Morocco

Prompt optimization has become crucial for enhancing the performance of large language models (LLMs) across a broad range of tasks. Although many research papers show its effectiveness, practical adoption is hindered as existing implementations are often tied to unmaintained and isolated research codebases. To address this, we introduce promptolution, a unified and modular open-source framework that provides all components required for prompt optimization within a single extensible system for both practitioners and researchers. It integrates multiple contemporary discrete prompt optimizers while remaining agnostic to the underlying LLM implementation.

EACL 2026

promptolution: A Unified, Modular Framework for Prompt Optimization

prompt optimization

demonstration

framework

large language models

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

demo

#### *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. 

We introduce T-pro 2.0, an open-weight Russian LLM for hybrid reasoning and efficient inference.
The model supports direct answering and reasoning-trace generation, using a Cyrillic-dense tokenizer and an adapted EAGLE speculative-decoding pipeline to reduce latency. To enable reproducible and extensible research, we release the model weights, the T-Wix 500k instruction corpus, the T-Math reasoning benchmark, and the EAGLE weights on HuggingFace. These resources allow users to study Russian-language reasoning and to extend or adapt both the model and the inference pipeline. A public web demo exposes reasoning and non-reasoning modes and illustrates the speedups achieved by our inference stack across domains.
T-pro 2.0 thus serves as an accessible open system for building and evaluating efficient, practical Russian LLM applications.


Demo: https://t-pro2eagle.streamlit.app/


https://huggingface.co/collections/t-tech/t-pro-20

T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground

We present SDialog, an MIT-licensed open-source Python toolkit that unifies dialog generation, evaluation and mechanistic interpretability into a single end-to-end framework for building and analyzing LLM-based conversational agents. Built around a standardized dialog representation, SDialog provides: (1) persona-driven multi-agent simulation with composable orchestration for controlled synthetic dialog generation, (2) comprehensive evaluation combining linguistic metrics, LLM-as-a-judge and functional correctness validators, (3) mechanistic interpretability tools for activation inspection and steering via feature ablation and induction, and (4) audio generation with full acoustic simulation including 3D room modeling and microphone effects. The toolkit integrates with all major LLM backends, enabling mixed-backend experiments under a unified API.
By coupling generation, evaluation, and interpretability in a dialog-centric architecture, SDialog enables researchers to build, benchmark and understand conversational systems more systematically.

SDialog: A Python Toolkit for End-to-End Agent Building, User Simulation, Dialog Generation, and Evaluation

Large language models (LLMs) have expanded the potential for AI-assisted scientific claim verification, yet existing systems often exhibit unverifiable attributions, shallow evidence mapping, and hallucinated citations. We present SciTrue, a claim verification system providing source-level accountability and evidence traceability. SciTrue links each claim component to explicit, verifiable scientific sources, enabling users to inspect and challenge model inferences, addressing limitations of both general-purpose and search-augmented LLMs. In a human evaluation of 300 attributions, SciTrue achieves high fidelity in summary traceability, attribution accuracy, and context alignment, substantially outperforming RAG-based baselines such as GPT-4o-search-preview and Perplexity Sonar Pro. These results underscore the importance of principled attribution and context-aware reasoning in AI-assisted scientific verification. A system demo is available at \href{https://www.scitrue.org/platform/?access=EACL2026DEMO}{www.scitrue.org}.

SciTrue: Evidence-Grounded Claim Verification in Science

Large language model (LLM) based search agents iteratively generate queries, retrieve external information, and reason to answer open-domain questions. While researchers have primarily focused on improving their utility, their safety behaviors remain underexplored. In this paper, we first evaluate search agents using red-teaming datasets and find that they are more likely to produce harmful outputs than base LLMs. For example, when asked ``How can I track someone's location without their consent?'', a base model refuses, whereas a search agent designed to retrieve and cite sources may lower its refusal threshold, fetch documents (e.g., court cases), and, once appended, synthesize them into an informative yet unsafe summary. We further show that utility-oriented fine-tuning intensifies this risk, motivating joint alignment of safety and utility. We present SafeSearch, a multi-objective reinforcement learning approach that couples a final-output safety/utility reward with a novel query-level shaping term that penalizes unsafe queries and rewards safe ones. Experiments show that SafeSearch reduces agent harmfulness by over 80% across three red-teaming datasets while producing safe, helpful responses, and matches the QA performance of a utility-only finetuned agent; further analyses confirm the effectiveness of the query-level reward in jointly improving safety and utility.

SafeSearch: Do Not Trade Safety for Utility in LLM Search Agents

Plane geometry problem solving (PGPS) has recently gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models. Despite the growing interest in PGPS, the research community still lacks a comprehensive overview that systematically synthesizes recent work in PGPS. To fill this gap, we present a survey of existing PGPS studies. We first categorize PGPS methods into an encoder-decoder framework and summarize the corresponding output formats used by their encoders and decoders. Subsequently, we classify and analyze these encoders and decoders according to their architectural designs. Finally, we outline major challenges and promising directions for future research. In particular, we discuss the hallucination issues arising during the encoding phase within encoder-decoder architectures, as well as the problem of data leakage in current PGPS benchmarks.

Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey

Recent multimodal large language models (MLLMs) extend language understanding beyond text to speech, enabling unified reasoning across modalities. While biases in text-based LLMs have been widely examined, their persistence and manifestation in spoken inputs remain underexplored. This work presents the first systematic investigation of speech bias in multilingual MLLMs. We construct and release the BiasInEar Dataset, a speech-augmented benchmark based on Global MMLU Lite, spanning English, Chinese, and Korean, balanced by gender and accent, and totaling 70.8 hours (4,249 minutes) of speech with 11,200 questions. Using four complementary metrics (accuracy, entropy, APES, and Fleiss’), we evaluate nine representative models under linguistic language and accent, demographic gender, and structural option order perturbations. Our findings reveal that MLLMs are relatively robust to demographic factors but highly sensitive to language and option order, suggesting that speech can amplify existing structural biases. Moreover, architectural design and reasoning strategy substantially affect robustness across languages. Overall, this study establishes a unified framework for assessing fairness and robustness in speech-integrated LLMs, bridging the gap between text- and speech-based evaluation.

Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations

The rise of toxic content on digital platforms has intensified the demand for automatic moderation tools. While English has benefited from large-scale annotated corpora, Spanish remains under-resourced, particularly for nuanced cases of toxicity such as irony, sarcasm, or indirect aggression. We present an extended version of the NECOS-TOX corpus, comprising 4,011 Spanish comments collected from 16 major news outlets. Each comment is annotated across three levels of toxicity (Non-Toxic, Slightly Toxic, and Toxic), following an iterative annotation protocol that achieved substantial inter-annotator agreement (k = 0.74). To reduce annotation costs while maintaining quality, we employed a human-in-the-loop active learning strategy, with manual correction of model pre-labels. We benchmarked the dataset with traditional machine learning (ML) methods, domain-specific transformers, and instruction-tuned large language models (LLMs). Results show that compact encoder models (e.g., RoBERTa-base-bne, 125M parameters) perform on par with much larger models (e.g., LLaMA-3.1-8B), underscoring the value of in-domain adaptation over raw scale. Our error analysis highlights persistent challenges in distinguishing subtle forms of toxicity, especially sarcasm and implicit insults, and reveals entity-related biases that motivate anonymization strategies. The dataset and trained models are released publicly.

Nuanced Toxicity Detection in Spanish: A New Corpus and Benchmark Study

Large Language Models (LLMs) are increasingly deployed in high-impact scenarios, raising concerns about their safety and security. Despite existing defense mechanisms, LLMs remain vulnerable to adversarial attacks. This paper introduces the novel attack-agnostic pipeline SENTRY (semantic entropy-based attack recognition system) for detecting such attacks by leveraging the predictive entropy of model outputs, quantified through the Token-Level Shifting Attention to Relevance (TokenSAR) score, a weighted token entropy measurement. Our approach dynamically identifies adversarial inputs without relying on prior knowledge of attack specifications. It requires only ten newly generated tokens, making it a computationally efficient and adaptable solution. We evaluate the pipeline on multiple state-of-the-art models, including Llama, Vicuna, Falcon, Deep Seek, and Mistral, using a diverse set of adversarial prompts generated via the h4rm31 framework. Experimental results demonstrate a clear separation in TokenSAR scores between benign, malicious, and adversarial prompts. This distinction enables effective threshold-based classification, achieving robust detection performance across various model architectures. Our method outperforms traditional defenses in terms of adaptability and resource efficiency.

Detection of Adversarial Prompts with Model Predictive Entropy

Large Language Models (LLMs) excel in Natural Language Processing (NLP) tasks, but they often propagate biases embedded in their training data, which is potentially impactful in sensitive domains like healthcare. While existing benchmarks evaluate biases related to individual social determinants of health (SDoH) such as gender or ethnicity, they often overlook interactions between these factors and lack context-specific assessments. This study investigates bias in LLMs by probing the relationships between gender and other SDoH in French patient records. Through a series of experiments, we found that embedded stereotypes can be probed using SDoH input and that LLMs rely on embedded stereotypes to make gendered decisions, thus positioning the assessment of relationships between SDoH as a potential complement to the evaluation of LLM performance.

Investigating Gender Stereotypes in Large Language Models via Social Determinants of Health

The predictive uncertainty of machine translation (MT) models is typically used as a quality estimation proxy. In this work, we posit that apart from confidently translating when a single correct translation exists, models should also maintain uncertainty when the input is ambiguous. We use uncertainty to measure gender bias in MT systems. When the source sentence includes a lexeme whose gender is not overtly marked, but whose target-language equivalent requires gender specification, the model must infer the appropriate gender from the context and can be susceptible to biases. Prior work measured bias via gender accuracy, however it cannot be applied to ambiguous cases. Using semantic uncertainty, we are able to assess bias when translating both ambiguous and unambiguous source sentences, and find that high translation accuracy does not correlate with exhibiting uncertainty appropriately, and that debiasing affects the two cases differently.

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T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground

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