Morocco

Ghosting, the ability to predict a user&#39;s intended text input for inline query auto-completion, is an invaluable feature for modern search engines and chat interfaces, greatly enhancing user experience. By suggesting completions to incomplete queries (or prefixes), ghosting aids users with slow typing speeds, disabilities, or limited language proficiency. Ghosting is a challenging problem and has become more important with the ubiquitousness of chat-based systems like ChatGPT, Copilot, etc. Despite the increasing prominence of chat-based systems utilizing ghosting, this challenging problem of Chat-Ghosting has received little attention from the NLP/ML research community. There is a lack of standardized benchmarks and relative performance analysis of deep learning and non-deep learning methods. We address this through an open and thorough study of this problem using four publicly available dialog datasets: two human-human (DailyDialog and DSTC7-Ubuntu) and two human-bot (Open Assistant and ShareGPT). We experiment with various existing query auto-completion methods (using tries), n-gram methods and deep learning methods, with and without dialog context. We also propose a novel entropy-based dynamic early stopping strategy. Our analysis finds that statistical n-gram models and tries outperform deep learning based models in terms of both model performance and inference efficiency for seen prefixes. For unseen queries, neural models like T5 and Phi-2 lead to better results. Adding conversational context leads to significant improvements in ghosting quality, especially for Open-Assistant and ShareGPT. We make code and data publicly available at https://anonymous.4open.science/r/Chat-AutoSuggest-Project-DF8F/

EACL 2026

Chat-Ghosting: Methods for Auto-Completion in Dialog Systems

query auto-completion

chat-ghosting

dialog systems

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AMA Research Challenge 2024 

Join the AMA Research Challenge poster symposium and semifinals to explore top research posters and score posters in the semifinals (an AMA member exclusive opportunity).

Ghosting, the ability to predict a user's intended text input for inline query auto-completion, is an invaluable feature for modern search engines and chat interfaces, greatly enhancing user experience. By suggesting completions to incomplete queries (or prefixes), ghosting aids users with slow typing speeds, disabilities, or limited language proficiency. Ghosting is a challenging problem and has become more important with the ubiquitousness of chat-based systems like ChatGPT, Copilot, etc. Despite the increasing prominence of chat-based systems utilizing ghosting, this challenging problem of Chat-Ghosting has received little attention from the NLP/ML research community. There is a lack of standardized benchmarks and relative performance analysis of deep learning and non-deep learning methods. We address this through an open and thorough study of this problem using four publicly available dialog datasets: two human-human (DailyDialog and DSTC7-Ubuntu) and two human-bot (Open Assistant and ShareGPT). We experiment with various existing query auto-completion methods (using tries), n-gram methods and deep learning methods, with and without dialog context. We also propose a novel entropy-based dynamic early stopping strategy. Our analysis finds that statistical n-gram models and tries outperform deep learning based models in terms of both model performance and inference efficiency for seen prefixes. For unseen queries, neural models like T5 and Phi-2 lead to better results. Adding conversational context leads to significant improvements in ghosting quality, especially for Open-Assistant and ShareGPT. We make code and data publicly available at https://anonymous.4open.science/r/Chat-AutoSuggest-Project-DF8F/

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.
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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. 

Document-Level Zero-Shot Relation Extraction (DocZSRE) aims to predict unseen relation labels in text documents without prior training on specific relations. Existing approaches rely on Large Language Models (LLMs) to generate synthetic data for unseen labels, which poses challenges for low-resource languages like Malaysian English. These challenges include the incorporation of local linguistic nuances and the risk of factual inaccuracies in LLM-generated data. This paper introduces Document-Level Zero-Shot Relation Extraction with Entity Side Information (DocZSRE-SI) to address limitations in the existing DocZSRE approach. The DocZSRE-SI framework leverages Entity Side Information, such as Entity Mention Descriptions and Entity Mention Hypernyms, to perform ZSRE without depending on LLM-generated synthetic data. The proposed low-complexity model achieves an average improvement of 11.6% in the macro F1-Score compared to baseline models and existing benchmarks. By utilising Entity Side Information, DocZSRE-SI offers a robust and efficient alternative to error-prone, LLM-based methods, demonstrating significant advancements in handling low-resource languages and linguistic diversity in relation extraction tasks. This research provides a scalable and reliable solution for ZSRE, particularly in contexts like Malaysian English news articles, where traditional LLM-based approaches fall short.

Document-Level Zero-Shot Relation Extraction with Entity Side Information

Large Language Models (LLMs) are increasingly used for knowledge-based reasoning tasks, yet understanding when they rely on genuine knowledge versus superficial heuristics remains challenging. We investigate this question through entity comparison tasks by asking models to compare entities along numerical attributes (e.g., ``Which river is longer, the Danube or the Nile?''), which offer clear ground truth for systematic analysis. Despite having sufficient numerical knowledge to answer correctly, LLMs frequently make predictions which contradict this knowledge. We identify three heuristic biases that strongly influence model predictions: entity popularity, mention order, and semantic co-occurrence. For smaller models, a simple logistic regression using only these surface cues predicts model choices more accurately than the model's own numerical predictions, suggesting heuristics largely override principled reasoning. Crucially, we find that larger models (32B parameters) selectively rely on numerical knowledge when it is more reliable, while smaller models (7-8B parameters) show no such discrimination, which explains why larger models outperform smaller ones even when the smaller models possess more accurate knowledge. Chain-of-thought prompting steers all models towards using the numerical features across all model sizes.

Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities

Large language models (LLMs) can answer prompts in many languages, despite being trained predominantly on English; yet, the mechanisms driving this generalization remain poorly understood. This work asks: How does an LLM's ability to align representations of non-English inputs to English impact its performance on natural language understanding (NLU) tasks? We study the role of representation alignment in instance-level task decisions, complementing prior analyses conducted both at the language level and task-independently. We introduce the Discriminative Alignment Index () to quantify instance-level alignment across 24 languages other than English and three distinct NLU tasks. Results show that incorrect NLU predictions are strongly associated with lower representation alignment with English in the model's middle layers. Through activation patching, we show that incorrect predictions in languages other than English can be fixed by patching their parallel English activations in the middle layers, thereby demonstrating the causal role of representation (mis)alignment in cross-lingual correctness.

Can you map it to English? The Role of Cross-Lingual Alignment in Multilingual Performance of LLMs

Large Language Models (LLMs) exhibit remarkable capabilities, but no single model optimally balances serving quality and deployment cost across diverse tasks. Multi-LLM systems address this challenge through intelligent routing mechanisms that dynamically allocate queries to the most appropriate model. However, existing routing methods suffer from two fundamental limitations: (i) dependence on extensive full-response datasets for training, and (ii) poor scalability when incorporating new models, typically necessitating retraining from scratch. In this paper, we propose SemiRouter, a novel LLM routing framework designed for data-sparse and evolving model environments. Our approach combines a data-efficient training methodology with an adaptive architecture that enables seamless integration of new models under limited supervision. As an extension, we also consider energy footprint as a potential deployment cost in our routing decision. Empirical evaluations demonstrate that our method improves data efficiency, adaptability, and routing accuracy compared to existing approaches, providing a scalable solution for dynamic multi-LLM deployment.

SEMIROUTER: Sparse-Data Enhanced Routing for Adaptive Multi-LLM System

As multilingual large language models become more widely used, ensuring their safety and fairness across diverse linguistic contexts presents unique challenges. While existing research on machine unlearning has mainly focused on monolingual settings, typically English, multilingual environments introduce additional complexities due to cross-lingual knowledge transfer and biases embedded in both pretraining and fine-tuning data. In this work, we address the problem of multilingual unlearning using the Aya-Expanse 8B model under two settings: (1) data unlearning and (2) concept unlearning. We extend benchmarks for factual knowledge and stereotypes into ten languages through translationвЂ”English, French, Arabic, Japanese, Russian, Farsi, Korean, Hindi, Hebrew, and IndonesianвЂ”spanning five language families and varying resource levels. Our experiments show that unlearning in high-resource languages tends to be more stable, with asymmetric transfer observed between typologically related languages. Moreover, analysis of linguistic distances reveals that syntactic similarity is the most predictive factor of cross-lingual unlearning effects.

Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs

Large Audio-Language Models (LALMs) are often constrained by short audio context windows, even when their text backbones support long contexts, limiting long-form audio understanding. Prior work has introduced contextextension methods (e.g. YaRN) on unimodal LLMs, yet their application to LALMs remains unexplored. First, building on RoPEbased context extension, we introduce Partial
YaRN, a training-free, modality-decoupled extension method that modifies only audio token positions, leaving text positions intact to preserve the base LLM’s text capabilities. Second, we propose Virtual Longform Audio Training (VLAT), a training strategy that extends Partial YaRN into a training-time positional augmentation. VLAT simulates diverse audio lengths during training, enabling generalization to inputs far longer than those seen in training. Our experiments on SALMONN and Qwen2-Audio confirm that Partial YaRN outperforms the original models across wide range of settings, and VLAT provides substantial performance improvement on long audio of unseen lengths.

Extending Audio Context for Long-Form Understanding in Large Audio-Language Models

The accelerating growth of scientific publications has intensified the need for scalable, trustworthy systems to synthesize knowledge across diverse literature. While recent retrieval-augmented generation (RAG) methods have improved access to scientific information, they often overlook citation graph structure, adapt poorly to complex queries, and yield fragmented, hard-to-verify syntheses. We introduce SciRAG, an open-source framework for scientific literature exploration that addresses these gaps through three key innovations: (1) adaptive retrieval that flexibly alternates between sequential and parallel evidence gathering; (2) citation-aware symbolic reasoning that leverages citation graphs to organize and filter supporting documents; and (3) outline-guided synthesis that plans, critiques, and refines answers to ensure coherence and transparent attribution. Extensive experiments across multiple benchmarks such as QASA and ScholarQA demonstrate that SciRAG outperforms prior systems in factual accuracy and synthesis quality, establishing a new foundation for reliable, large-scale scientific knowledge aggregation.

SciRAG: Adaptive, Citation-Aware, and Outline-Guided Retrieval and Synthesis for Scientific Literature

We examine in how far a person's value system has an impact on their perception of offensiveness. For instance, a scholar is likely to be offended by being accused of reporting unverified claims whereas many non-scholars would not feel that way. Thus, we move away from the assumption that offensiveness can be defined through a universal perspective. Ultimately, such research aims to support personalized approaches to content moderation. Our main contribution is the introduction of a dataset consisting of neutrally-phrased sentences on controversial topics, evaluated by individuals from 4 different value systems. This allows us to identify offensiveness patterns across value systems and conduct classification experiments.

The Relevance of Value Systems for Offensive Language Detection

Extending Large Language Models (LLMs) to new languages is challenging, with most methods proposed suffering from high computational cost and catastrophic forgetting of original model capabilities. Embedding relearning~\citep{artetxe-etal-2020-cross}, a technique that creates new tokenizers and tunes embeddings on fixed model weights for target language adaptation, is both light-weight and performant. However, it has only been shown to work for older generation encoder-only models and for high resource languages. In this paper, we extend this framework to decoder-only LLMs focusing on joint adaptation to many languages, including low-resource ones. We experiment in three language groups over 100 languages each. We adapt a pre-trained LLM via switching to a customized tokenizer, and relearning the embedding layer. Across 96 diverse languages spanning both classification and generation tasks, we show embedding relearning improves \texttt{Gemma2} models by up to 20%, being highly competitive with full-weight updating baselines while vastly more computationally efficient and mitigating catastrophic forgetting. This translates into better results in transferring the improved multilingual performance to tasks that build on core English abilities (e.g., multilingual math reasoning), compared to various baselines. Further analysis reveals the critical role of customizing tokenizers in achieving effective language transfer, particularly for non-Latin script languages.

Tokenizer-Aware Cross-Lingual Adaptation of Decoder-Only LLMs through Embedding Relearning and Swapping

Large language models (LLMs) are safety-aligned to prevent harmful response generation, yet still remain vulnerable to jailbreak attacks. While prior works have focused on improving jailbreak attack effectiveness, they offer little explanation for why safety alignment fails. We address this gap by framing jailbreaks as inference-time alignment, connecting attack design and safety alignment within a unified optimization framework. This framing allows us to extend best-of-N inference-time alignment to the adversarial setting, called LIAR (Leveraging Inference-time Alignment to jailbReak), and derive suboptimality bounds that show LIAR provably approaches an optimal jailbreak as compute scales. Interestingly, our framework allows us to develop the notion of a Safety-Net, a measure of how vulnerable an LLM is to jailbreaks, which helps to explain why safety alignment can fail. Empirically, LIAR produces natural, hard-to-detect prompts that achieve a competitive attack success rate while running 10 to 100x faster than prior suffix-based jailbreaks.

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