Morocco

Automatic Speech Recognition (ASR) systems for low-resource languages like Hindi often produce erroneous transcripts due to limited annotated data and linguistic complexity. **Post-ASR correction** using language models (LMs) and large language models (LLMs) offers a promising approach to improve transcription quality. In this work, we compare fine-tuned LMs (mT5, ByT5), fine-tuned LLMs (Nanda 10B), and instruction-tuned LLMs (GPT-4o-mini, LLaMA variants) for post-ASR correction in Hindi. Our findings reveal that **smaller, fine-tuned models** consistently **outperform larger LLMs** in both fine-tuning and in-context learning (ICL) settings. We observe a **U-shaped inverse scaling** trend under zero-shot ICL, where mid-sized LLMs degrade performance before marginal recovery at extreme scales, yet still fall short of fine-tuned models. **ByT5 is more effective for character-level corrections** such as transliteration and word segmentation, while **mT5 handles broader semantic inconsistencies**. We also identify performance drops in out-of-domain settings and propose **mitigation strategies** to preserve domain fidelity. In particular, we observe similar trends in **Marathi and Telugu**, indicating the broader applicability of our findings across low-resource Indian languages.

EACL 2026

Post-ASR Correction in Hindi: Comparing Language Models and Large Language Models in Low-Resource Scenarios

post-asr correction

large language model

automatic speech recognition

low resource

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. 

Interpretability provides a toolset for understanding how and why language models behave in certain ways. However, there is little unity in the field: most studies employ ad-hoc evaluations and do not share theoretical foundations, making it difficult to measure progress and compare the pros and cons of different techniques. Furthermore, while mechanistic understanding is frequently discussed, the basic causal units underlying these mechanisms are often not explicitly defined. In this article, we propose a perspective on interpretability research grounded in causal mediation analysis. Specifically, we describe the history and current state of interpretability taxonomized according to the types of causal units (mediators) employed, as well as methods used to search over mediators. We discuss the pros and cons of each mediator, providing insights as to when particular kinds of mediators and search methods are most appropriate. We argue that this framing yields a more cohesive narrative of the field and helps researchers select appropriate methods based on their research objective. Our analysis yields actionable recommendations for future work, including the discovery of new mediators and the development of standardized evaluations tailored to these goals.

The Quest for the Right Mediator: Surveying Mechanistic Interpretability for NLP Through the Lens of Causal Mediation Analysis

Membership Inference Attacks (MIAs) aim to determine whether a specific data point was included in the training set of a target model. Although there are have been numerous methods developed for detecting data contamination in large language models (LLMs), their performance on multimodal LLMs (MLLMs) falls short due to the instabilities introduced through multimodal component adaptation and possible distribution shifts across multiple inputs. In this work, we investigate multimodal membership inference and address two issues: first, by identifying distribution shifts in the existing datasets, and second, by releasing an extended baseline pipeline to detect them. We also generalize the perturbation-based membership inference methods to MLLMs and release FiMMIA — a modular Framework for Multimodal MIA. Our approach trains a neural network to analyze the target model's behavior on perturbed inputs, capturing distributional differences between members and non-members. Comprehensive evaluations on various fine-tuned multimodal models demonstrate the effectiveness of our perturbation-based membership inference attacks in multimodal domains.

FiMMIA: scaling semantic perturbation-based membership inference across modalities

Despite growing interest in measuring linguistic diversity on the one hand and the increasing availability of cross-linguistically comparable parsed corpora on the other, tools for systematically measuring the diversity of specific linguistic phenomena on such data remain limited. To address this gap, we present DELTA, an open-source framework that integrates dependency tree querying with diversity computation, enabling systematic measurement across multiple linguistic levels (lexis, morphology, syntax) and multiple diversity dimensions (variety, balance, disparity). The pipeline processes CoNLL-U formatted corpora through configurable workflows and supports both predefined configurations and flexible customization. We validate DELTA on Parallel Universal Dependencies multilingual dataset, demonstrating its capacity for corpus profiling and cross-corpus diversity comparison.

DELTA: A Unified Framework for Measuring Linguistic Diversity in Dependency-Parsed Corpora

Sustainability reports contain rich Environmental, Social and Governance (ESG) information, but their heterogeneous layouts and complex multi-table structures pose major challenges for LLMs, especially for unit normalization, cross-document reasoning, and precise numerical computation. We present CLARIESG, an end-to-end system that couples robust table extraction with a structured prompting framework for multi-table filtering, normalization, and program-of-thought reasoning. On ESG-focused multi-table benchmarks, CLARIESG consistently outperforms standard prompting and provides transparent, auditable reasoning, supporting more reliable ESG analysis and greenwashing detection in real-world settings.

CLARIESG: An End-to-End System for ESG Analysis over Complex Tables in Corporate Reports

PromptLab is a web-based platform for collaborative prompt engineering across diverse natural language processing tasks and datasets. The platform addresses primary challenges in prompt development, including template creation, collaborative review, and quality assurance through a comprehensive workflow that supports both individual researchers and team-based projects. PromptLab integrates with HuggingFace and provides AI-assisted prompt generation via OpenRouter, supporting real-time validation with multiple Large Language Models (LLMs). The platform features a flexible templating system using Jinja2, role-based project management, peer review processes, and supports programmatic access through RESTful APIs. To ensure data privacy and support sensitive research environments, PromptLab includes an easy CI/CD pipeline for self-hosted deployments and institutional control. We demonstrate the platform's effectiveness through two evaluations: a controlled comparison study with six researchers across five benchmark datasets and 13 models with 90 prompts; and a comprehensive case study in instruction tuning research, where over 350 prompts across 80+ datasets have been developed and validated by multiple team members. The platform is available at https://promptlab.up.railway.app.

PromptLab: A Collaborative Platform for Prompt Engineering and Dataset Curation (presented virtually)

Evaluating Retrieval-Augmented Generation
(RAG) systems remains a challenging task: existing
metrics often collapse heterogeneous behaviors
into single scores and provide little insight
into whether errors arise from retrieval,
reasoning, or grounding. In this paper, we introduce
RAGVUE, a diagnostic and explainable
framework for automated, reference-free
evaluation of RAG pipelines. RAGVUE decomposes
RAG behavior into retrieval quality,
answer relevance and completeness, strict
claim-level faithfulness, and judge calibration.
Each metric includes a structured explanation,
making the evaluation process transparent. Our
framework supports both manual metric selection
and fully automated agentic evaluation. It
also provides a Python API, CLI, and a local
Streamlit interface for interactive usage. In
comparative experiments, RAGVUE surfaces
fine-grained failures that existing tools such
as RAGAS often overlook. We showcase the
full RAGVUE workflow and illustrate how it
can be integrated into research pipelines and
practical RAG development. The source code
and detailed instructions on usage are publicly
available on Github.

RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation

Agentic search has emerged as a promising paradigm for adaptive retrieval systems powered by large language models (LLMs). However, existing benchmarks primarily focus on quality, overlooking efficiency factors that are critical for real-world deployment. Moreover, real-world user queries often contain underspecified preferences, a challenge that remains largely underexplored in current agentic search evaluation. As a result, many agentic search systems remain impractical despite their impressive performance. In this work, we introduce HotelQuEST, a benchmark comprising 214 hotel search queries that range from simple factual requests to complex queries, enabling evaluation across the full spectrum of query difficulty. We further address the challenge of evaluating underspecified user preferences by collecting clarifications that make annotators' implicit preferences explicit for evaluation. We find that LLM-based agents achieve higher accuracy than traditional retrievers, but at substantially higher costs due to redundant tool calls and suboptimal routing that fails to match query complexity to model capability. Our analysis exposes inefficiencies in current agentic search systems and demonstrates substantial potential for cost-aware optimization.

HotelQuEST: Balancing Quality and Efficiency in Agentic Search

Long-context conversational agents require robust memory, but existing frameworks struggle to organize information effectively across dimensions like time and topic, leading to poor retrieval. To address this, we introduce H-Mem, a novel Hybrid Multi-Dimensional Memory architecture. H-Mem stores conversational facts in two parallel, hierarchical data structures: a temporal tree that organizes information chronologically and a semantic tree that organizes it conceptually. This dual-tree design enables a hybrid retrieval mechanism managed by an intelligent Mode Controller. Based on the query, the controller dynamically chooses between a sequential search using semantic anchors and an intersective search combining both hierarchies. Our experiments on long-context QA datasets demonstrate that H-Mem provides a more flexible approach to memory management, leading to significant improvements of over 8.4\% compared to other state-of-the-art systems.

H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents

Prompt-based adversarial attacks are a key tool for assessing the robustness of large language models (LLMs). Yet, existing studies typically treat prompts as flat text, overlooking their internal structure, different components within a prompt contribute unequally to robustness. This work introduces PromptAnatomy, a framework that decomposes prompts into functional components and perturbs them selectively to expose component-wise vulnerabilities. We further propose ComPerturb, a controlled perturbation method that ensures linguistic plausibility through perplexity-based filtering. Using this framework, four instruction-tuning datasets are structurally annotated and validated by human reviewers. Experiments across five advanced LLMs show that ComPerturb achieves state-of-the-art attack success rates, while ablation analyses confirm the complementary effects of prompt dissection and perplexity filtering. These results highlight the importance of structural awareness in evaluating and improving the adversarial robustness of LLMs.

Are All Prompt Components Value-Neutral? Understanding the Heterogeneous Adversarial Robustness of Dissected Prompt in LLMs

Leveraging multimodal large language models (MLLMs) to construct embodied agents represents a promising approach for addressing real-world tasks. However, current benchmarks predominantly focus on language-centric tasks or heavily rely on simulated environments, thereby restricting their capacity to evaluate performance in realistic settings. To bridge this gap, we introduce CityNav, a comprehensive benchmark encompassing four diverse global cities, explicitly designed to evaluate the decision-making capabilities of raw MLLM-driven agents in real-world navigation tasks. Specifically, agents must rely solely on visual observations and internal multimodal reasoning to sequentially execute a significant number of decisions (50+) without additional environmental annotations or specialized architectural enhancements. Extensive evaluation reveals that popular linguistic reasoning techniques (e.g., Chain-of-Thought, Self-Consistency, Reflection) fail to deliver substantial improvements in performance. To address this, we propose Verbalization of Path (VoP), a novel method that explicitly grounds the agent's internal multimodal reasoning through verbalized navigational paths, substantially enhancing navigation success. Nonetheless, overall performance in particularly challenging cities remains insufficient, underscoring the critical necessity for advanced reasoning frameworks and robust methods capable of handling complex, long-range sequential decision-making tasks. The code and dataset will be released on acceptance.

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The Quest for the Right Mediator: Surveying Mechanistic Interpretability for NLP Through the Lens of Causal Mediation Analysis

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Next from EACL 2026

The Quest for the Right Mediator: Surveying Mechanistic Interpretability for NLP Through the Lens of Causal Mediation Analysis

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads