Morocco

Humanitarian Mine Action (HMA) addresses the challenge of detecting and removing landmines from conflict regions. Much of the life-saving operational knowledge produced by HMA agencies is buried in unstructured reports, limiting the transferability of information between agencies. To address this issue, we propose TextMine: the first dataset, evaluation framework and ontology-guided large language model (LLM) pipeline for knowledge extraction in the HMA domain. TextMine structures HMA reports into (subject, relation, object)-triples, thus creating domain-specific knowledge. To ensure real-world relevance, we created the dataset in collaboration with Cambodian Mine Action Center (CMAC). We further introduce a bias-aware evaluation framework that combines human-annotated triples with an LLM-as-Judge protocol to mitigate position bias in reference-free scoring. Our experiments show that ontology-aligned prompts improve extraction accuracy by up to 44.2%, reduce hallucinations by 22.5%, and enhance format adherence by 20.9% compared to baseline models. We publicly release the dataset and code.

EACL 2026

TextMine: Data, Evaluation Framework and Ontology-guided LLM Pipeline for Humanitarian Mine Action

humanitarian mine action

knowledge graph

information extraction

## Welcome to EMNLP 2022!
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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. 

In recent years, pre-trained large language models (LLMs) have become a cornerstone for automatically generating answers in question-and-answer (Q&A) communities, significantly reducing user wait times and improving response quality. However, these models require substantial computational resources and are prone to generating hallucinated or unreliable content. To overcome these limitations, we propose an advanced expert-oriented Retrieval-Augmented Generation (RAG) framework as a cost-effective and reliable alternative. Central to our approach is a user-aware question entailment recognition module, which leverages user modeling to identify archived questions with answers that fully or partially address the user's new query. This user modeling significantly improves retrieval relevance, resulting in reduced hallucination and enhanced answer quality. The framework synthesizes expert-written answers from similar questions to generate unified responses. Experimental results on the CQADupStack and SE-PQA datasets show the superiority of our user-aware approach over its user-agnostic counterpart, with ROUGE-1 gains of 3.6% and 0.9%. Both human and AI evaluations confirm the effectiveness of incorporating user modeling in minimizing hallucination and delivering contextually appropriate answers, demonstrating its potential for real-world Q&A systems. The code and data are available on a GitHub repository at https://anonymous.4open.science/r/User-Oriented-RAG-CQA.

Enhancing Reliability in Community Question Answering with an Expert-Oriented RAG System

Sequential test-time scaling is a promising training-free method to improve large reasoning model accuracy, but as currently implemented, significant limitations have been observed. Inducing models to think for longer can increase their accuracy, but as the length of reasoning is further extended, it has also been shown to result in accuracy degradation and model instability. This work presents a novel sequential test-time scaling method, Min-Seek, which improves model accuracy significantly, over a wide range of induced thoughts, stabilizing the accuracy of sequential scaling, and removing the need for reasoning length fine-tuning. Beyond improving model accuracy over a variety of reasoning tasks, our method is inherently efficient, as only the KV pairs of one additional induced thought are kept in the KV cache during reasoning. With a custom KV cache which stores keys without position embeddings, by dynamically encoding them contiguously before each new generated thought, our method can continue to reason well beyond a model's maximum context length, and under mild conditions has linear computational complexity.

Thinking Long, but Short: Stable Sequential Test-Time Scaling for Large Reasoning Models

The rapid growth of academic video content makes it difficult for students and educators to find relevant information efficiently. This is especially challenging for low-resource languages like Bengali due to the lack of a video summarization tool. This paper presents the first end-to-end pipeline for the abstractive summarization of Bengali academic videos. The proposed system preprocesses audio to improve transcription quality and converts speech to text using Google’s Speech Recognition API. The text is segmented using a smart chunking method to be compatible with the model's context window. For summarization, we fine-tuned the BanglaT5 model on a new benchmark dataset of 10,029 text-summary pairs obtained from educational videos. To generate relevant titles, we fine-tuned the mT5-multilingual-XLSum model on our curated dataset of 1,005 summary-title pairs. Our fine-tuned summarization model shows strong performance, achieving F1 scores of 0.8793 (BERTScore), 0.3894 (ROUGE-1), and 0.2557 (ROUGE-L), outperforming other models. Our title generation model achieved ROUGE-1 and ROUGE-L F1 scores of 0.4476 and 0.3720, respectively. The summaries include timestamps for easy video navigation. This work aims to improve the accessibility of educational content in Bengali. It also contributes valuable datasets and a robust baseline system that demonstrates strong zero-shot capabilities on other spoken contents. The code and datasets are available at https://github.com/LamisaDeya/Bangla-Video-Summarization.

Abstractive Summarization of Bengali Academic Videos Based on Audio Subtitles

Recent work has explored the use of personal information in the form of persona sentences or self-disclosures to improve modeling of individual characteristics and prediction of annotator labels for subjective tasks. The volume of personal information has historically been restricted and thus little exploration has gone into understanding what kind of information is most informative for predicting annotator labels. In this work, we categorize self-disclosure sentences and use them to build annotator models for predicting judgments of social norms. We perform several ablations and analyses to examine the impact of the type of information on our ability to predict annotation patterns. We find that demographics are more impactful than attitudes, relationships, and experiences. Generally, theory-based approaches worked better than automatic clusters. Contrary to previous work, only a small number of related comments are needed. Lastly, having a more diverse sample of annotator self-disclosures leads to the best performance.

Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Norms

Despite significant ongoing efforts in safety alignment, large language models (LLMs) such as GPT-4 and LLaMA 3 remain vulnerable to jailbreak attacks that can induce harmful behaviors, including through the use of adversarial suffixes. Building on prior research, we hypothesize that these adversarial suffixes are not mere bugs but may represent features that can dominate the LLM's behavior. To evaluate this hypothesis, we conduct several experiments. First, we demonstrate that benign features can be effectively made to function as adversarial suffixes, i.e., we develop a feature extraction method to extract sample-agnostic features from benign dataset in the form of suffixes and show that these suffixes may effectively compromise safety alignment. Second, we show that adversarial suffixes generated from jailbreak attacks may contain meaningful features, i.e., appending the same suffix to different prompts results in responses exhibiting specific characteristics. Third, we show that such benign-yet-safety-compromising features can be easily introduced through fine-tuning using only benign datasets. As a result, we are able to completely eliminate GPT's safety alignment in a blackbox setting through finetuning with only benign data. Our code and data is available at \url{anonymous.4open.science/r/suffix-maybe-features-D17C/}.

Unleashing the Unseen: Harnessing Benign Datasets for Jailbreaking Large Language Models

Large language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, their ability to precisely control summary attributes (e.g., length or topic) remains underexplored, limiting their adaptability to specific user preferences. In this paper, we systematically explore the controllability of LLMs. To this end, we revisit summary attribute measurements and introduce iterative evaluation metrics, failure rate and average iteration count, to more precisely evaluate controllability beyond assessment of errors. Our findings show that LLMs struggle more with numerical attributes than with linguistic attributes. To address this challenge, we propose a guide-to-explain framework (GTE) for controllable summarization. GTE enables the model to identify misaligned attributes in the initial draft and guides it to self-explain errors in the previous output. By encouraging reflection on attribute misalignment, GTE generates well-adjusted summaries that satisfy the desired attributes with robust effectiveness while requiring surprisingly fewer iterations than other iterative approaches.

Exploring Iterative Controllable Summarization with Large Language Models

Evaluating visual activity recognition systems is challenging due to inherent ambiguities in verb semantics and image interpretation. When describing actions in images, synonymous verbs can refer to the same event (e.g., *brushing* vs. *grooming*), while different perspectives can lead to equally valid but distinct verb choices (e.g., *piloting* vs. *operating*). Standard exact-match evaluation, which relies on a single gold answer, fails to capture these ambiguities, resulting in an incomplete assessment of model performance. To address this, we propose a vision-language clustering framework that constructs **verb sense clusters**, providing a more robust evaluation. Our analysis of the imSitu dataset shows that each image maps to an average of 2.8 sense clusters, with each cluster representing a distinct perspective of the image. We evaluate multiple activity recognition models and compare our cluster-based evaluation with standard evaluation methods. Additionally, our human alignment analysis suggests that the cluster-based evaluation better aligns with human judgements, offering a more nuanced assessment of model performance.

Towards Robust Evaluation of Visual Activity Recognition: Resolving Verb Ambiguity with Sense Clustering

Multimodal Small-to-Medium sized Language Models (MSLMs) have demonstrated strong capabilities in integrating visual and textual information but still face significant limitations in visual comprehension and mathematical reasoning, particularly in geometric problems with diverse levels of visual infusion. Current models struggle to accurately decompose intricate visual inputs and connect perception with structured reasoning, leading to suboptimal performance. To address these challenges, we propose SpatialMath, a novel Spatial Comprehension-Infused Symbolic Reasoning Framework designed to integrate spatial representations into structured symbolic reasoning chains. SpatialMath employs a specialized perception module to extract spatially-grounded representations from visual diagrams, capturing critical geometric structures and spatial relationships. These representations are then methodically infused into symbolic reasoning chains, facilitating visual comprehension-aware structured reasoning. To this end, we introduce MATHVERSE-PLUS, a novel dataset containing structured visual interpretations and step-by-step reasoning paths for vision-intensive mathematical problems. SpatialMath significantly outperforms strong multimodal baselines, achieving up to 10 percentage points improvement over supervised fine-tuning with data augmentation in vision-intensive settings. Robustness analysis reveals that enhanced spatial representations directly improve reasoning accuracy, reinforcing the need for structured perception-to-reasoning pipelines in MSLMs.

SpatialMath: Spatial Comprehension-Infused Symbolic Reasoning for Mathematical Problem-Solving

Scripting interfaces enable users to automate tasks and customize software workflows, but creating scripts traditionally requires programming expertise and familiarity with specific APIs, posing barriers for many users. While Large Language Models (LLMs) can generate code from natural language queries, runtime code generation is severely limited due to unverified code, security risks, longer response times, and higher computational costs. To bridge the gap, we propose an offline simulation framework to curate a software-specific skillset—a collection of verified scripts—by exploiting LLMs and publicly available scripting guides. Our framework comprises two components: (1) task creation, using top-down functionality guidance and bottom-up API synergy exploration to generate helpful tasks; and (2) skill generation with trials, refining and validating scripts based on execution feedback. To efficiently navigate the extensive API landscape, we introduce a Graph Neural Network (GNN)-based link prediction model to capture API synergy, enabling the generation of skills involving underutilized APIs and expanding the skillset’s diversity. Experiments with Adobe Illustrator demonstrate that our framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation. This is the first attempt to use software scripting interfaces as a testbed for LLM-based systems, highlighting the advantages of leveraging execution feedback in a controlled environment and offering valuable insights into aligning AI capabilities with user needs in specialized software domains.

Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs

Linear concept vectors effectively steer LLMs, but existing methods suffer from noisy features in diverse datasets that undermine steering robustness. We propose Sparse Autoencoder-Denoised Concept Vectors (SDCV), which selectively keep the most discriminative SAE latents while reconstructing hidden representations. Our key insight is that concept-relevant signals can be explicitly separated from dataset noise by scaling up activations of top-k latents that best differentiate positive and negative samples. Applied to linear probing and difference-in-mean, SDCV consistently improves steering success rates by 4-16% across six challenging concepts, while maintaining topic relevance.

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Enhancing Reliability in Community Question Answering with an Expert-Oriented RAG System

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