Morocco

AI-text detectors achieve high accuracy on in-domain benchmarks, but often struggle to generalize across different generation conditions such as unseen prompts, model families, or domains. While prior work has reported these generalization gaps, there are limited insights about the underlying causes. In this work, we present a systematic study aimed at explaining generalization behavior through linguistic analysis. We construct a comprehensive benchmark that spans 6 prompting strategies, 7 large language models (LLMs), and 4 domain datasets, resulting in a diverse set of human- and AI-generated texts. Using this dataset, we fine-tune classification-based detectors on various generation settings and evaluate their cross-prompt, cross-model, and cross-dataset generalization. To explain the performance variance, we compute correlations between generalization accuracies and feature shifts of 80 linguistic features between training and test conditions. Our analysis reveals that generalization performance for specific detectors and evaluation conditions is significantly associated with linguistic features such as tense usage and pronoun frequency.

EACL 2026

Explaining Generalization of AI-Generated Text Detectors Through Linguistic Analysis

ai-text detection

linguistic features

interpretability

generalization

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
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The University of Texas at Austin 
NAACL-2024 General Chair 
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

technical paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

In today’s rapidly evolving large language model (LLM) landscape, technology companies such as Cisco face the difficult challenge
of selecting the most suitable model for downstream tasks that demand deep, domain-specific
product knowledge. Specialized benchmarks not only inform this decision making but also
can be leveraged to rapidly create quizzes that can effectively train engineering and marketing
personnel on novel product offerings in a continually growing Cisco product space.
We present Pro-QuEST, our Prompt-chain based Quiz Engine using state-of-the-art LLMs
for generating multiple-choice questions on Specialized Technical products. In Pro-QuEST,
we first identify key terms and topics from a given professional certification textbook or
product guide, and generate a series of multiple-choice questions using domain-knowledge
guided prompts. We show LLM benchmarking results with the question benchmarks generated by Pro-QuEST using a range of latest
open-source, and proprietary LLMs and compare them with expert-created exams and review 
questions to derive insights on their composition and difficulty. Our experiments indicate that though there is room for improvement
in Pro-QuEST to generate questions of the complexity levels seen in expert-designed 
certification exams, question-type based prompts provide a promising direction to address this limitation. In sample user studies with Cisco personnel, Pro-QuEST was received with high optimism for its practical usefulness in quickly
compiling quizzes for self-assessment on knowledge of novel products in the rapidly changing tech sector.

Pro-QuEST: Prompt-chaining Quiz Engine for testing Specialized Technical Product Knowledge

A detailed understanding of the basic properties of text collections produced by humans or generated synthetically is vital for all steps of the natural language processing system life cycle, from training to evaluating model performance and synthetic texts.
To facilitate the analysis of these properties, we introduce elfen, a Python library for efficient linguistic feature extraction for text datasets. It includes the largest set of item-level linguistic features in eleven feature areas: surface-level, POS, lexical richness, readability, named entity, semantic, information-theoretic, emotion, psycholinguistic, dependency, and morphological features. Building on top of popular NLP and modern dataframe libraries, elfen enables feature extraction in various languages ($80$ at the moment) on thousands of items, even given limited computing resources. We show how using elfen enables linguistically informed data selection, outlier detection, and text collection comparison.
We release elfen as an open-source PyPI package, accompanied by extensive documentation, including tutorials. We host the code at https://github.com/mmmaurer/elfen/, make it available through the GESIS Methods Hub at https://methodshub.gesis.org/library/methods/elfen/, and provide documentation and tutorials at https://elfen.readthedocs.io/en/latest/. A screencast showcasing elfen is available at https://youtu.be/b4pqHWn6UPU.

elfen: A Python Package for Efficient Linguistic Feature Extraction for Natural Language Datasets

Recent advancements in Large Language Models (LLMs) have showcased their proficiency in answering natural language queries. However, their effectiveness is hindered by limited domain-specific knowledge, raising concerns about the reliability of their responses. We introduce a hybrid system that augments LLMs with domain-specific knowledge graphs (KGs), thereby aiming to enhance factual correctness using a KG-based retrieval approach. We focus on a medical KG to demonstrate our methodology, which includes (1) pre-processing, (2) Cypher query generation, (3) Cypher query processing, (4) KG retrieval, and (5) LLM-enhanced response generation. We evaluate our system on a curated dataset of 69 samples, achieving a precision of 78% in retrieving correct KG nodes. Our findings indicate that the hybrid system surpasses a standalone LLM in accuracy and completeness, as verified by an LLM-as-a-Judge evaluation method. This positions the system as a promising tool for applications that demand factual correctness and completeness, such as target identification --- a critical process in pinpointing biological entities for disease treatment or crop enhancement. Moreover, its intuitive search interface and ability to provide accurate responses within seconds make it well-suited for time-sensitive, precision-focused research contexts. We publish the source code together with the dataset and the prompt templates used.

Fact Finder - Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs

We present ClinicalTrialsHub, an interactive search-focused platform that consolidates all data from ClinicalTrials.gov and augments it by automatically extracting and structuring trial-relevant information from PubMed research articles. Our system effectively increases access to structured clinical trial data by 83.8% compared to relying on ClinicalTrials.gov alone, with potential to make access easier for patients, clinicians, researchers, and policymakers, advancing evidence-based medicine. ClinicalTrialsHub uses large language models such as GPT-5.1 and Gemini-3-Pro to enhance accessibility. The platform automatically parses full-text research articles to extract structured trial information, translates user queries into structured database searches, and provides an attributed question-answering system that generates evidence-grounded answers linked to specific source sentences. We demonstrate its utility through a user study involving clinicians, clinical researchers, and PhD students of pharmaceutical sciences and nursing, and a systematic automatic evaluation of its information extraction and question answering capabilities.

ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access

While large language models (LLMs) excel at generating structured data, such as code, their ability to precisely manipulating it based on instructions remains relatively under-explored. Regular expressions (regexes), critical in practice, are challenging to manipulate. Crucially, the correctness of transformations can be mathematically verified, making them exceptionally well-suited for measuring the symbolic reasoning of LLMs. We introduce Query4Regex, a new benchmark for evaluating verifiable transformations on regexes. Our benchmark tests two query formats: natural language instructions and a program-like domain-specific language (DSL) that specifies the sequence of operations. We evaluate a range of LLMs, verifying semantic correctness through rigorous deterministic finite automata (DFA) equivalence testing. Our empirical studies reveal: 1) the formal DSL significantly outperforms natural language, achieving up to 6.74%p average accuracy gains. 2) Performance for both formats degrades sharply as compositional complexity increases, highlighting a core challenge in multi-step reasoning. 3) Models often generate syntactically valid but semantically incorrect regexes, making errors difficult to detect without formal verification. Query4Regex provides a robust framework for analyzing the gap between LLMs' linguistic fluency and their symbolic reasoning, paving the way for more reliable and verifiable manipulation of formal languages.

Query4Regex: Verifiable Regex Transformation through Formal Operations from NL and DSL Queries

Agentic AI systems, which build on Large Language Models (LLMs) and interact with tools and memory, have rapidly advanced in capability and scope. Yet, since LLMs have been shown to struggle in multilingual settings, typically resulting in lower performance and reduced safety, agentic systems risk inheriting these limitations. This raises concerns about the accessibility of such systems, as users interacting in languages other than English may encounter unreliable or security-critical agent behavior. Despite growing interest in evaluating agentic AI, existing benchmarks focus exclusively on English, leaving multilingual settings unexplored. To address this gap, we propose MAPS, a multilingual benchmark suite designed to evaluate agentic AI systems across diverse languages and tasks. MAPS builds on four widely used agentic benchmarks - GAIA (real-world tasks), SWE-bench (code generation), MATH (mathematical reasoning), and the Agent Security Benchmark (security). We translate each dataset into eleven diverse languages, resulting in 805 unique tasks and 9,660 total language-specific instances - enabling a systematic analysis of the multilingual effect on AI agents' performance and robustness. Empirically, we observe a degradation in both performance and security when transitioning from English to other languages, with severity varying by task and correlating with the amount of translated input. Building on these findings, we provide actionable recommendations to guide agentic AI systems development and assessment under multilingual settings. This work establishes the first standardized evaluation framework for multilingual agentic AI, encouraging future research towards equitable, reliable, and accessible agentic AI. The MAPS benchmark suite will become publicly available upon publication.

MAPS: A Multilingual Benchmark for Agent Performance and Security

Large Language Models (LLMs) are now state-of-the-art at summarization, yet the hidden notion of importance that drives their information selections remains hidden. We propose to address this by combining behavioral and computational analyses. Behaviorally, we generate length-controlled summaries and derive empirical importance distributions based on how often each information unit is selected. These reveal that LLMs converge on consistent importance patterns, sharply different from pre-LLM baselines, and that LLMs cluster more by size than by family. Computationally, we probe internal representations and find that middle layers strongly encode importance, and certain attention heads alone align well with empirical importance distributions. Together, these results provide initial insights about what LLMs prioritize in summarization and how this priority is internally represented, opening a path toward interpreting, and ultimately controlling, information selection in LLMs.

What Matters to an LLM? Behavioral and Computational Evidences from Summarization

Large language models (LLMs) achieve state-of-the-art performance in machine translation, but are also known to be sensitive to errors in user prompts. Given these models are largely trained on and respond best to prompts in standard English, this may affect the quality of LLM outputs for second language English speakers as well as real-world lay users, with potentially disproportionate effects on the former. We explore this effect by modeling a range of error types exhibited by such users, motivated by studies of L2 English, and quantifying their impact on LLM performance. We work with two related tasks: machine translation and machine translation evaluation. We find that LLMs-as-MT are brittle to natural spelling errors but not to errors at the phrasal level. However, the variance in quality caused by these errors is lower than the variance over the initial prompt choice, suggesting that ``perfect English'' for a given prompt is less important than choosing a good prompt. Since lay users and L2 speakers may use non-optimal prompts as well as display imperfect language skills, our work calls for increasing the resilience of model performance to both these phenomena to best serve a diverse user base, both from a robustness and fairness perspective.

How Important is ‘Perfect’ English for Machine Translation Prompts?

Large Language Models (LLMs) frequently confabulate scientific facts, but the mechanisms underlying these failures remain poorly understood. We introduce **Reddit False And Correct Texts** (ReFACT), a benchmark of 1,001 expert-annotated question-answer pairs with **span-level error annotations**, enabling fine-grained analysis of confabulation detection, localization, and correction. Evaluating 9 state-of-the-art LLMs reveals two fundamental limitations. First, models exhibit a dominant **salient distractor** failure mode: 61% of incorrect span predictions are semantically unrelated to actual errors, indicating models fixate on contextually prominent terms rather than true error locations. This pattern persists across model scales (1B to 70B), suggesting scaling alone is insufficient to address this limitation. Second, **comparative judgment** (selecting which of two answers contains factual confabulations) proves fundamentally harder than **independent judgment** (classifying a single answer as confabulated or not). For example, even GPT-4o's Fв‚Ѓ score drops from 0.67 to 0.53 when comparing factual versus confabulated answers side-by-side. This dramatic performance degradation challenges the reliability of LLM-as-Judge paradigms widely adopted in current benchmarks. Code and data are released on [anonymized](https://storage.googleapis.com/dataset-refact/refact-dataset.jsonl).

ReFACT: A Benchmark for Scientific Confabulation Detection with Positional Error Annotations

Traditional Information Retrieval (IR) metrics, such as nDCG, MAP, and MRR, assume that human users sequentially examine documents with diminishing attention to lower ranks. This assumption breaks down in Retrieval Augmented Generation (RAG) systems, where search results are consumed by Large Language Models (LLMs), which, unlike humans, process all retrieved documents as a whole rather than sequentially. Additionally, traditional IR metrics do not account for related but irrelevant documents that actively degrade generation quality, rather than merely being ignored. Due to these two major misalignments, namely human vs. machine position discount and human relevance vs. machine utility, classical IR metrics do not accurately predict RAG performance. We introduce a utility-based annotation schema that quantifies both the positive contribution of relevant passages and the negative impact of distracting ones. Building on this foundation, we propose UDCG (Utility and Distraction-aware Cumulative Gain), a metric using an LLM-oriented positional discount to directly optimize the correlation with the end-to-end answer accuracy. Experiments on five datasets and six LLMs demonstrate that UDCG improves correlation by up to 36\% compared to traditional metrics. Our work provides a critical step toward aligning IR evaluation with LLM consumers and enables more reliable assessment of RAG components.

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Pro-QuEST: Prompt-chaining Quiz Engine for testing Specialized Technical Product Knowledge

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Next from EACL 2026

Pro-QuEST: Prompt-chaining Quiz Engine for testing Specialized Technical Product Knowledge

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BUST: Benchmark for the evaluation of detectors of LLM-Generated Text

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