India

Large Language Models (LLMs) are increasingly used to support scientific writing, but their tendency to produce hallucinated content threatens academic reliability. Existing benchmarks have addressed hallucination detection in general-domain tasks, such as fact-checking or question answering, but they do not reflect the fine-grained, domain-specific needs of scientific communication. We introduce SciHallu, a dataset for identifying hallucinations in academic text at three levels of granularity: token, sentence, and paragraph. To establish a reliable ground truth, we select source passages from research papers published prior to the widespread adoption of LLMs. Our dataset includes both hallucinated and non-hallucinated paragraph instances, constructed through controlled perturbations at varying levels of noise and validated by human annotators. A rationale is paired with each instance, explaining the nature of the modification. SciHallu covers multiple academic fields, such as Computer Science, Health Sciences, and Humanities and Social Sciences. It is built using a model-guided annotation pipeline, followed by expert human validation. We evaluate state-of-the-art LLMs on both binary and fine-grained classification tasks, revealing challenges in detecting subtle hallucinations. SciHallu supports the development of context-aware systems for more trustworthy scientific content generation.

IJCNLP-AACL 2025

SciHallu: A Multi-Granularity Hallucination Detection Dataset for Scientific Writing

scientific writing

large language model

hallucination

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

poster

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

We present **JPharmatron**, a Japanese domain-specific large language model (LLM) for the pharmaceutical field, developed through continual pre-training on two billion Japanese pharmaceutical tokens and eight billion English biomedical tokens. For rigorous evaluation, we introduce **JPharmaBench**, a benchmark suite consisting of three new benchmarks: YakugakuQA, based on national pharmacist licensing exams; NayoseQA, which tests cross-lingual synonym and terminology normalization; and SogoCheck, a novel task involving cross-document consistency checking.
We evaluate our model against open-source medical LLMs and commercial models, including GPT-4o. Experimental results show that **JPharmatron** outperforms existing open models and achieves competitive performance with commercial ones.
Interestingly, even GPT-4o performs poorly on SogoCheck, suggesting that cross-sentence consistency reasoning remains an open challenge.
**JPharmatron** enables secure and local model deployment for pharmaceutical tasks, where privacy and legal constraints limit the use of closed models. Besides, **JPharmaBench** offers a reproducible framework for evaluating Japanese pharmaceutical natural language processing. Together, they demonstrate the feasibility of practical and cost-efficient language models for Japanese healthcare and pharmaceutical sectors.
Our model, codes, and datasets are available on HuggingFace: https://huggingface.co/collections/EQUES/jpharmatron and https://huggingface.co/collections/EQUES/jpharmabench.

A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP

We present ThaiOCRBench, the first comprehensive benchmark for evaluating vision-language models (VLMs) on Thai text-rich visual understanding tasks. Despite recent progress in multimodal modeling, existing benchmarks predominantly focus on high-resource languages, leaving Thai underrepresented, especially in tasks requiring document structure understanding. ThaiOCRBench addresses this gap by offering a diverse, human-annotated dataset comprising 2,808 samples across 13 task categories. We evaluate a wide range of state-of-the-art VLMs in a zero-shot setting, spanning both proprietary and open-source systems. Results show a significant performance gap, with proprietary models (e.g., Gemini 2.5 Pro) outperforming open-source counterparts. Notably, fine-grained text recognition and handwritten content extraction exhibit the steepest performance drops among open-source models. Through detailed error analysis, we identify key challenges such as language bias, structural mismatch, and hallucinated content. ThaiOCRBench provides a standardized framework for assessing VLMs in low-resource, script-complex settings, and provides actionable insights for improving Thai-language document understanding.

ThaiOCRBench: A Task-Diverse Benchmark for Vision-Language Understanding in Thai

Factor analysis studies have shown that the primary dimensions of word meaning are Valence (V), Arousal (A), and Dominance (D). Existing lexicons such as the NRC VAD Lexicon, published in 2018, include VAD association ratings for words. Here, we present a complement to it, which has **human ratings of valence, arousal, and dominance for ∼10k English Multiword Expressions (MWEs) and their constituent words**. We also increase the coverage of unigrams, especially words that have become more common since 2018. In all, the new **NRC VAD Lexicon v2 now has entries for ∼10k MWEs and ∼25k words, in addition to the entries in v1**. We show that the associations are highly reliable. We use the lexicon to examine emotional characteristics of MWEs, including: 1. The degree to which MWEs (idioms, noun compounds, and verb particle constructions) exhibit strong emotionality; 2. The degree of emotional compositionality in MWEs. The lexicon enables a wide variety of research in NLP, Psychology, Public Health, Digital Humanities, and Social Sciences. The NRC VAD Lexicon v2 is freely available through the project web- page: http://saifmohammad.com/ WebPages/nrc-vad.html

Breaking Bad: Norms for Valence, Arousal, and Dominance for over 10k English Multiword Expressions

Large Language Models (LLMs) are increasingly treated as universal, general-purpose solutions across NLP tasks, particularly in English. But does this assumption hold for low-resource, morphologically rich languages such as Sanskrit? We address this question by comparing instruction-tuned and in-context-prompted LLMs with smaller task-specific encoder–decoder models on the Sanskrit poetry-to-prose conversion task. This task is intrinsically challenging: Sanskrit verse exhibits free word order combined with rigid metrical constraints, and its conversion to canonical prose (anvaya) requires multi-step reasoning involving compound segmentation, dependency resolution, and syntactic linearisation. This makes it an ideal testbed to evaluate whether LLMs can surpass specialised models.

For LLMs, we apply instruction fine-tuning on general-purpose models and design in-context learning templates grounded in Pāṇinian grammar and classical commentary heuristics. For task-specific modelling, we fully fine-tune a ByT5-Sanskrit Seq2Seq model. Our experiments show that domain-specific fine-tuning of ByT5-Sanskrit significantly outperforms all instruction-driven LLM approaches. Human evaluation strongly corroborates this result, with scores exhibiting high correlation with Kendall’s Tau scores.
Additionally, our prompting strategies provide an alternative to fine-tuning when domain-specific verse corpora are unavailable, and the task-specific Seq2Seq model demonstrates robust generalisation on out-of-domain evaluations.
Our code$^1$ and dataset$^2$ are publicly available.

Still Not There: Can LLMs Outperform Smaller Task-Specific Seq2Seq Models on the Poetry-to-Prose Conversion Task?

Progress in biomedical Named Entity Recognition (NER) and Entity Linking (EL) is currently hindered by a fragmented data landscape, a lack of resources for building explainable models, and the limitations of semantically-blind evaluation metrics. To address these challenges, we present MedPath, a large-scale and multi-domain biomedical EL dataset that builds upon nine existing expert-annotated EL datasets. In MedPath, all entities are 1) normalized using the latest version of the Unified Medical Language System (UMLS), 2) augmented with mappings to 62 other biomedical vocabularies and, crucially, 3) enriched with full ontological paths---i.e., from general to specific---in up to 11 biomedical vocabularies. MedPath directly enables new research frontiers in biomedical NLP, facilitating training and evaluation of semantic-rich and interpretable EL systems, and the development of the next generation of interoperable and explainable clinical NLP models.

MedPath: Multi-Domain Cross-Vocabulary Hierarchical Paths for Biomedical Entity Linking

Question Generation (QG) is central to information retrieval, education, and knowledge assessment, yet its progress is bottlenecked by unreliable and non-scalable evaluation practices. Traditional metrics fall short in structured settings like document-grounded QG, and human evaluation, while insightful, remains expensive, inconsistent, and difficult to replicate at scale. We introduce AURA-QG: an Automated, Unsupervised, Replicable Assessment pipeline that scores question sets using only the source document. It captures four orthogonal dimensions i.e., answerability, non-redundancy, coverage, and structural entropy, without needing reference questions or relative baselines. Our method is modular, efficient, and agnostic to the question generation strategy. Through extensive experiments across four domains i.e., car manuals, economic surveys, health brochures, and fiction, we demonstrate its robustness across input granularities and prompting paradigms. Chain-of-Thought prompting, which first extracts answer spans and then generates targeted questions, consistently yields higher answerability and coverage, validating the pipeline’s fidelity. The metrics also exhibit strong agreement with human judgments, reinforcing their reliability for practical adoption. The complete implementation of our evaluation pipeline is publicly available.

AURA-QG: Automated Unsupervised Replicable Assessment for Question Generation

Recent advances in large language models (LLMs) have significantly impacted data science workflows, giving rise to specialized data science agents designed to automate analytical tasks. Despite rapid adoption, systematic benchmarks evaluating the efficacy and limitations of these agents remain scarce. In this paper, we introduce a comprehensive benchmark specifically crafted to reflect real-world user interactions with data science agents by observing usage of our commercial applications. We evaluate three LLMs: Claude-4.0-Sonnet, Gemini-2.5-Flash, and OpenAI-o4-Mini across three approaches: zero-shot with context engineering, multi-step with context engineering, and with SmolAgent. Our benchmark assesses performance across a diverse set of eight data science task categories, additionally exploring the sensitivity of models to common prompting issues, such as data leakage and slightly ambiguous instructions. We further investigate the influence of temperature parameters on overall and task-specific outcomes for each model and approach. Our findings reveal distinct performance disparities among the evaluated models and methodologies, highlighting critical factors that affect practical deployment. The benchmark dataset and evaluation framework introduced herein aim to provide a foundation for future research of more robust and effective data science agents.

DSBC : Data Science task Benchmarking with Context engineering

Although Farsi is widely spoken, no comprehensive benchmark exists for assessing commonsense reasoning in language models. We therefore present \textbf{FarSense}, a 6‑task benchmark for Farsi covering True/False judgment,  multiple-choice questions, Explanation, Cause‑Effect inference, Counterfactual reasoning, and Knowledge Completion. Starting from Farsi‑Wikipedia, we filtered noise and retained ~4,210 passages, rewrote them into realistic daily scenarios, and derived the above tasks from each scenario. Scenario and task generation quality was first judged via native‑speaker annotations on outputs from five major LLMs—GPT‑4o, Gemini-2.5-Flash, Mistral-Large, Qwen‑Plus, and DeepSeek‑Chat. Gemini-2.5-Flash demonstrated the highest performance, leading to its use in generating a large-scale dataset, subsequently finalized through meticulous two-step human validation.  Using \textbf{FarSense}, we measured the commonsense ability of the same five flagship LLMs and also fine‑tuned six compact models (1B–24B parameters) before re‑evaluating them. To ensure broad applicability, task wording was designed to minimize dialectal, cultural, or religious bias. Experiments show that targeted fine‑tuning yields substantial gains, confirming \textbf{FarSense} as a reliable, openly licensed resource for advancing reproducible commonsense understanding research in Farsi NLP. We publicly release all code and data at https://github.com/KamyarZeinalipour/FarSense.

FarSense: A Comprehensive Commonsense Benchmark and Evaluation Framework for the Farsi Language

The rapid growth of social networks has led to a surge in social bots, which often disseminate low-quality content and may manipulate public opinion, posing threats to online security. Although recent GNN-based bot detection methods perform strongly, they still face two major challenges. First, deep GNNs are prone to over-smoothing: neighbor aggregation blends bot and human node representations, obscuring bot-specific features. Second, social graphs are dominated by human–human and human–bot connections, while direct bot–bot links are scarce, making it difficult for effective bot representations to propagate within GNNs. To address these issues, we propose a Topology-Aware Gated Graph Neural Network (\framework) to detect social bots. \framework~ employs topology-aware data augmentation to synthesize realistic bot nodes that preserve the original graph structure, mitigating class imbalance; it also introduces a hierarchical gating mechanism that restructures node embeddings into a tree format, selectively filtering noise and enhancing discriminative features. Experiments on three standard benchmark datasets show that \framework consistently surpasses leading baselines in highly imbalanced settings, delivering superior accuracy and robustness.

Topology-Aware Gated Graph Neural Network for Social Bot Detection

Using a sample of $25,000$ Bing Copilot conversations, we study how the agent responds to users of varying levels of domain expertise and the resulting impact on user experience along multiple dimensions. Our findings show that across a variety of topical domains, the agent largely responds at proficient or expert levels of expertise (77\% of conversations) which correlates with positive user experience regardless of the user's level of expertise. Misalignment, such that the agent responds at a level of expertise below that of the user, has a negative impact on overall user experience, with the impact more profound for more complex tasks. We also show that users engage more, as measured by the number of words in the conversation, when the agent responds at a level of expertise commensurate with that of the user. Our findings underscore the importance of alignment between users and AI when designing human-centered AI systems, to ensure satisfactory and productive interactions.

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