Morocco

Logical table-to-text generation aims to generate natural language descriptions that fluently and precisely describe the given table with both surface-level and logic-level fidelity. Although large language models (LLMs) have demonstrated strong capabilities in plain text, their proficiency in interpreting and reasoning tabular data is still limited. In this paper, we are the first to comprehensively explore the performance of various LLMs in the logical table-to-text generation task. However, we find that existing LLMs are difficult to achieve satisfactory results in this task. Even worse, existing prompt strategies cannot cope with complex non-chain logical reasoning scenarios on tables. To address the challenges mentioned above, we constructed a new table-related instruction dataset called LogicTableInstruct and instruction-tuned the open-source LLM on this dataset, resulting in the specialized LLM (LogicTableLLaMA-3.1-8B) for table-related tasks. We also introduced a novel reasoning method, Logic Tree-of-Program (LogicToP), to improve the logical reasoning ability of the LLMs on tables. Our extensive experiments on various LLMs demonstrated that LogicToP can effectively improve the performance of LLMs on this task. Our LogicTableLLaMA-3.1-8B model in the 5-shot LogicToP setting achieves state-of-the-art results on the Logic2Text dataset. The code and data will be released to boost future work on table-related tasks.

EACL 2026

LogToP: Logic Tree-of-Program with Table Instruction-tuned LLMs for Controlled Logical Table-to-Text Generation

table instruction tuning

llms

data-to-text generation

## 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. 

Retrieval-augmented generation (RAG) systems rely on retrieval models for identifying relevant contexts and answer generation models for utilizing those contexts. However, retrievers exhibit imperfect recall and precision, limiting downstream performance. We introduce RAG-RL, an answer generation model trained for multi-hop question answering (MHQA) to not only generate answers but also to identify and cite relevant information from larger sets of retrieved contexts, shifting some of the burden of identifying relevant documents from the retriever to the answer generator. Our approach uses curriculum learning, where models are trained across retrieval settings with varying levels of noise. Our experiments show that training samples with fewer distractor documents enable models to acquire citation and reasoning skills with greater sample efficiency and generalizability, demonstrating strong model performance even as the number of irrelevant passages increases. We benchmark our methods on three open-domain MHQA datasets and report significant gains in answer and citation accuracy. Furthermore, our experiments provide empirical insights into how simpler training samples can give models stronger signals for learning specific skills (e.g., citation generation) and how different components of post-training (e.g., training set construction, rule-based rewards, training sample ordering, etc.) impact final model performance.

Tackling Distractor Documents in Multi-Hop QA with Reinforcement and Curriculum Learning

We propose MaskLoRA, a plug-and-play masking mechanism that turns PEFT's low-rank subspace into a faithful, compute-aware token selector. Instead of relying on attention entropy or gradients, MaskLoRA scores tokens by the LoRA-induced representation shift and learns differentiable gates with an objective combining task loss, expected L 0 sparsity, prediction consistency (KL), and a subspace concentration term. We provide a bound showing the KL between full and masked predictions is controlled by the sum of dropped scores, implying top- k selection minimizes the bound. Across encoders on GLUE and SQuAD, MaskLoRA matches full-model accuracy while yielding 1.3 -- 2.6x end-to-end speedups; on long-context NarrativeQA/GovReport it attains 1.5 -- 3.6x with small but nonzero degradation (e.g., 0.4 at =70%, up to 1.2 at =50%, and up to 2.5 at 30%). Faithfulness improves over strong baselines (sufficiency 99.2%, necessity 59.3%, lower KL at =50%), and energy falls accordingly (GPU Joules-per-correct 3.28 1.34 at 30%). The method is KV-cache compatible, rank- and budget-controllable, and robust under OOD stress (HANS, adversarial sentiment, noisy SQuAD; at moderate budgets (50%) 0.9 abs. drop at matched budgets). Qualitative analyses show layer-wise retention concentrating at semantic bottlenecks and class-separated h i embeddings. MaskLoRA offers accuracy-matched speedups with interpretable rationales and practical deployment hooks.

MaskLoRA: LowвЂ‘Rank SubspaceвЂ“Induced Token Masking for Efficient and Faithful Language Models

A key barrier to interpreting large language models is polysemanticity, where neurons activate for multiple unrelated concepts. Sparse autoencoders (SAEs) have been proposed to mitigate this issue by transforming dense activations into sparse, more interpretable features. While prior work suggests that SAEs promote monosemanticity, no quantitative comparison has examined how concept activation distributions differ between SAEs and their base models. This paper provides the first systematic evaluation of SAEs against base models through activation distribution lens. We introduce a fine-grained concept separability score based on the JensenвЂ“Shannon distance, which captures how distinctly a neuron's activation distributions vary across concepts. Using two large language models (Gemma-2-2B and DeepSeek-R1) and multiple SAE variants across five datasets (including word-level and sentence-level), we show that SAEs reduce polysemanticity and achieve higher concept separability. To assess practical utility, we evaluate concept-level interventions using two strategies: full neuron masking and partial suppression. We find that, compared to base models, SAEs enable more precise concept-level control when using partial suppression. Building on this, we propose Attenuation via Posterior Probabilities (APP), a new intervention method that uses concept-conditioned activation distributions for targeted suppression. APP achieves the smallest perplexity increase while remaining highly effective at concept removal.

Evaluating Sparse Autoencoders for Monosemantic Representation

Access control is a cornerstone of secure computing, yet large language models often blur role boundaries by producing unrestricted responses. We study role-conditioned refusals, focusing on the LLM's ability to adhere to access control policies by answering when authorized and refusing when not. To evaluate this behavior, we created a novel dataset that extends the Spider and BIRD text-to-SQL datasets, both of which have been modified with realistic PostgreSQL role-based policies at the table and column levels. We compare three designs: (i) zero or few-shot prompting, (ii) a two-step generator-verifier pipeline that checks SQL against policy, and (iii) LoRA fine-tuned models that learn permission awareness directly. Across multiple model families, explicit verification (the two-step framework) improves refusal precision and lowers false permits. At the same time, fine-tuning achieves a stronger balance between safety and utility (i.e., when considering execution accuracy). Longer and more complex policies consistently reduce the reliability of all systems. We release RBAC-augmented datasets and code.

Role-Conditioned Refusals: Evaluating Access Control Reasoning in Large Language Models

Modern human labor is characterized by specialization; we train for years and develop particular tools that allow us to perform well across a variety of tasks. Similarly, specialized AI agents with task-specific tools or architectures often fail to generalize beyond their intended scope. In this work, we ask: *what is the minimal set of general tools for achieving generalizability across diverse domains?* We propose OpenHands-Versa, a single-agent system with a modest number of general tools like code execution, search engine, web browser and multimodal file viewer, for three practical domains: software engineering, deep research, and web browsing. Notably, OpenHands-Versa demonstrates superior or competitive performance over task-specific specialized agents on three challenging benchmarks: SWE-Bench Multimodal, GAIA, and The Agent Company, with absolute improvements in success rate of **9.1**, **1.3**, and **9.1 points**, respectively. Thus, our minimal *single-agent* system can achieve strong generalization indicating that specialist agents provide no practical benefit. Furthermore, we find that specialist multi-agent systems do not generalize beyond their intended scope. These findings establish OpenHands-Versa as a strong baseline for future research.

Coding Agents with Multimodal Browsing are Generalist Problem Solvers

Reinforcement learning (RL) has re-emerged as a natural approach for training interactive LLM agents in real-world environments. However, directly applying the widely used Group Relative Policy Optimization (GRPO) algorithm to multi-turn tasks exposes notable limitations, particularly in scenarios requiring long-horizon reasoning. To address these challenges, we investigate more stable and effective advantage estimation strategies, especially for multi-turn settings. We first explore Proximal Policy Optimization (PPO) as an alternative and find it to be more robust than GRPO. To further enhance PPO in multi-turn scenarios, we introduce turn-PPO, a variant that operates on a turn-level MDP formulation, as opposed to the commonly used token-level MDP. Our results on the WebShop and Sokoban datasets demonstrate the effectiveness of turn-PPO, both with and without long reasoning components.

Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs

Modern language models are trained on large scrapes of the Web, containing millions of personal information (PI) instances, many of which language models (LMs) memorize, increasing privacy risks. In this work, we develop the regexes and rules (R&R) detector suite to detect email addresses, phone numbers, and IP addresses, which outperforms the best regex-based PI detectors. On a manually curated set of 483 instances of PI, we measure memorization and parroting: finding that 13.6% are parroted verbatim by the Pythia-6.9b model, i.e. when the model is prompted with the tokens that precede the PI in the original document, greedy decoding generates the entire PI span exactly. We expand this analysis to study models of varying sizes (160M-6.9B) and timesteps of pretraining (70k-143k iterations) on the Pythia model suite and find that both model size and amount of pretraining are positively correlated with memorization. Even the smallest model, Pythia-160m, parrots 2.7% of the instances exactly. Consequently, we strongly recommend that pretraining datasets be aggressively filtered and anonymized to minimize PI parroting.

Personal Information Parroting in Language Models

Large Language Models (LLMs) are increasingly deployed across multilingual applications that handle sensitive data, yet their scale and linguistic variability introduce major privacy risks. Mostly evaluated for English, this paper investigates how language structure affects privacy leakage in LLMs trained on English, Spanish, French, and Italian medical corpora. We quantify six linguistic indicators and evaluate three attack vectors: extraction, counterfactual memorization, and membership inference. Results show that privacy vulnerability scales with linguistic redundancy and tokenization granularity: Italian exhibits the strongest leakage, while English shows higher membership separability. In contrast, French and Spanish display greater resilience due to higher morphological complexity. Overall, our findings provide the first quantitative evidence that language matters in privacy leakage, underscoring the need for language-aware privacy-preserving mechanisms in LLM deployments.

The Model's Language Matters: A Comparative Privacy Analysis of LLMs

Mathematical reasoning remains one of the most challenging domains for large language models (LLMs), requiring not only linguistic understanding but also structured logical deduction and numerical precision. While recent LLMs demonstrate strong general-purpose reasoning abilities, their mathematical competence across diverse languages remains underexplored. Existing benchmarks primarily focus on English or a narrow subset of high-resource languages, leaving significant gaps in assessing multilingual and cross-lingual mathematical reasoning. To address this, we introduce MATHMIST, a parallel multilingual benchmark for mathematical problem solving and reasoning. MATHMIST encompasses 2,890 parallel Bangla-English gold standard artifacts, totaling approximately 30K aligned question--answer pairs across thirteen languages, representing an extensive coverage of high-, medium-, and low-resource linguistic settings. The dataset captures linguistic variety, multiple types of problem settings, and solution synthesizing capabilities. We systematically evaluate a diverse suite of models, including open-source small and medium LLMs, proprietary systems, and multilingual-reasoning-focused models under zero-shot, chain-of-thought (CoT), perturbated reasoning, and code-switched reasoning paradigms. Our results reveal persistent deficiencies in LLMs' ability to perform consistent and interpretable mathematical reasoning across languages, with pronounced degradation in low-resource settings. 

MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning

Large Language Models (LLMs) are increasingly used in creative tasks. However, a formal and systematic assessment of creativity capabilities in LLMs is still missing, which limits our understanding of how capable LLMs are in generating content that is diverse and contextually relevant. The widely used Divergent Association Task (DAT) focuses on novelty, ignoring appropriateness, a core component of creativity. We evaluate a range of state-of-the-art LLMs on DAT and show that their results on the task are lower than those of two baselines that do not possess any creative abilities, highlighting its critical shortcomings. Grounded in human creativity theory, which defines creativity as the combination of novelty and appropriateness, we introduce the Conditional Divergent Association Task (CDAT). CDAT conditions novelty on cue appropriateness, separating noise from creativity better than DAT, while remaining simple and objective. Under CDAT, smaller model families often show most creativity, whereas advanced families favor appropriateness at lower novelty; We hypothesize training and alignment likely shift models along this frontier, making outputs more appropriate but less creative. We will release the dataset and code to ensure reproducibility.

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Tackling Distractor Documents in Multi-Hop QA with Reinforcement and Curriculum Learning

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