Morocco

Large Language Model (LLM) safety is inherently pluralistic, reflecting variations in moral norms, cultural expectations, and demographic contexts. Yet, existing alignment datasets such as Anthropic-HH and DICES rely on demographically narrow annotator pools, overlooking variation in safety perception across communities. Demo-SafetyBench addresses this gap by modeling demographic pluralism directly at the prompt level, decoupling value framing from responses. In Stage I, prompts from DICES are reclassified into 14 safety domains (adapted from BeaverTails) using Mistral-7B-Instruct-v0.3, retaining demographic metadata and expanding low-resource domains via Llama-3.1-8B-Instruct with SimHash-based deduplication, yielding 43,050 samples. In Stage II, pluralistic sensitivity is evaluated using LLMs-as-Raters—Gemma-7B, GPT-4o, and LLaMA-2-7B—under zero-shot inference. Balanced thresholds (delta = 0.5, tau = 10) achieve high reliability (ICC = 0.87) and low demographic sensitivity (DS = 0.12), confirming that pluralistic safety evaluation can be both scalable and demographically robust. Code and data available at: https://github.com/usmaann/Demo-SafetyBench

EACL 2026

Do Large Language Models Reflect Demographic Pluralism in Safety?

demographics

safety

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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. 

We design, implement, and evaluate adversarial decoding, a new, generic text generation technique that produces readable documents for adversarial objectives such as RAG poisoning, jailbreaking, and evasion of defensive filters. Prior generation methods either produce easily detectable gibberish (even methods that optimize for low perplexity), or cannot handle objectives that include embedding similarity. In particular, they cannot produce readable adversarial documents that (1) are retrieved by RAG systems in response to broad classes of queries, and (2) adversarially influence subsequent generation. We measure the effectiveness of adversarial decoding for different objectives and demonstrate that it outperforms existing methods while producing adversarial documents that cannot be automatically distinguished from natural documents by fluency and readability.

Adversarial Decoding: Generating Readable Documents for Adversarial Objectives

Large Language Models (LLMs) have been frequently used as automatic annotators for tasks such as Text Emotion Recognition (TER). We consider a scenario in which annotators assign at least one emotion label from a large set of options to a text snippet. For this emotion tagging task, we propose a novel zero-shot algorithm that leverages Best-Worst Scaling (BWS), prompting the LLM to choose the least and most suitable emotions for a given text from several label subsets. The LLM's choices can be represented by a graph linking labels via worse-than relations. Random walks on this graph yield the final score for each label. We compare our algorithm with naive prompting approaches as well as an established BWS-based method. Extensive experiments demonstrate the suitability of the method. It proves to compare favorably to the benchmarks in terms of both accuracy and calibration with respect to human annotations. Moreover, our algorithm's automatic annotations are shown to be suitable for finetuning lightweight emotion classification models. The proposed method consumes considerably fewer computational resources than the established BWS approach.

Training-Free Text Emotion Tagging via LLM-Based Best-Worst Scaling

Text-to-SQL systems translate natural language questions into executable SQL queries, and recent progress with large language models (LLMs) has driven substantial improvements in this task. Schema linking remains a critical component in Text-to-SQL systems, reducing prompt size for models with narrow context windows and sharpening model focus even when the entire schema fits. We present a zero-shot, training-free schema linking approach that first constructs a schema graph based on foreign key relations, then uses a single prompt to a lightweight LLM to extract source and destination tables from the user query, followed by applying classical path-finding algorithms and post-processing to identify the optimal sequence of tables and columns that should be joined, enabling the LLM to generate more accurate SQL queries. To handle real-world databases where foreign keys may be missing or inconsistent, we further propose an LLM-guided joinability discovery step that infers table connections before graph construction, ensuring robustness across diverse schemas. Despite being simple, cost-effective, and highly scalable, our method achieves state-of-the-art results on both the BIRD and Spider 2.0 benchmarks, outperforming previous specialized, fine-tuned, and complex multi-step LLM-based approaches.

SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases

Although expressive TTS systems aim to capture human-like emotion, little is known about how well emotional signals in text correspond to those in speech. In this short paper, we investigate the emotion alignment (Valence, Arousal, Dominance) between text and speech models. We use 8 large language models for identifying text emotions and two audio models for speech analysis, across three genres: Podcasts, Audiobooks and TED talks. Findings show that while language models perform well on emotion recognition from situational text, and the audio models performs well on speech, they show alignment on the Valence dimension only. Further, the genre of the content significantly impacts the alignment performance: audiobooks exhibit better text-audio correlation than TED talks. Our results highlight the limitations of text-based emotion expression in TTS systems: performance depends on genre, is mostly limited to Valence, and more context fails to improve emotional alignment between text and speech.

Emotion Alignment Between Text and Speech is Limited: A Cross-Modal Study

The significance of tasks entrusted to LLM-based research assistants (agents) and the associated societal risks are increasing each year. Agents are being explored in critical domains such as medicine, finance, law, infrastructure, and other sensitive applications that require system transparency and high user trust. The quality of these agents is typically evaluated by accuracy, sometimes extended to partial correctness. In this position paper, we argue that this focus on outcomes is insufficient as it can obscure risky agent behaviours such as skipping important steps, hallucinating tool use, relying on outdated parametric knowledge and other means of bypassing recommended processes. Our core position is that a holistic agent evaluation must include process evaluation, especially for critical applications. We conduct a small-scale study to assess the feasibility of automatic process evaluation, present a proof-of-concept compliance score, analyse use cases of bad and good behaviours, and offer recommendations for a more holistic evaluation of agents.

Process Evaluation for Agentic Systems

Professionals working in technical domain typically hand-draw (on whitebpoard, paper, etc.) technical diagrams (e.g., flowcharts, block diagrams, etc.) during discussions, however, later if they want to edit these, it needs to be drawn from scratch. Modern day VLMs have made tremendous progress in image understanding but these struggle when it comes to understanding technical diagrams. One way to overcome this problem is to fine-tune on real world hand-drawn images, but it is not practically possible to generate large number of such images. In this resource paper, we introduce a large synthetically generated corpus (reflective of real world images) for training VLMs and subsequently evaluate VLMs on a smaller corpus of hand-drawn images (with the help of humans). We introduce several new self-supervision tasks for training and perform extensive experiments with various baseline models and fine-tune Llama 3.2 11B-instruct model on synthetic images on these tasks to obtain LLama-VL-TUG, which significantly improves the ROUGE-L performance of Llama 3.2 11B-instruct by 2.14x and achieves best all round performance across all baseline models. On real-world images, human evaluation reveals that we achieve minimum compilation errors across all baselines in 7 out of 8 diagram types and improve average F1 score of Llama 3.2 11B-instruct by 6.97x.

TechING: Towards Real World Technical Image Understanding via VLMs

In real clinical practice, clinicians must sift through noisy and often conflicting information, progressively gathering and sequencing evidence before reaching conclusions. However, existing tuning methods for medical AI models are typically monologue-based — that is, models are fine-tuned on static question-answering (QA) tasks or medical articles, which fail to reflect the interactive and iterative nature of clinical reasoning. To bridge this gap, we introduce muddymaze, a benchmark designed to expose the limitations of current monologue-based tuning, and construct a large dialogue dataset of 22.2k doctor–patient interactions that capture stepwise diagnostic reasoning validated by medical experts. Building on those, we propose dialogue-tuning, a new fine-tuning paradigm that captures the internal reasoning dynamics unfolding across interactions. To assess the effectiveness of our approach, we evaluated dialogue-tuned models on muddymaze, where they outperform monologue-tuned baselines (e.g., MedQA) by +16.1% in one-round and +4.1% in multi-round evidence ranking, while maintaining or even improving accuracy on standard medical QA benchmarks (e.g., PubMedQA). These results indicate that dialogue-tuning not only enhances reasoning robustness and evidence integration but also preserves the factual precision of traditional QA performance.

Dialogue is Better Than Monologue: Instructing Meidcal LLMs via Strategic Conversations

Retrieval-augmented generation (RAG) is a common technique for grounding language model outputs in domain-specific information. However, RAG is often challenged by reasoning-intensive question-answering (QA), since common retrieval methods like cosine similarity maximize relevance at the cost of introducing redundant content, which can reduce information recall. To address this, we introduce Diversity-Focused Retrieval-Augmented Generation (DF-RAG) that systematically incorporates diversity into the retrieval step to improve performance on complex, reasoning-intensive QA benchmarks. DF-RAG builds upon the Maximal Marginal Relevance framework to select information chunks that are both relevant to the query and maximally dissimilar from each other. A key innovation of DF-RAG is its ability to optimize the level of diversity for each query dynamically at test time without requiring any additional fine-tuning or prior information. We show that DF-RAG improves F1 performance on reasoning-intensive QA benchmarks by 4вЂ“10% over vanilla RAG using cosine similarity and also outperforms other established baselines. Furthermore, we estimate an Oracle ceiling of up to 18% absolute F1 gains over vanilla RAG, of which DF-RAG captures up to 91.3%.

DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation

Vision Language Models (VLMs) have demonstrated remarkable capabilities in processing multimodal data, but their advanced abilities also raise significant privacy concerns, particularly regarding Personally Identifiable Information (PII) leakage. While relevant research has been conducted on single-modal language models to some extent, the vulnerabilities in the multimodal setting have yet to be fully investigated. Our work assesses these emerging risks and introduces a concept-guided mitigation approach. By identifying and modifying the model's internal states associated with PII-related content, our method guides VLMs to refuse PII-sensitive tasks effectively and efficiently, without requiring re-training or fine-tuning. We also address the current lack of multimodal PII datasets by constructing various ones that simulate real-world scenarios. Experimental results demonstrate the method can achieve on average 93.3% refusal rate for various PII-related tasks with minimal impact on unrelated model performances. We further examine the mitigation's performance under various conditions to show the adaptability of our proposed method.

Defeating Cerberus: Privacy-Leakage Mitigation in Vision Language Models

Large Language Models (LLMs) have enabled remarkable progress in natural language processing, yet their high computational and memory demands pose challenges for deployment in resource-constrained environments. Although recent low-rank decomposition methods offer a promising path for structural compression, they often suffer from accuracy degradation, expensive calibration procedures, and result in inefficient model architectures that hinder real-world inference speedups. In this paper, we propose FLAT-LLM, a fast and accurate, training-free structural compression method based on fine-grained low-rank transformations in the activation space. Specifically, we reduce the hidden dimension by transforming the weights using truncated eigenvectors computed via head-wise Principal Component Analysis, and employ a greedy budget redistribution strategy to adaptively allocate ranks across decoders. FLAT-LLM achieves efficient and effective weight compression without recovery fine-tuning, which could complete the calibration within a few minutes. Evaluated across 5 models and 11 datasets, FLAT-LLM outperforms structural pruning baselines in generalization and downstream performance, while delivering inference speedups over decomposition-based methods.

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Adversarial Decoding: Generating Readable Documents for Adversarial Objectives

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SafetyBench: Evaluating the Safety of Large Language Models