Morocco

This paper presents an evaluation framework for probing large language models&#39; linguistic knowledge of Indigenous languages of the Americas using zero- and few-shot prompting. The framework consists of three tasks: (1) language identification, (2) cloze completion of Spanish sentences supported by Indigenous-language translations, and (3) grammatical feature classification. We evaluate models from five major families (GPT, Gemini, DeepSeek, Qwen, and LLaMA) on 13 Indigenous languages, including Bribri, Guarani, and Nahuatl. The results show substantial variation across both languages and model families. While a small number of model-language combinations demonstrate consistently stronger performance across tasks, many others perform near chance, highlighting persistent gaps in current models&#39; abilities on Indigenous languages.

EACL 2026

Measuring Linguistic Competence of LLMs on Indigenous Languages of the Americas

indigenous languages

low resource languages

benchmarking

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
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The University of Texas at Austin 
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

This paper presents an evaluation framework for probing large language models' linguistic knowledge of Indigenous languages of the Americas using zero- and few-shot prompting. The framework consists of three tasks: (1) language identification, (2) cloze completion of Spanish sentences supported by Indigenous-language translations, and (3) grammatical feature classification. We evaluate models from five major families (GPT, Gemini, DeepSeek, Qwen, and LLaMA) on 13 Indigenous languages, including Bribri, Guarani, and Nahuatl. The results show substantial variation across both languages and model families. While a small number of model-language combinations demonstrate consistently stronger performance across tasks, many others perform near chance, highlighting persistent gaps in current models' abilities on Indigenous languages.

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. 

Despite the impressive performance of vision-language models (VLMs) on downstream tasks, their ability to understand and reason about causal relationships in visual inputs remains unclear. Robust causal reasoning is fundamental to solving complex high-level reasoning tasks, yet existing benchmarks often include a mixture of reasoning questions, and VLMs can frequently exploit object recognition and activity identification as shortcuts to arrive at the correct answers, making it challenging to truly assess their causal reasoning abilities. To bridge this gap, we introduce VQA-Causal and VCR-Causal, two new benchmarks specifically designed to isolate and rigorously evaluate VLMs' causal reasoning abilities. Our findings reveal that while VLMs excel in object and activity recognition, they perform poorly on causal reasoning tasks, often only marginally surpassing random guessing. Further analysis suggests that this limitation stems from a severe lack of causal expressions in widely used training datasets, where causal relationships are rarely explicitly conveyed. We additionally explore fine-tuning strategies with hard negative cases, showing that targeted fine-tuning can improve model's causal reasoning while maintaining generalization and downstream performance. Our study highlights a key gap in current VLMs and lays the groundwork for future work on causal understanding.

What's Missing in Vision-Language Models? Probing Their Struggles with Causal Order Reasoning

Recent advances in multimodal and speech-native large language models (LLMs) have delivered impressive speech recognition, translation, understanding, and question-answering capabilities for high-resource languages. However, African languages and non-native French or English accents remain dramatically underrepresented in benchmarks limiting the understanding and applicability of leading LLMs for millions of francophone and anglophone users in low-resource settings. We presents AfriVox, an open-source benchmark (including novel domain-specific and unscripted datasets) across 20 African languages, African-accented French, Arabic, and 100+ African English accents, contrasting leading multimodal speech LLMs with traditional unimodal automatic speech transcription (ASR) and translation (AST) models. Our analysis reveals significant language coverage variation, surprising LLM translation performance gains (e.g. Gemini), robustness concerns with unscripted speech, and substantial performance disparities for "supported" African languages. We profile the strengths, limitations, and language support of each model, and conduct the first targeted fine-tuning of a modern speech LLM (Qwen2.5-Omni) for three Nigerian languages, exceeding SOTA, and achieving up to 54% relative WER reduction and significant BLEU gains, offering practical guidance for implementers seeking to serve local language users.

AfriVox: Probing Multilingual and Accent Robustness of Speech LLMs

Current evaluation of German automatic text simplification (ATS) relies on general-purpose metrics such as SARI, BLEU, and BERTScore, which insufficiently capture simplification quality in terms of simplicity, meaning preservation, and fluency. While specialized metrics like LENS have been developed for English, corresponding efforts for German have lagged behind due to the absence of human-annotated corpora. To close this gap, we introduce DETECT, the first German-specific metric that holistically evaluates ATS quality across all three dimensions of simplicity, meaning preservation, and fluency, and is trained entirely on synthetic large language model (LLM) responses. Our approach adapts the LENS framework to German and extends it with (i) a pipeline for generating synthetic quality scores via LLMs, enabling dataset creation without human annotation, and (ii) an LLM-based refinement step for aligning grading criteria with simplification requirements. To the best of our knowledge, we also construct the largest German human evaluation dataset for text simplification to validate our metric directly. Experimental results show that DETECT achieves substantially higher correlations with human judgments than widely used ATS metrics, with particularly strong gains in meaning preservation and fluency. Beyond ATS, our findings highlight both the potential and the limitations of LLMs for automatic evaluation and provide transferable guidelines for general language accessibility tasks.

DETECT: Determining Ease and Textual Clarity of German Text Simplifications

LLMs, while outperforming humans in a wide range of tasks, can still fail in unanticipated ways. We focus on two pervasive failure modes: (i) hallucinations, where models produce incorrect information about the world, and (ii) the low-resource effect, where the models show impressive performance in high-resource languages like English but the performance degrades significantly in low-resource languages like Bengali. We study the intersection of these issues and ask: do hallucination detectors suffer from the low-resource effect? We conduct experiments on five tasks across three domains (factual recall, STEM, and Humanities). Experiments with four LLMs and three hallucination detectors reveal a curious finding: As expected, the task accuracies in low-resource languages experience large drops (compared to English). However, the drop in hallucination detection accuracy is often several times smaller than the drop in task accuracy. Our finding suggests that even in low-resource languages, the internal mechanisms of LLMs might encode valuable signals about their uncertainty.

Do LLM hallucination detectors suffer from low-resource effect?

Large language models (LLMs) increasingly rely on external tools and APIs to execute complex tasks specified in natural language. Evaluating such tool-calling capabilities in realistic enterprise settings is challenging: APIs are often proprietary, heterogeneous, and difficult to share, limiting reproducible benchmarks. To address this, we introduce Live API Bench, a comprehensive benchmark constructed by transforming NL2SQL datasets into interactive API environments. Our pipeline converts SQL queries from BIRD-SQL into executable API sequences across three formulations—SLOT, SEL, and REST—covering minimal general-purpose operations, domain-specific multi-step tasks, and function-oriented RESTful interactions, respectively. The benchmark spans 11 databases with over 2,500 invocable tools, paired with human-authored queries, ground-truth API sequences, and verified final answers. Live API Bench enables systematic evaluation of core challenges in tool use, including error handling, sequential reasoning, parameter generation, response parsing, and robustness across diverse domains. We evaluate 10 LLMs and 4 ReACT agents, observing low task completion rates (7–47%), which improve modestly to 50% under interactive agent settings, highlighting substantial scope for improving LLM tool-calling performance. We release all code and data associated with this paper.

Live API-Bench: 2500+ Live APIs for Testing Multi-Step Tool Calling

Despite the recent advancements in NLP with the advent of Large Language Models (LLMs), Entity Linking (EL) for historical texts remains challenging due to linguistic variation, noisy inputs, and evolving semantic conventions. Existing solutions either require substantial training data or rely on domain-specific rules that limit scalability. In this paper, we present MHEL-LLaMo (Multilingual Historical Entity Linking with Large Language MOdels), an unsupervised ensemble approach combining a Small Language Model (SLM) and an LLM. MHEL-LLaMo leverages a multilingual bi-encoder (BELA) for candidate retrieval and an instruction-tuned LLM for NIL prediction and candidate selection via prompt chaining. Our system uses SLM's confidence scores to discriminate between easy and hard samples, applying an LLM only for hard cases. This strategy reduces computational costs while preventing hallucinations on straightforward cases. We evaluate MHEL-LLaMo on four established benchmarks in six European languages (English, Finnish, French, German, Italian and Swedish) from the 19th and 20th centuries. Results demonstrate that MHEL-LLaMo outperforms state-of-the-art models without requiring fine-tuning, offering a scalable solution for low-resource historical EL. Our error analysis reveals that 41% of false predictions exhibit semantic proximity to ground truth entities, highlighting the LLM's accurate disambiguation of historical references.

It's All About the Confidence: An Unsupervised Approach for Multilingual Historical Entity Linking using Large Language Models

Test-time compute methods can significantly improve the reasoning capabilities and problem-solving accuracy of large language models (LLMs). However, these approaches require substantially more computational resources, with most compute wasted on exploring low-diversity branches where the model already exhibits high confidence. We observe that a small subset of uncertain reasoning steps has a disproportionately large impact on final prediction accuracy, and branching at these critical junctures tends to yield more diverse and higher-quality candidate reasoning steps. We propose Entropy-Gated Branching (EGB), which branches only at high-uncertainty steps and prunes expansions with a lightweight verifier. On mathematical and financial reasoning benchmarks, EGB improves accuracy by 22.6% over standard inference while operating 31%-75% faster across math benchmarks than test-time beam search with higher performance. Our results show that dynamic resource allocation during inference can substantially improve both efficiency and effectiveness, offering a more scalable pathway to enhanced LLM reasoning capabilities.

Entropy-Gated Branching for Efficient Test-Time Reasoning

This paper presents a novel task of extracting Latin fragments from mixed-language historical documents with varied layouts. We benchmark and evaluate the performance of large foundation models against a multimodal dataset of 724 annotated pages. The results demonstrate that reliable Latin detection with contemporary models is achievable. Our study provides the first comprehensive analysis of these models' capabilities and limits for this task.

Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark

Speculative decoding has emerged as a promising approach to accelerate Large Language Model inference. However, existing research has predominantly focused on 7B-70B parameters models, leaving a critical knowledge gap for small language models (1-2B parameters) that are increasingly important for edge computing and agentic AI systems. This paper presents the first comprehensive empirical study of speculative decoding techniques for small language models. We evaluate five distinct method categories across three representative model families and reveal that drafting overhead, rather than draft quality, becomes the primary bottleneck fundamentally limiting acceleration of small models. We demonstrate that traditional independent drafting fails completely due to the suboptimal architecture of available drafters, while self-drafting methods achieve meaningful acceleration only when employing sufficiently efficient draft modules. In contrast, retrieval-based methods with negligible computational overhead yield consistent gains. Based on these insights, we establish practical guidelines for effective small model acceleration.

An Empirical Study of Speculative Decoding for Small Language Models

Large language models are being rapidly applied across many fields such as healthcare, finance, transportation, and energy, among many others. These applications often involve analyzing time-series data alongside contextual information in the form of natural language to support informed decisions. However, current time-series models are limited in their ability to perform reasoning that involves both time-series and their textual content. In this work, we address this gap by introducing Chat-TS, a large language model (LLM) based framework designed to support reasoning over time series and textual data. Unlike traditional models, Chat-TS integrates time-series tokens into LLMs' vocabulary, enhancing its reasoning ability over both modalities without compromising core natural language capabilities. To support learning and evaluation, we contribute new datasets: the TS Instruct Training Dataset (pairing diverse time-series data with relevant text instructions and responses for instruction tuning), the TS Instruct Question and Answer (QA) Gold Dataset (multiple-choice questions to evaluate multimodal reasoning), and a TS Instruct Quantitative Probing Set (a small subset of TS Instruct QA reasoning tasks alongside math and decision-making questions for LLM evaluation). We design a training strategy to preserve the inherent reasoning capabilities of LLMs while augmenting them for time-series reasoning. Experiments show that Chat-TS achieves state-of-the-art performance in multimodal reasoning tasks by maintaining strong natural language proficiency while improving time-series reasoning.

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