Morocco

Analogical reasoning is an essential aspect of human cognition. In this paper, we summarize key theory about the processes underlying analogical reasoning from the cognitive science literature and relate it to current research in natural language processing. While these processes can be easily linked to concepts in NLP, they are generally not viewed through a cognitive lens. Furthermore, we show how these notions are relevant for several major challenges in NLP research, not directly related to analogy solving. This may guide researchers to better optimize relational understanding in text, as opposed to relying heavily on entity-level similarity.

EACL 2026

Modelling analogies and analogical reasoning: connecting cognitive science theory and NLP research.

analogy

cognitive science

reasoning

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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. 

Answering complex questions that require numerical reasoning over financial documents is challenging due to the diverse and scattered nature of relevant information. While large language models (LLMs) excel at financial reasoning, their enterprise deployment is often limited by cost and latency. Small language models (SLMs) present a cost-effective alternative but need to be fine-tuned with high-quality, domain-specific question-answer (QA) data. Acquiring such data requires manual expert annotation, presenting a bottleneck to the wider application of SLMs. This work introduces a modular, scalable end-to-end agentic pipeline that extracts and selects relevant content from unstructured financial documents and then generates QA pairs from the selected content for SLM fine-tuning. Compared to the same models trained on previous manually generated data for the task, one of the models trained on our pipeline-produced synthetic data achieved competitive in-distribution performance, and all tested models demonstrated superior generalization. The framework thus demonstrates considerable potential to accelerate the deployment of smaller, cost-effective models by reducing manual data creation efforts.

Synthesizing question answering data from financial documents: An End-to-End Multi-Agent Approach

The legal systems have a hierarchical structure, and a higher-level law often authorizes a lower-level law to implement detailed provisions, which is called delegation. When interpreting legal texts with delegation, readers must repeatedly consult the lower-level laws that stipulate the detailed provisions, imposing a substantial workload. Therefore, it is necessary to develop a system that enables readers to instantly refer to relevant laws in delegation. However, manually annotating delegation is difficult because it requires extensive legal expertise, careful reading of numerous legal texts, and continuous adaptation to newly enacted laws. In this study, we focus on Japanese law and develop a two-stage pipeline system for automatic delegation annotation. First, we extract keywords that indicate delegation using a named entity recognition approach. Second, we identify the delegated provision corresponding to each keyword as an entity disambiguation task. In our experiments, the proposed system demonstrates sufficient performance to assist manual annotation in practice.

Toward Automatic Delegation Extraction in Japanese Law

Automated feature engineering (AutoFE) liberates data scientists from the burden of manual feature construction. The rich semantic information of datasets has been underutilized in many existing AutoFE works. We present PromptFE, a novel AutoFE framework that leverages large language models (LLMs) to automatically construct features in a compact string format and generate semantic explanations based on dataset descriptions. By learning the performance of constructed features in context, the LLM iteratively improves feature construction. We demonstrate through experiments on real-world datasets the superior performance of PromptFE over state-of-the-art methods. We verify the impact of dataset semantic information and provide comprehensive study on the LLM-based feature construction process.

PromptFE: Automated Feature Engineering by Prompting

There has been considerable interest in using surprisal from Transformer-based language models (LMs) as predictors of human sentence processing difficulty. Recent work has observed an inverse scaling relationship between Transformers' per-word estimated probability and the predictive power of their surprisal estimates on reading times, showing that LMs with more parameters and trained on more data are less predictive of human reading times. However, these studies focused on predicting latency-based measures. Tests on brain imaging data have not shown a trend in any direction when using a relatively small set of LMs, leaving open the possibility that the inverse scaling phenomenon is constrained to latency data. This study therefore conducted a more comprehensive evaluation using surprisal estimates from 17 pre-trained LMs across three different LM families on two functional magnetic resonance imaging (fMRI) datasets. Results show that the inverse scaling relationship between models' per-word estimated probability and model fit on both datasets still obtains, resolving the inconclusive results of previous work and indicating that this trend is not specific to latency-based measures.

Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly

Intrinsic evaluation metrics for conditional language models, such as perplexity or bits-per-character, are widely used in both mono- and multilingual settings. These metrics are rather straightforward to use and compare in monolingual setups, but rest on a number of assumptions in multilingual setups. One such assumption is that comparing the perplexity of CLMs on parallel sentences is indicative of their quality since the information content (here understood as the semantic meaning) is the same. However, the metrics are inherently measuring information content in the information-theoretic sense. We make this and other such assumptions explicit and discuss their implications. We perform experiments with six metrics on two multi-parallel corpora both with mono- and multilingual models. Ultimately, we find that current metrics are not universally comparable. We look at the form-meaning debate to provide some explanation for this.

Form and Meaning in Intrinsic Multilingual Evaluations

Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) is an increasingly explored approach for combining the reasoning capabilities of large language models with the structured evidence of knowledge graphs. However, current evaluation practices fall short: existing benchmarks often include questions that can be directly answered using existing triples in KG, making it unclear whether models perform reasoning or simply retrieve answers directly. Moreover, inconsistent evaluation metrics and lenient answer matching criteria further obscure meaningful comparisons. In this work, we introduce a general method for constructing benchmarks, together with an evaluation protocol, to systematically assess KG-RAG methods under knowledge incompleteness. Our empirical results show that current KG-RAG methods have limited reasoning ability under missing knowledge, often rely on internal memorization, and exhibit varying degrees of generalization depending on their design.

What Breaks Knowledge Graph based RAG? Empirical Insights into Reasoning under Incomplete Knowledge

Extending large language models to low-resource languages is essential for global accessibility, but training separate models per language is prohibitively expensive. Mixture-of-Experts (MoE) architectures address this by adding sparse language-specific parameters, but determining how many experts each layer needs remains an open question. Current approaches allocate experts based on layer-level similarity, yet language processing exhibits fine-grained specialization at individual neurons. We propose NeuronMoE, a method that analyzes language-specific neurons across all transformer components to guide expert allocation per layer based on empirically measured cross-lingual neuron diversity. Applied to Llama-3.2-3B for low-resource languages (Greek, Turkish, and Hungarian), this approach achieves approximately 40% average parameter reduction while matching the performance of the LayerMoE baseline. We find that low-resource language experts independently develop neuron specialization patterns mirroring the high-resource language, which are concentrated in early and late layers. This reveals potential universal architectural principles in how multilingual models organize linguistic knowledge. Our approach generalizes across architectures, as validated on Qwen, and shows that allocation strategy matters more than total expert count.

NeuronMoE: Efficient Cross-Lingual Extension via Neuron-Guided Mixture-of-Experts

Multimodal large language models (MLLMs) have shown impressive capabilities in vision-language tasks such as reasoning segmentation, where models generate segmentation masks based on textual queries. While prior work has primarily focused on perturbing image inputs, semantically equivalent textual paraphrases—crucial in real-world applications where users express the same intent in varied ways—remain underexplored. To address this gap, we introduce a novel adversarial paraphrasing task: generating grammatically correct paraphrases that preserve the original query meaning while degrading segmentation performance. To evaluate the quality of adversarial paraphrases, we develop a comprehensive automatic evaluation protocol validated with human studies. Furthermore, we introduce SPARTA—a black-box, sentence-level optimization method that operates in the low-dimensional semantic latent space of a text autoencoder, guided by reinforcement learning. SPARTA achieves significantly higher success rates, outperforming prior methods by up to 2x on both the ReasonSeg and LLMSeg-40k datasets. We use SPARTA and competitive baselines to assess the robustness of advanced reasoning segmentation models. We reveal that they remain vulnerable to adversarial paraphrasing—even under strict semantic and grammatical constraints. All code and data will be released publicly upon acceptance.

SPARTA: Evaluating Reasoning Segmentation Robustness through Black-Box Adversarial Paraphrasing in Text Autoencoder Latent Space

In this work, we propose an entity type-specific and knowledge-augmented token classification framework designed to improve encoder models' performance on recipe texts. Our empirical analysis shows that this approach achieves state-of-the-art (SOTA) results on 5 out of 7 benchmark recipe datasets, significantly outperforming traditional token classification methods. We introduce a novel methodology leveraging curated domain-specific knowledge contexts to guide encoder models such as BERT and RoBERTa, which we refer to as RecipeBERT-KA and RecipeRoBERTa-KA. Additionally, we release a newly reprocessed entity type-specific and knowledge-enriched dataset that merges seven widely used food datasets, making it the largest annotated food-related dataset to date. Comparative analysis with SOTA large language models (GPT-4o, Mistral-7B, LLaMA 3-13B and LLaMA 3-70B) highlights the practical advantages of our smaller and specialised models. Finally, we analyse the impact of the different knowledge contexts, our models' potential for transfer learning, the effect of combining the datasets and scenarios where traditional token classification may still perform competitively, offering nuanced insight into method selection.

Knowledge Augmentation Enhances Token Classification for Recipe Understanding

Counterspeech generation has emerged as a promising approach to combat online hate speech, with recent work focusing on controlling attributes used in counterspeech, such as strategies or intents. While these attributes are often evaluated automatically using classifiers, a key goal of this evaluation is to compare the performance of different generation models. However, the validity of such evaluation results is questionable when the classifiers themselves have only modest performance. This paper examines the automatic evaluation of counterspeech attributes using a multi-attribute counterspeech dataset containing 2,728 samples. We investigate when automatic evaluation can be trusted for model comparison and address the limitations of current evaluation methodologies. We make concrete recommendations for how to perform classifier validation before model evaluation. Our classifier validation results demonstrate that even limited classifiers can produce trustworthy model rankings. Therefore, we argue that when comparing counterspeech generation models, a classifier's ability to rank generation models is a more direct measure of its practical utility than traditional classification metrics, e.g., accuracy and F1.

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