Morocco

Extremist groups develop complex in-group language, also referred to as cryptolects, to exclude or mislead outsiders. We investigate the ability of current language technologies to detect and interpret the cryptolects of two online extremist platforms. Evaluating eight models across six tasks, our results indicate that general purpose LLMs cannot consistently detect or decode extremist language. However, performance can be significantly improved by domain adaptation and specialised prompting techniques. These results provide important insights to inform the development and deployment of automated moderation technologies. We further develop and release novel labelled and unlabelled datasets, including 19.4M posts from extremist platforms and lexicons validated by human experts.

EACL 2026

IYKYK: Using language models to decode extremist cryptolects

cryptolects

radicalisation

online abuse

social media

dataset

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
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NAACL 2024

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

technical paper

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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. 

We present AutoBool, a reinforcement learning (RL) framework that trains large language models (LLMs) to generate effective Boolean queries for medical systematic reviews. Boolean queries are the primary mechanism for literature retrieval in this domain and must achieve high recall while maintaining reasonable precision---a challenging balance that existing prompt-based LLM approaches often struggle to achieve. A major limitation in this space is the lack of ground-truth best Boolean queries for each topic, which makes supervised fine-tuning impractical. AutoBool addresses this challenge by leveraging RL to directly optimize query generation against retrieval performance metrics, without requiring ideal target queries. To support this effort, we create and release the largest dataset of its kind: 65 588 topics in total for training and evaluating the task of automatic Boolean query formulation. Experiments on our new dataset and two established datasets (CLEF TAR and Seed Collection) show that AutoBool significantly outperforms zero-shot/few-shot prompting and matches or exceeds the effectiveness of much larger GPT-based models (e.g., GPT-4o, O3) using smaller backbones. It also approaches effectiveness of expert-authored queries while retrieving 10вЂ“16 times fewer documents. Ablation studies reveal the critical roles of model backbone, size, decoding temperature, and prompt design. Code and data are available at https://anonymous.4open.science/r/AutoBool-B3E5/.

AutoBool: Reinforcement-Learned LLM for Effective Automatic Systematic Reviews Boolean Query Generation

Understanding narratives requires identifying which events are most salient for a story's progression. We present a contrastive learning framework for modeling narrative salience that learns story embeddings from narrative twins: stories that share the same plot but differ in surface form. Our model is trained to distinguish a story from both its narrative twin and a distractor with similar surface features but different plot. Using the resulting embeddings, we evaluate four narratologically motivated operations for inferring salience (deletion, shifting, disruption, and summarization). Experiments on short narratives (ROCStories) and longer Wikipedia plot summaries show that contrastively learned story embeddings outperform a masked-language-model baseline, and that summarization is the most reliable operation for identifying salient sentences. If retellings are not available, random dropout can be used to generate narrative twins from a single story. Effective distractors can be obtained either by prompting LLMs or, in long-form narratives, by using different parts of the same story.

Contrastive Learning with Narrative Twins for Modeling Story Salience

The intentional creation and spread of disinformation poses a significant threat to public discourse. However, existing English datasets and research rarely address the intentionality behind the disinformation. This work presents MALINT, the first human-annotated English corpus developed in collaboration with expert fact-checkers to capture disinformation and its malicious intent. We utilize our novel corpus to benchmark 12 language models, including small language models (SLMs) such as BERT and large language models (LLMs) like Llama 3.3, on binary and multilabel intent classification tasks. Moreover, inspired by inoculation theory from psychology and communication studies, we investigate whether incorporating knowledge of malicious intent can improve disinformation detection. To this end, we propose intent-based inoculation, an intentвЂ‘augmented reasoning for LLMs that integrates intent analysis to mitigate the persuasive impact of disinformation. Analysis on six disinformation datasets, five LLMs, and seven languages shows that intentвЂ‘augmented reasoning improves zeroвЂ‘shot disinformation detection. To support research in intentвЂ‘aware disinformation detection, we release the MALINT dataset with annotations from each annotation step.

MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection

Quality estimation is omnipresent in machine translation, for both evaluation and generation. Unfortunately, quality estimation models are often opaque and computationally expensive, making them impractical to be part of large-scale pipelines. In this work, we tackle two connected challenges: (1) reducing the cost of quality estimation at scale, and (2) developing an inexpensive uncertainty estimation method for quality estimation. To address the latter, we introduce Instant Confidence COMET, an uncertainty-aware quality estimation model that matches the performance of previous approaches at a fraction of their costs. We extend this to Early-Exit COMET, a quality estimation model that can compute quality scores and associated confidences already at early model layers, allowing us to early-exit computations and reduce evaluation costs. We also apply our model to machine translation reranking. We combine Early-Exit COMET with an upper confidence bound bandit algorithm to find the best candidate from a large pool without having to run the full evaluation model on all candidates. In both cases (evaluation and reranking) our methods reduce the required compute by 50% with very little degradation in performance. Finally, we show how Instant Confidence COMET can be used to decide which translations a human evaluator should score rather than relying on the COMET score.

Early-Exit and Instant Confidence Translation Quality Estimation

Multi-modal summarization (MMS) has emerged as a critical research area driven by the proliferation of multimedia content, focusing on generating condensed summaries by cross-modal complementary information synthesis. Previous studies have demonstrated the effectiveness of heterogeneous fusion paradigms, particularly through visual-centric feature extraction mechanisms, in constructing cross-modal representations that yield substantial performance gains. However, the use of multi-modal information and the inter-correlation among textual content, visual elements, and summary generation are still underestimated. We propose the Patch-Refined Visual Information Network (PRVIN) to address the insufficient exploitation of visual information. The essential patch selector and patch refiner components in PRVIN work collaboratively to progressively identify and refine critical visual features. An additional vision-to-summary alignment mechanism is also introduced to enhance the semantic connections between multi-modal representations and summary outputs. Extensive experiments conducted on two public MMS benchmark datasets demonstrate the superiority of PRVIN while quantitatively validating the crucial role of comprehensive visual information utilization in MMS tasks.\footnote{Our code will be available at

Progressive Visual Refinement for Multi-modal Summarization

Neuro-symbolic NLP methods aim to leverage the complementary strengths of large language models and formal logical solvers. However, current approaches are mostly static in nature, i.e., the integration of a target solver is predetermined at design time, hindering the ability to employ diverse formal inference strategies. To address this, we introduce an adaptive, multi-paradigm, neuro-symbolic inference framework that: (1) automatically identifies formal reasoning strategies from problems expressed in natural language; and (2) dynamically selects and applies specialized formal logical solvers via autoformalization interfaces. Extensive experiments on individual and multi-paradigm reasoning tasks support the following conclusions: LLMs are effective at predicting the necessary formal reasoning strategies with an accuracy above 90%. This enables flexible integration with formal logical solvers, resulting in our framework outperforming competing baselines -- by 17% and 6% compared to GPT-4o and DeepSeek-V3.1, respectively. Moreover, adaptive reasoning can even positively impact pure LLM methods, yielding gains of 10%, 5%, and 6% on zero-shot, CoT, and CoT sym settings with GPT-4o. Finally, although smaller models struggle with adaptive neuro-symbolic reasoning, post-training offers a viable path to improvement. Overall, this work establishes the foundations for adaptive LLM-symbolic reasoning, offering a path forward for unifying material and formal inferences on heterogeneous reasoning challenges. The code and data are available [online](https://github.com/idiap/adaptive symbolic reasoning).

Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition

Large reasoning models are trained to solve problems by decomposing them into steps. While they show impressive progress on reasoning tasks, "reasoning" here is typically limited to formal reasoning, i.e., math, code, and logic. An open question is whether these abilities transfer to pronominal reasoning, where step-by-step thinking in non-reasoning models worsens performance, but code pre-training may help. I answer this question by evaluating six pairs of original and DeepSeek-distilled models (1.5B-70B parameters) on six challenging datasets for English pronoun resolution (identifying whom a pronoun refers to) and pronoun fidelity (learning and applying a pronoun mapping correctly). Performance improves statistically significantly on all datasets (31% relative increase), indicating that distilling step-by-step formal reasoning does in fact help with pronominal reasoning, in part by improving instruction-following. With a qualitative evaluation of 720 generations, I show that improvements occur across granular error types, and come from plausible-looking reasoning chains employing a variety of reasoning strategies. However, the gains put models just above random performance on these datasets, leaving plenty of room for improvement.

Training in Step-by-Step Formal Reasoning Improves Pronominal Reasoning in Language Models

Strongly human-correlated evaluation metrics serve as an essential compass for the development and improvement of generation models, and they must be highly reliable and robust. Recent embedding-based neural text evaluation metrics, such as COMET for the translation task, are widely used in both research and development fields. However, there is no guarantee that they yield reliable evaluation results due to the black-box nature of neural networks. To raise concerns about the reliability and safety of such metrics, we propose a method for creating a single adversarial text in the discrete space that is consistently evaluated as high-quality, regardless of the test cases, in order to identify the vulnerabilities in evaluation metrics. Our found single hub text achieved 83.1 and 79.1 COMET% on the WMT'23 and WMT'24 English-to-Japanese translation tasks, respectively, outperforming translations generated individually for each source sentence by M2M100, a general translation model. Furthermore, we confirmed that our created single hub text generalizes across multiple language pairs such as Ja--En, En--De, and De--En.

Hacking Neural Evaluation Metrics with a Single Text

Novelty assessment is a central yet understudied aspect of peer review, particularly in high-volume fields like NLP where reviewer capacity is increasingly strained. We present a structured approach for automated novelty evaluation that models expert reviewer behavior through three stages: (i) content extraction from submissions, (ii) retrieval and synthesis of related work, and (iii) structured comparison for evidence-based assessment. Our method is informed by analysis of human-written novelty reviews and captures key patterns such as independent claim verification and contextual reasoning. Evaluated on 182 ICLR 2025 submissions with human-annotated reviewer novelty assessments, the approach achieves 86.5% alignment with human reasoning and 75.3% agreement on novelty conclusions, substantially outperforming existing LLM-based baselines. It produces detailed, literature-aware analysis and improves consistency over ad hoc reviewer judgments. These results highlight the potential for structured LLM-assisted approaches to support more rigorous and transparent peer review without displacing human expertise. The data and the code are available at https://ukplab.github.io/eacl2026-assessing-paper-novelty/

Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback

Large Vision-Language Models (LVLMs), trained by aligning visual encoders to LLMs on extensive vision-language data, demonstrate impressive performance across a broad variety of tasks that require understanding of both visual and textual inputs. Acknowledging this, recent work proposed to post-hoc convert generative LVLMs into vision-language encoders (VLEs) via supervised contrastive learning objectives. This type of training enables LVLMs to produce better representations, i.e., embeddings for image and text input, used in retrieval and (semantic) similarity tasks. Having observed that this type of VLEs (i.e., LVLMs turned into encoders) commonly employ last-token pooling in downstream tasks, without using special sequence-end tokens, in this focused contribution, we study the effect of pooling strategies on VLEs' downstream performance. We empirically show that, in contrast to mean pooling, last-token pooling (without special sequence-end tokens) makes VLEs highly sensitive to end-of-input artifacts in fine-tuning and inference data, e.g., whether input sequences end with punctuation or newline characters. Finally, we show that introducing the special end-of-sequence token removes this sensitivity and makes VLEs robust to formatting artifacts of input text.

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