Morocco

Retrieval-Augmented Generation (RAG) systems degrade sharply under extreme noise,where irrelevant or redundant passages dominate. Current methods-fixed top-k retrieval, cross-encoder reranking, or policy based iteration-depend on static heuristics or costly reinforcement learning, failing to assess evidence sufficiency, detect subtle mismatches, or reduce redundancy, leading to hallucinations and poor grounding. We introduce ReflectiveRAG, a lightweight yet reasoning-driven architecture that enhances factual grounding through two complementary mechanisms: Self-Reflective Retrieval (SRR) and Contrastive Noise Removal (NR). SRR employs small language model as a decision controller that iteratively evaluates evidence sufficiency, enabling adaptive query reformulation without fixed schedules or policy training. NR further refines retrieved content via embedding-based contrastive filtering, enforcing semantic sparsity and removing redundant or tangential passages. Evaluated on WebQuestions, HotpotQA (distractor setting) and InternalQA with 50M Common Crawl distractors, ReflectiveRAG achieves substantial gains over strong baselines-including DeepRAG-improving EM by +2.7 pp and F1 by +2.5 pp, while reducing evidence redundancy by 30.88% with only 18 ms additional latency. Ablation studies confirm that SRR and NR jointly drive both factual accuracy and efficiency, validating our central claim that retrieval reasoning and contrastive filtering can outperform large-scale policy optimization in RAG.

EACL 2026

ReflectiveRAG: Rethinking Adaptivity in Retrieval-Augmented Generation

low latency systems

information retrieval

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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)*<br><br>

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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. 

Retrieval-Augmented Generation (RAG) systems depend critically on retrieval quality to enable accurate, contextually relevant LLM responses. While LLMs excel at synthesis, their RAG performance is bottlenecked by document relevance. We evaluate advanced retrieval techniques including embedding model comparison, Reciprocal Rank Fusion (RRF), embedding concatenation and list-wise and adaptive LLM-based re-ranking, demonstrating that zero-shot LLMs outperform traditional cross-encoders in identifying high-relevance passages. We also explore context-aware embeddings, diverse chunking strategies, and model fine-tuning. All methods are rigorously evaluated on a proprietary dataset powering our deployed production chatbot, with validation on three public benchmarks: FiQA, HotpotQA, and SciDocs. Results show consistent gains in Recall@10, closing the gap with Recall@50 and yielding actionable pipeline recommendations. By prioritizing retrieval enhancements, we significantly elevate downstream LLM response quality in real-world, customer-facing applications.

Retrieval Enhancements for RAG: Insights from a Deployed Customer Support Chatbot

Public opinion surveys show cross-cultural differences in political opinions between socio-cultural contexts. However, there is no clear evidence whether these differences translate to cross-lingual differences in multilingual large language models (MLLMs). We analyze whether opinions transfer between languages or whether there are separate opinions for each language in MLLMs of various sizes across five Western languages. We evaluate MLLMs' opinions by prompting them to report their (dis)agreement with political statements from voting advice applications. To better understand the interaction between languages in the models, we evaluate them both before and after aligning them with more left or right views using direct preference optimization and English alignment data only. Our findings reveal that unaligned models show only very few significant cross-lingual differences in the political opinions they reflect. The political alignment shifts opinions almost uniformly across all five languages. We conclude that in Western language contexts, political opinions transfer between languages, demonstrating the challenges in achieving explicit socio-linguistic, cultural, and political alignment of MLLMs.

Do Political Opinions Transfer Between Western Languages? An Analysis of Unaligned and Aligned Multilingual LLMs

For socially sensitive tasks like hate speech detection, the quality of explanations from Large Language Models (LLMs) is crucial for factors like user trust and model alignment. While Persona prompting (PP) is increasingly used as a way to steer model towards user-specific generation, its effect on model rationales remains underexplored. We investigate how LLM-generated rationales vary when conditioned on different simulated demographic personas. Using datasets annotated with word-level rationales, we measure agreement with human annotations from different demographic groups, and assess the impact of PP on model bias and human alignment. Our evaluation across three LLMs results reveals three key findings: (1) PP improving classification on the most subjective task (hate speech) but degrading rationale quality. (2) Simulated personas fail to align with their real-world demographic counterparts, and high inter-persona agreement shows models are resistant to significant steering. (3) Models exhibit consistent demographic biases and a strong tendency to over-flag content as harmful, regardless of PP. Our findings reveal a critical trade-off: while PP can improve classification in socially-sensitive tasks, it often comes at the cost of rationale quality and fails to mitigate underlying biases, urging caution in its application.

Persona Prompting as a Lens on LLM Social Reasoning

We present AraBabyTalk-EGY, an enriched release of the Egyptian Arabic CHILDES corpus, that opens the child-adult interactions genre to modern Arabic NLP research. Starting from the original CHILDES recordings and IPA transcriptions of caregiver-child sessions, we (i) map each IPA token to fully diacritized Arabic script, and (ii) add core part-of-speech tags and lemmas aligned with existing dialectal Arabic morphological resources. These layers yield ~26K annotated tokens suitable for both text- and speech-based NLP tasks. We provide a benchmark on morphological disambiguation and Arabic ASR. We outline lexical and morphosyntactic differences between AraBabyTalk-EGY and general Egyptian Arabic resources, highlighting the value of genre-specific training data for language acquisition studies and Arabic speech technology.

Computational Benchmarks for Egyptian Arabic Child Directed Speech

Reliable uncertainty quantification (UQ) is essential when employing large language models (LLMs) in high-risk domains such as clinical question answering (QA). In this work, we evaluate uncertainty estimation methods for clinical QA focusing, for the first time, on eleven clinical specialties and six question types, and across ten open-source LLMs (general-purpose, biomedical, and reasoning models). We analyze score-based UQ methods, present a case study introducing a novel lightweight method based on behavioral features derived from reasoning-oriented models, and examine conformal prediction as a complementary set-based approach. Our findings reveal that uncertainty reliability is not a monolithic property, but one that depends on clinical specialty and question type due to shifts in calibration and discrimination. Our results highlight the need to select or ensemble models based on their distinct, complementary strengths and clinical use.

Mind the Gap: Benchmarking LLM Uncertainty and Calibration with Specialty-Aware Clinical QA and Reasoning-Based Behavioural Features

Medical reasoning in large language models seeks to replicate clinicians' cognitive processes in interpreting patient data and making diagnostic decisions. However, widely used benchmarksвЂ”such as MedQA, MedMCQA, and PubMedQAвЂ”mix questions that require multi-step reasoning with those answerable through factual recall, complicating evaluation. We demonstrate this by training a PubMedBERT-based classifier on expert-curated labels and applying it to 11 widely used biomedical QA benchmarks, where we find that only 32.8% of the questions require multi-step reasoning, indicating that current evaluations largely measure recall. This stratified evaluation of biomedical models (HuatuoGPT-o1, MedReason, m1) and general-domain models (DeepSeek-R1, o4-mini, Qwen3) reveals consistently lower performance on reasoning versus knowledge (e.g., HuatuoGPT-o1: 56.9% vs. 44.8%). Beyond accuracy, we assess robustness through adversarial evaluations in which models are prefixed with uncertainty-inducing statements; biomedical reasoning models degrade sharply in this setting (e.g., MedReason: 50.4% в†’ 24.4%), with declines especially pronounced on reasoning-heavy questions. Finally, we show that fine-tuning on high-quality reasoning examples augmented with adversarial traces, followed by reinforcement learning with GRPO, improves both robustness and accuracy across knowledge and reasoning subsets.

Reasoning or Knowledge: Stratified Evaluation of Biomedical LLMs

Sparse Autoencoders (SAEs) are powerful tools for interpreting neural representations, yet their use in audio remains underexplored. We train SAEs across all encoder layers of Whisper and HuBERT, provide an extensive evaluation of their stability, interpretability, and show their practical utility. Over 50% of the features remain consistent across random seeds, and reconstruction quality is preserved. SAE features capture general acoustic and semantic information as well as specific events, including environmental noises and paralinguistic sounds (e.g. laughter, whispering) and disentangle them effectively, requiring removal of only 19-27% of features to erase a concept. Feature steering reduces Whisper’s false speech detections by 70% with negligible WER increase, demonstrating real-world applicability. Finally, we find SAE features correlated with human EEG activity during speech perception, indicating alignment with human neural processing. The code and checkpoints are available at https://github.com/audiosae/audiosae_demo.

AudioSAE: Towards Understanding of Audio-Processing Models with Sparse AutoEncoders

Despite their impressive performance, vision-language models (VLMs) still struggle on culturally situated inputs. To understand how VLMs process culturally grounded information, we study the presence of culture-sensitive neurons, i.e. neurons whose activations show preferential sensitivity to inputs associated with particular cultural contexts. We examine whether such neurons are important for culturally diverse visual question answering and where they are located. Using the CVQA benchmark, we identify neurons of culture selectivity and perform causal tests by deactivating the neurons flagged by different identification methods. Experiments on three VLMs across 25 cultural groups demonstrate the existence of neurons whose ablation disproportionately harms performance on questions about the corresponding cultures, while having minimal effects on others. Moreover, we propose a new margin-based selector: Contrastive Activation Selection (CAS), and show that it outperforms existing probability- and entropy-based methods in identifying culture-sensitive neurons. Finally, our layer-wise analyses reveals that such neurons tend to cluster in certain decoder layers. Overall, our findings shed new light on the internal organization of multimodal representations.

Finding Culture-Sensitive Neurons in Vision-Language Models

Persona-assigned large language models (LLMs) are used in domains such as education, healthcare, and sociodemographic simulation. Yet, they are typically evaluated only in short, single-round settings that do not reflect real-world usage. We introduce an evaluation protocol that combines long persona dialogues (over 100 rounds) and evaluation datasets to create dialogue-conditioned benchmarks that can robustly measure long-context effects. We then investigate the effects of dialogue length on persona fidelity, instruction-following, and safety of seven state-of-the-art open- and closed-weight LLMs. We find that persona fidelity degrades over the course of dialogues, especially in goal-oriented conversations, where models must sustain both persona fidelity and instruction following. We identify a trade-off between fidelity and instruction following, with non-persona baselines initially outperforming persona-assigned models; as dialogues progress and fidelity fades, persona responses become increasingly similar to baseline responses. Our findings highlight the fragility of persona applications in extended interactions and our work provides a protocol to systematically measure such failures.

Persistent Personas? Role-Playing, Instruction Following, and Safety in Extended Interactions

The potential misuse and misalignment of language models (LMs) is a central safety concern. This work presents Self-Destruct, a novel mechanism to restrict specific behaviors in LMs by leveraging overlooked properties of the underlying hardware. We observe that the LM frameworks use limited-precision formats (e.g., FP32), which are vulnerable to overflow errors during matrix multiplications. Exploiting this property, Self-Destruct replaces selected weights in pre-trained LM layers with values that act as traps, triggering a system error only when the model engages in targeted behaviors, such as harmful text generation, while leaving normal functionality unaffected. Unlike post-hoc filters, this safeguard is embedded directly within the model, introduces neither inference overhead nor auxiliary models, and requires only a set of examples for calibration. Experiments on Llama-3.2 and Qwen-2.5 demonstrate that Self-Destruct provides competitive protection against jailbreak attacks while preserving accuracy on standard benchmarks. Our results highlight the potential of hardware-aware safeguards as an efficient, low-overhead complement to existing LM defenses.

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Retrieval Enhancements for RAG: Insights from a Deployed Customer Support Chatbot

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Retrieval Enhancements for RAG: Insights from a Deployed Customer Support Chatbot

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