Austria

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.

ACL 2025

Enhancing Software Requirements Engineering with Language Models and Prompting Techniques: Insights from the Current Research and Future Directions

large language model

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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 \textit{``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.

Question Decomposition for Retrieval-Augmented Generation

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.

Spanish Dialect Classification: A Comparative Study of Linguistically Tailored Features, Unigrams and BERT Embeddings

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.

MA-COIR: Leveraging Semantic Search Index and Generative Models for Ontology-Driven Biomedical Concept Recognition

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.

LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries

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.

Interactive Text Games: Lookahead Is All You Need!

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

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

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

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

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

Cross-Genre Learning for Old English Poetry POS Tagging

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

A Computational Framework to Identify Self-Aspects in Text

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

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