1-scores of up to 0.91. Finally, we (3) analyze feuilleton fiction via interpretable features to test its drift in discourse from neighboring nonfiction.Our results support the construction of alternative literary corpora and contribute to ongoing work on modeling the fiction–nonfiction boundary by operationalizing discourse-level distinctions at scale.