Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)

elfen: A Python Package for Efficient Linguistic Feature Extraction for Natural Language Datasets (2026.eacl-demo)

Challenge:	Specialized benchmarks can be leveraged to create quizzes that can effectively train engineering and marketing personnel on novel product offerings in a continually growing Cisco product space.
Approach:	They propose to generate multiple-choice questions using domain-specific prompts using a set of professional certification textbooks and a range of latest open-source and proprietary LLMs.
Outcome:	The proposed quiz engine generates multiple-choice questions using domain-specific prompts and a range of latest open-source, and proprietary LLMs.

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Maximilian Maurer

Information Extraction & Text Mining

Challenge:	elfen is a Python library for efficient linguistic feature extraction for text datasets.
Approach:	They propose a Python library for efficient linguistic feature extraction for text datasets.
Outcome:	The proposed library enables linguistic feature extraction on thousands of items even on limited computing resources.

DELTA: A Toolkit for Measuring Linguistic Diversity in Dependency-Parsed Corpora (2026.eacl-demo)

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Louis Estève, Kaja Dobrovoljc

Challenge:	Existing tools for measuring diversity of specific linguistic phenomena are limited . we present an open-source framework for measuring linguistic diversity .
Approach:	They propose an open-source framework that integrates dependency tree querying with diversity computation.
Outcome:	The proposed framework can measure diversity across multiple linguistic levels and dimensions.

CLARIESG: An End-to-End System for ESG Analysis over Complex Tables in Corporate Reports (2026.eacl-demo)

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Marta Santacroce, Michele Luca Contalbo, Sara Pederzoli, Riccardo Benassi, Venturelli Valeria, Matteo Paganelli, Francesco Guerra

Low-Resource NLP

Challenge:	Sustainability reports contain rich Environmental, Social and Governance information, but their heterogeneous layouts and complex multi-table structures pose major challenges for LLMs, especially for unit normalization, cross-document reasoning, and precise numerical computation.
Approach:	They propose an end-to-end system that couples robust table extraction with a structured prompting framework for multi-table filtering, normalization, and program-of-thought reasoning.
Outcome:	The proposed system outperforms standard prompting on multi-table benchmarks and provides transparent, auditable reasoning.

Fact Finder - Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs (2026.eacl-demo)

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Daniel Steinigen, Roman Teucher, Timm Heine Ruland, Max Rudat, Nicolas Flores-Herr, Peter Fischer, Nikola Milosevic, Christopher Schymura, Angelo Ziletti

Semantic Text Processing

Challenge:	Recent advances in Large Language Models have demonstrated their proficiency in answering natural language queries.
Approach:	They propose a system that augments Large Language Models with domain-specific knowledge graphs . they evaluate a medical KG and use a KG-based retrieval approach to enhance factual correctness .
Outcome:	The proposed system surpasses a standalone LLM in accuracy and completeness on a medical KG dataset.

Simplifying Outcomes of Language Model Component Analyses with ELIA (2026.eacl-demo)

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Aaron Louis Eidt, Nils Feldhus

IntelliCode: A Multi-Agent LLM Tutoring System with Centralized Learner Modeling (2026.eacl-demo)

Challenge:	ELIA is an interactive web application that simplifies the outputs of various language model component analyses for a broader audience.
Approach:	They propose to use a vision-language model to automatically generate natural language explanations for the complex visualizations produced by these methods.
Outcome:	The proposed system integrates three key techniques and generates natural language explanations for complex visualizations.

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Jones David, Shreya Ghosh

Challenge:	Existing LLM tutors lack persistent representations of learner knowledge . current systems provide inconsistent hints, overlook dependencies between concepts .
Approach:	They propose a multi-agent LLM tutoring system that integrates mastery estimates, misconceptions, review schedules, and engagement signals.
Outcome:	The proposed system integrates skill assessment, learner profiling, graduated hinting, curriculum selection, spaced repetition, and engagement monitoring over a shared state under a single-writer policy.

FiMMIA: scaling semantic perturbation-based membership inference across modalities (2026.eacl-demo)

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Anton Emelyanov, Sergei Kudriashov, Alena Fenogenova

Robustness in NLP

Challenge:	Membership Inference attacks aim to determine whether a specific data point was included in the training set of a target model.
Approach:	They propose to train a neural network to analyze the target model’s behavior on perturbed inputs, capturing interactions between semantic domains and loss values on members and non-members in the local neighborhood of each sample.
Outcome:	The proposed methods can detect distribution shifts in existing datasets and release a baseline pipeline to detect them.

A Browser-based Open Source Assistant for Multimodal Content Verification (2026.eacl-demo)

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Rosanna Milner, Michael Foster, Twin Karmakharm, Olesya Razuvayevskaya, Valentin Porcellini, Denis Teyssou, Ian Roberts, Kalina Bontcheva

Multimodality

Challenge:	Disinformation and advanced generative AI content pose a significant challenge for journalists and fact-checkers who must rapidly verify digital media.
Approach:	They propose to integrate a browser-based tool that automatically extracts content from a suite of backend NLP classifiers and presents actionable credibility signals and AI-generation likelihood in an easy-to-digest format.
Outcome:	The Verification Assistant is a browser-based tool that extracts content and routes it to a suite of backend NLP classifiers, presenting actionable credibility signals, AI-generation likelihood, and other verification advice in an easy-to-digest format.

Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes (2026.eacl-demo)

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Johann Frei, Nils Feldhus, Lisa Raithel, Roland Roller, Alexander Meyer, Frank Kramer

Challenge:	HL7 FHIR format is a desirable format for clinical data integration and healthcare services.
Approach:	They propose an end-to-end framework that adheres to the HL7 FHIR document schema . it uses LLM agents, code execution, and healthcare terminology database tools .
Outcome:	The proposed framework adheres to the HL7 FHIR document schema and competes well with a human baseline in predicting FHIr resources from unstructured text.

BOOM: Beyond Only One Modality KIT’s Multimodal Multilingual Lecture Companion (2026.eacl-demo)

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Sai Koneru, Fabian Retkowski, Christian Huber, Lukas Hilgert, Seymanur Akti, Enes Yavuz Ugan, Alexander Waibel, Jan Niehues

Multimodality

Challenge:	a multimodal multilingual lecture companion is needed to preserve lecture content in its entirety . globalization of education and rapid growth of online learning have made localizing educational content a challenge .
Approach:	They propose a multimodal multilingual lecture companion that translates lecture audio and slides to produce synchronized outputs across three modalities.
Outcome:	The proposed solution preserves the original content in its entirety while preserving translations across three modalities.

PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models (2026.eacl-demo)

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Robert Belanec, Ivan Srba, Maria Bielikova

Semantic Text Processing

Challenge:	Parameter-Efficient Fine-Tuning (PEFT) methods address the increasing size of Large Language Models (LLMs).
Approach:	They propose a framework for efficient fine-tuning Large Language Models (LLMs) they aim to train only a small percentage of the full model's parameters .
Outcome:	Xu et al., 2023; Ding e t al, 2024; Lialin e al. 2023) show that using PEFT methods can improve performance.

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Juri Opitz, Andrianos Michail, Lucas Moeller, Sebastian Padó, Simon Clematide

AlignFix: A Tool for Parallel Corpora Augmentation and Refinement (2026.eacl-demo)

Challenge:	XPLAINSIM is a Python package that explains textual similarity in an easy-to-use way.
Approach:	They propose a Python package that unifies three approaches to explain text similarity . they demonstrate the value of the package through intuitive examples and empirical research .
Outcome:	XPLAINSIM is a Python package that unifies three approaches to explain text similarity . the authors show that the package is useful for explaining text similarities in a simple way .

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Samuel Frontull, Simon Haller-Seeber

Machine Translation

Challenge:	High-quality datasets are crucial for training effective state of the art machine translation systems, but they can be noisy and degrade performance.
Approach:	They propose an open-source tool for augmenting data, identifying and correcting errors in parallel corpora.
Outcome:	The tool extracts consistent phrase pairs, enabling targeted replacements that can improve the dataset quality.

PromptLab: A Collaborative Platform for Prompt Engineering and Dataset Curation (2026.eacl-demo)

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Maged S. Al-shaibani, Zaid Alyafeai, Dania Refai, Nawaf Alomari, Ahmed Ashraf, Mais Alheraki, Mustafa Alturki, Hamzah Luqman, Irfan Ahmad

LLM BiasScope: A Real-Time Bias Analysis Platform for Comparative LLM Evaluation (2026.eacl-demo)

Challenge:	PromptLab is a web-based prompt engineering platform for collaborative prompt development across diverse natural language processing tasks and datasets.
Approach:	They propose to integrate prompt generation via OpenRouter and provide real-time validation with multiple Large Language Models.
Outcome:	The platform addresses primary challenges in prompt development, including template creation, collaborative review, and quality assurance through a comprehensive workflow that supports both individual researchers and team-based projects.

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Himel Ghosh, Nick Elias Werner

InkSight: Towards AI-Aided Historical Manuscript Analysis (2026.eacl-demo)

Challenge:	Existing work on bias evaluation includes benchmark datasets and automated detection methods.
Approach:	They propose an open-source web application for side-by-side comparison of LLM outputs with real-time bias analysis.
Outcome:	The open-source application compares LLM outputs with real-time bias analysis.

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Andrey Sakhovskiy, Ivan Ulitin, Emilia Bojarskaja, Vladimir Kokh, Ruslan Murtazin, Maxim Novopoltsev, Semen Budennyy

promptolution: A Unified, Modular Framework for Prompt Optimization (2026.eacl-demo)

Challenge:	Large-scale scientific research on medieval Arabic manuscripts remains challenging due to the need for advanced paleographic and linguistic training and the lack of assisting software.
Approach:	They propose an end-to-end Arabic manuscript analysis tool for manuscript-based analytics and research hypothesis testing.
Outcome:	The proposed tool overcomes the limitations of existing tools and can be used in large-scale scientific research.

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Tom Zehle, Timo Heiß, Moritz Schlager, Matthias Aßenmacher, Matthias Feurer

Semantic Text Processing

Challenge:	Existing implementations of prompt optimization are tied to unmaintained, isolated codebases or require invasive integration into application frameworks.
Approach:	They propose a unified, modular open-source framework that integrates multiple contemporary discrete prompt optimizers within a single extensible system for both practitioners and researchers.
Outcome:	The proposed framework integrates multiple discrete prompt optimizers, supports systematic and reproducible benchmarking, and returns framework-agnostic prompt strings, enabling seamless integration into existing LLM pipelines while remaining agnosite to the underlying model implementation.

T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground (2026.eacl-demo)

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Dmitrii Stoianov, Danil Taranets, Olga Tsymboi, Ramil Latypov, Almaz Dautov, Vladislav Kruglikov, Surkov Nikita, German Abramov, Pavel Gein, Dmitry Abulkhanov, Mikhail Gashkov, Viktor Zelenkovskiy, Artem Batalov, Aleksandr Medvedev, Anatolii Potapov

Reasoning

Challenge:	Recent foundation models show that reasoningoriented training and improved decoding methods can substantially boost both accuracy and speed.
Approach:	They propose an open-weight Russian LLM for hybrid reasoning and efficient inference.
Outcome:	The proposed model supports direct answering and reasoning-trace generation . the model and inference pipeline can be extended or modified to suit Russian-language reasoning .

SDialog: A Python Toolkit for End-to-End Agent Building, User Simulation, Dialog Generation, and Evaluation (2026.eacl-demo)

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Sergio Burdisso, Séverin Baroudi, Yanis Labrak, David Grünert, Pawel Cyrta, Yiyang Chen, Srikanth Madikeri, Esaú Villatoro-tello, Ricard Marxer, Petr Motlicek

Dialogue Systems & Conversational Agents

Challenge:	SDialog is an open-source Python toolkit for end-to-end development, simulation, evaluation and analysis of LLM-based conversational agents.
Approach:	They present an open-source Python toolkit for end-to-end development, simulation, evaluation and analysis of LLM-based conversational agents.
Outcome:	SDialog enables more controlled, transparent, and systematic research on conversational systems.

Agentic AI for Human Resources: LLM-Driven Candidate Assessment (2026.eacl-demo)

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Kamer Ali Yuksel, Abdul Basit Anees, Ashraf Hatim Elneima, Sanjika Hewavitharana, Mohamed Al-Badrashiny, Hassan Sawaf

Trove: A Flexible Toolkit for Dense Retrieval (2026.eacl-demo)

Challenge:	Current systems rely on keyword matching and shallow keyword-based screening, leading to missed opportunities and inconsistent evaluations.
Approach:	They propose a framework that uses Large Language Models to automate candidate assessment in recruitment.
Outcome:	The proposed framework outputs detailed assessment reports, candidate comparisons, and ranked recommendations that are transparent, auditable, and suitable for real-world hiring workflows.

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Reza Esfandiarpoor, Max Zuo, Stephen Bach

Information Retrieval

Challenge:	Existing retrieval tools require considerable engineering effort for many tasks like efficient data management or model customization.
Approach:	They propose a novel open-source retrieval toolkit that simplifies research experiments without sacrificing flexibility or speed.
Outcome:	The proposed tool reduces memory consumption by 2.6 and allows for arbitrary customizations.

ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access (2026.eacl-demo)

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Jiwoo Park, Ruoqi Liu, Avani Jagdale, Andrew Srisuwananukorn, Jing Zhao, Lang Li, Ping Zhang, Sachin Kumar

Question Answering

Challenge:	ClinicalTrialsHub consolidates clinical trial data from ClinicalTrial.gov and augments it by extracting and structuring trial-relevant information from PubMed.
Approach:	They propose a search-focused platform that consolidates PubMed data and extracts structured trial information.
Outcome:	ClinicalTrialsHub increases access to structured clinical trial data by 83.8% compared to ClinicalTrial.gov alone.

SciTrue: Evidence-Grounded Claim Verification in Science (2026.eacl-demo)

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Neset Tan, Minghao Li, Mark Gahegan

PUCP-Metrix: An Open-source and Comprehensive Toolkit for Linguistic Analysis of Spanish Texts (2026.eacl-demo)

Challenge:	Existing systems often exhibit unverifiable attributions, shallow evidence mapping, and hallucinated citations.
Approach:	They propose a claim verification system that provides source-level accountability and evidence traceability.
Outcome:	SciTrue outperforms RAG-based baselines in summary traceability, attribution accuracy, and context alignment in a human evaluation of 300 attributions.

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Javier Alonso Villegas Luis, Marco Antonio Sobrevilla Cabezudo

Integrity Shield A System for Ethical AI Use & Authorship Transparency in Assessments (2026.eacl-demo)

Challenge:	Existing tools for linguistic analysis of Spanish texts lack linguistic features for interpretability and tasks that involve style, structure, and readability.
Approach:	They propose to use PUCP-Metrix to analyze Spanish texts in a language repository.
Outcome:	The proposed toolkit performs better on automated readability assessments and machine-generated text detection tasks than existing repositories and strong neural baselines.

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Ashish Raj Shekhar, Shiven Agarwal, Priyanuj Bordoloi, Yash Shah, Tejas Anvekar, Vivek Gupta

Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SciLire System (2026.eacl-demo)

Challenge:	Existing methods for detecting authorship and short answers are ineffective when students query proprietary black-box systems using instructor-provided documents.
Approach:	They propose a document-layer watermarking system that embeds schema-aware, item-level watermarks into assessment PDFs while keeping their human-visible appearance unchanged.
Outcome:	The proposed system achieves high prevention and detection reliability across 30 question papers spanning STEM, humanities, and medical reasoning.

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Necva Bölücü, Jessica Irons, Changhyun Lee, Brian Jin, Maciej Rybinski, Huichen Yang, Andreas Duenser, Stephen Wan

Information Extraction & Text Mining

Challenge:	rapid growth of scientific literature has made manual extraction of structured knowledge increasingly impractical.
Approach:	They propose a system for creating datasets from scientific literature that integrates human-AI teaming principles and iterative workflows.
Outcome:	The proposed system improves extraction fidelity and facilitates efficient dataset creation.

xLM: A Python Package for Non-Autoregressive Language Models (2026.eacl-demo)

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Dhruvesh Patel, Durga Prasad Maram, Sai Sreenivas Chintha, Benjamin Rozonoyer, Andrew McCallum

AITutor-EvalKit: Exploring the Capabilities of AI Tutors (2026.eacl-demo)

Challenge:	Autoregressive language models generate text sequentially from left to right by adding one token at a time.
Approach:	They propose a python package that provides a suite of small non-autoregressive language models that can be used by researchers.
Outcome:	The proposed package makes implementing small non-autoregressive language models faster and provides a suite of pre-trained models that can be used by the research community.

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Numaan Naeem, Kaushal Kumar Maurya, Kseniia Petukhova, Ekaterina Kochmar

Challenge:	Personalized one-on-one tutoring is an effective educational approach, yet its widespread adoption is constrained by the limited availability of qualified tutors and the high costs associated with tutor training.
Approach:	They propose an evaluation tool that uses language technology to evaluate the pedagogical quality of AI tutors.
Outcome:	The proposed evaluation tool is aimed at education stakeholders as well as the *ACL community at large, as it supports learning and can also collect user feedback and annotation.

EvalSense: A Framework for Domain-Specific LLM (Meta-)Evaluation (2026.eacl-demo)

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Adam Dejl, Jonathan Pearson

Challenge:	EvalSense is a flexible framework for constructing domain-specific evaluation suites for large language models . it provides out-of-the-box support for a broad range of model providers and evaluation strategies .
Approach:	They propose a framework for constructing domain-specific evaluation suites for large language models.
Outcome:	The proposed framework provides out-of-the-box support for a broad range of model providers and evaluation strategies.

AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments (2026.eacl-demo)

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Ario Saeid Vaghefi, Aymane Hachcham, Veronica Grasso, Nakiete Msemo, Chiara Colesanti Senni, Markus Leippold

Information Retrieval

Challenge:	EWS financial flows are opaque and lack standardized labels, structures, and terminology for EWS-related spending.
Approach:	They propose an agent-based Retrieval-Augmented Generation system that uses hybrid retrieval and internal chain-of-thought reasoning to extract relevant financial data and classify EWS investments.
Outcome:	The proposed system outperforms four alternatives on multi-label classification and budget allocation on an annotated CREWS Fund corpus.

RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation (2026.eacl-demo)

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Keerthana Murugaraj, Salima Lamsiyah, Martin Theobald

SmartMatch: Real-Time Semantic Retrieval for Translation Memory Systems (2026.eacl-demo)

Challenge:	Existing tools for evaluating RAG systems often collapse heterogeneous behaviors into single scores.
Approach:	They propose a diagnostic framework for automated, reference-free evaluation of RAG pipelines.
Outcome:	The proposed framework decomposes RAG behavior into retrieval quality,answer relevance and completeness, strictclaim-level faithfulness, and judge calibration.

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Ernesto L. Estevanell-Valladares, Salima Lamsiyah, Alicia Picazo-Izquierdo, Tharindu Ranasinghe, Ruslan Mitkov, Rafael Muñoz

QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models (2026.eacl-demo)

Challenge:	Translation Memory (TM) systems are core components of computer-aided translation tools . however, they fail to retrieve semantically relevant content when surface similarity is low.
Approach:	They propose an open-source demo and evaluation toolkit for TM retrieval that connects modern sentence encoders and strong lexical/fuzzy baselines with a vector database.
Outcome:	The proposed toolkit exposes the end-to-end retrieval pipeline through a web-based UI for qualitative inspection and preference logging.

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Maximilian Kreutner, Jens Rupprecht, Georg Ahnert, Ahmed Salem, Markus Strohmaier