China

Materials science research requires multi-step reasoning and precise material informatics retrieval, where minor errors can propagate into significant failures in downstream experiments. Despite their general success, Large Language Models (LLMs) often struggle with hallucinations, handling domain-specific data effectively (e.g., crystal structures), and integrating experimental workflows. To address these challenges, we introduce LLaMP, a hierarchical multi-agent framework designed to emulate the materials science research workflow. The high-level supervisor agent decomposes user requests into sub-tasks and coordinates with specialized assistant agents. These assistant agents handle domain-specific tasks, such as retrieving and processing data from the Materials Project (MP) or conducting simulations as needed. This pipeline facilitates iterative refinement of material property retrieval and enables the simulation of real-world research workflows. To ensure reliability, we propose a novel metric combining uncertainty and confidence estimates to evaluate the self-consistency of responses from LLaMP and other methods. Our experiments with real-world materials science tasks demonstrate LLaMP’s superior performance in material property retrieval, crystal structure editing, and annealing molecular dynamics simulations using pre-trained interatomic potentials. Unlike prior work focused solely on material property prediction or discovery, LLaMP serves as a foundation for autonomous materials research by combining grounded informatics and enabling iterative experimental processes.

EMNLP 2025

LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval

large language models (llms)

material science

planning

agent

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

poster

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Large visual-language models (LVLMs) have demonstrated remarkable performance in visual-language tasks. However, object hallucination remains a significant challenge for LVLMs. Existing studies attribute object hallucinations in LVLMs mainly to linguistic priors and data biases. We further explore the causes of object hallucinations from the perspective of frequency domain and reveal that insufficient frequency information in images amplifies these linguistic priors, increasing the likelihood of hallucinations. To mitigate this issue, we propose the Multi-Frequency Contrastive Decoding (MFCD) method, a simple yet training-free approach that removes the hallucination distribution in the original output distribution, which arises from LVLMs neglecting the high-frequency information or low-frequency information in the image input. Without compromising the general capabilities of LVLMs, the proposed MFCD effectively mitigates the object hallucinations in LVLMs. Our experiments demonstrate that MFCD significantly mitigates object hallucination across diverse large-scale vision-language models, without requiring additional training or external tools. In addition, MFCD can be applied to various LVLMs without modifying model architecture or requiring additional training, demonstrating its generality and robustness. Codes are available at https://anonymous.4open.science/r/mfcd

Multi-Frequency Contrastive Decoding: Alleviating Hallucinations for Large Vision-Language Models

Social media platforms provide an ideal environment to spread misinformation, where social bots can accelerate the spread. This paper explores the interplay between social bots and misinformation on the Sina Weibo platform. We construct a large-scale dataset that includes annotations for both misinformation and social bots. From the misinformation perspective, the dataset is multimodal, containing 11,393 pieces of misinformation and 16,416 pieces of verified information. From the social bot perspective, this dataset contains 65,749 social bots and 345,886 genuine accounts, annotated using a weakly supervised annotator. Extensive experiments demonstrate the comprehensiveness of the dataset, the clear distinction between misinformation and real information, and the high quality of social bot annotations. Further analysis illustrates that: (i) social bots are deeply involved in information spread; (ii) misinformation with the same topics has similar content, providing the basis of echo chambers, and social bots would amplify this phenomenon; and (iii) social bots generate similar content aiming to manipulate public opinions.

How Do Social Bots Participate in Misinformation Spread? A Comprehensive Dataset and Analysis

Languages vary along a wide variety of dimensions. In Natural Language Processing (NLP), it is useful to know how ``distant'' languages are from each other, so that we can inform NLP models about these differences or predict good transfer languages. Furthermore, it can inform us about how diverse language samples are. However, there are many different perspectives on how distances across languages could be measured, and previous work has predominantly focused on either intuition or a single type of distance, like genealogical or typological distance. %In fact, often intuition, or an extremely generic method like number of language family is used. Therefore, we propose DistaLs, a toolkit that is designed to provide users with easy access to a wide variety of language distance measures. We also propose a filtered subset, which contains less redundant and more reliable features. DistaLs is designed to be accessible for a variety of use cases, and offers a Python, CLI, and web interface. It is easily updateable, and available as a pip package. Finally, we provide a case-study in which we use DistaLs to measure correlations of distance measures with performance on four different morphosyntactic tasks.

DistaLs: a Comprehensive Collection of Language Distance Measures

We introduce LINGCONV, an interactive toolkit for paraphrase generation enabling finegrained control over 40 specific lexical, syntactic, and discourse linguistic attributes. Users can directly manipulate target attributes using sliders, and with automatic imputation for unspecified attributes, simplifying the control process. Our adaptive Quality Control mechanism employs iterative refinement guided by line search to precisely steer the generation towards target attributes while preserving semantic meaning, overcoming limitations associated with fixed control strengths. Applications of LINGCONV include enhancing text accessibility by adjusting complexity for different literacy levels, enabling personalized communication through style adaptation, providing a valuable tool for linguistics and NLP research, and facilitating second language learning by tailoring text complexity. The system is available at https://mohdelgaar-lingconv.hf.space, with a demo video at https://youtu.be/wRBJEJ6EALQ

LingConv: An Interactive Toolkit for Controlled Paraphrase Generation with Linguistic Attribute Control

Large language models (LLMs) have shown impressive performance on general-purpose tasks, yet adapting them to specific domains remains challenging due to the scarcity of high-quality domain data. Existing data synthesis tools often struggle to extract reliable fine-tuning data from heterogeneous documents effectively. To address this limitation, we propose Easy Dataset, a unified framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface (GUI). Specifically, Easy Dataset allows users to easily configure text extraction models and chunking strategies to transform raw documents into coherent text chunks. It then leverages a persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs. Throughout the pipeline, a human-in-the-loop visual interface facilitates the review and refinement of intermediate outputs to ensure data quality. Experiments on a financial question-answering task show that fine-tuning LLMs on the synthesized dataset significantly improves domain-specific performance while preserving general knowledge. The source code and installable package are available at https://github.com/ConardLi/easy-dataset and have garnered over 10,000 GitHub stars.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents

Large Language Model (LLM) agents produce rich, multi-step trajectories that interleave observations, internal reasoning, and tool actions. However, most evaluation pipelines focus solely on end-task success, leaving the agent’s decision-making process opaque and poorly understood. We introduce AgentDiagnose, an open-source, modular framework for diagnosing agent trajectories. The present release fully supports the web domain, and AgentDiagnose is architect as an extensible, open platform with compatibility for most agent trajectories. AgentDiagnose consists of (i) an evaluation module that quantifies five core agentic competencies—backtracking & exploration, task decomposition, observation reading, self-verification, and objective quality—and (ii) a visualization module that highlights trajectory semantics through t-SNE action embeddings, interactive word clouds, and state-transition timelines. On a set of 30 manually annotated trajectories, our automatic metrics achieve a mean Pearson correlation of 0.57 with human judgments, rising to 0.78 for task decomposition. Furthermore, filtering the 46k-example NNetNav-Live dataset with AgentDiagnose and fine-tuning a Llama-3.1-8B model on the top 6k trajectories improves WebArena success rates by 0.98, despite using only 13% of the original data. AgentDiagnose thus serves as both a diagnostic lens for agent analysis and a practical tool for curating higher-quality training data. The toolkit and demo are publicly available.

AgentDiagnose: An Open Toolkit for Diagnosing LLM Agent Trajectories

Despite Bengali being the sixth most spoken language in the world, handwritten text recognition (HTR) systems for Bengali remain severely underdeveloped. The complexity of Bengali script—featuring conjuncts, diacritics, and highly variable handwriting styles—combined with a scarcity of annotated datasets makes this task particularly challenging. We present **GraDeT-HTR**, a resource-efficient Bengali handwritten text recognition system based on a decoder-only transformer architecture. To address the unique challenges of Bengali script, we integrate a grapheme-based tokenizer and demonstrate that it significantly improves recognition accuracy compared to conventional subword tokenizers. Our model is pretrained on large-scale synthetic data and fine-tuned on real human-annotated samples, achieving state-of-the-art performance on multiple benchmark datasets.

GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer

The ARR Responsible NLP Research checklist website states that the "checklist is designed to encourage best practices for responsible research, addressing issues of research ethics, societal impact and reproducibility." Answering the questions is an opportunity for authors to reflect on their work and make sure any shared scientific assets follow best practices. Ideally, considering a checklist before submission can favorably impact the writing of a research paper. However, previous research has shown that self-reported checklist responses don’t always accurately represent papers. In this work, we introduce ConfReady, a retrieval-augmented generation (RAG) application that can be used to empower authors to reflect on their work and assist authors with conference checklists. To evaluate checklist assistants, we curate a dataset of 1975 ACL checklist responses, analyze problems in human answers, and benchmark RAG and Large Language Model (LM) based systems on a evaluation subset. Our code is released under the AGPL-3.0 license on GitHub, with documentation covering the user interface and Python package.

ConfReady: A RAG based Assistant and Dataset for Conference Checklist Responses

The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image (T2I) models. However, to generate high-quality images, users must still craft detailed prompts specifying scene, style, and context—often through multiple rounds of refinement. We propose PromptSculptor, a novel multi-agent framework that automates this iterative prompt optimization process. Our system decomposes the task into four specialized agents that work collaboratively to transform a short, vague user prompt into a comprehensive, refined prompt. By leveraging Chain-of-Thought (CoT) reasoning, our framework effectively infers hidden context and enriches scene and background details. To iteratively refine the prompt, a self-evaluation agent aligns the modified prompt with the original input, while a feedback-tuning agent incorporates user feedback for further refinement. Experimental results demonstrate that PromptSculptor significantly enhances output quality and reduces the number of iterations needed for user satisfaction. Moreover, its model-agnostic design allows seamless integration with various T2I models, paving the way for industrial applications.

PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization

Accurate clinical coding is essential for healthcare documentation, billing, and decision-making. While prior work shows that off-the-shelf LLMs struggle with this task, evaluations based on exact match metrics often overlook errors where predicted codes are hierarchically close but incorrect. Our analysis reveals that such hierarchical misalignments account for a substantial portion of LLM failures. We show that lightweight interventions, including prompt engineering and small-scale fine-tuning, can improve accuracy without the computational overhead of search-based methods. To address hierarchically near-miss errors, we introduce clinical code verification as both a standalone task and a pipeline component. To mitigate the limitations in existing datasets, such as incomplete evidence and inpatient bias in MIMIC, we release an expert double-annotated benchmark of outpatient clinical notes with ICD-10 codes. Our results highlight verification as an effective and reliable step toward improving LLM-based medical coding.

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Multi-Frequency Contrastive Decoding: Alleviating Hallucinations for Large Vision-Language Models

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Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents