Austria

Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks. However, developing robust agents presents significant challenges: substantial engineering overhead, lack of standardized components, and insufficient evaluation frameworks for fair comparison. We introduce Agent Graph-based Orchestration for Reasoning and Assessment (AGORA), a flexible and extensible framework that addresses these challenges through three key contributions: (1) a modular architecture with a graph-based workflow engine, efficient memory management, and clean component abstraction; (2) a comprehensive suite of reusable agent algorithms implementing state-of-the-art reasoning approaches; and (3) a rigorous evaluation framework enabling systematic comparison across multiple dimensions. Through extensive experiments on mathematical reasoning and multimodal tasks, we evaluate various agent algorithms across different LLMs, revealing important insights about their relative strengths and applicability. Our results demonstrate that while sophisticated reasoning approaches can enhance agent capabilities, simpler methods like Chain-of-Thought often exhibit robust performance with significantly lower computational overhead. AGORA not only simplifies language agent development but also establishes a foundation for reproducible agent research through standardized evaluation protocols.
We made a demo video at: https://www.youtube.com/watch?v=WRH-F1zegKI. The comparison agent of algorithms is also available at https://huggingface.co/spaces/omlab/open-agent-leaderboard. Source code of AGORA can be found at https://github.com/om-ai-lab/OmAgent.

ACL 2025

Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research

language reasoning

agent evaluation

multimodal large language model

large language model

agent

## 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*

EMNLP 2025

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

demo

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Dialects suffer from the scarcity of computational textual resources as they exist predominantly in spoken rather than written form and exhibit remarkable geographical diversity. Collecting dialect data and subsequently integrating it into current language technologies present significant obstacles. Gamification has been proven to facilitate remote data collection processes with great ease and on a substantially wider scale. This paper introduces Dia-Lingle, a gamified interface aimed to improve and facilitate dialectal data collection tasks such as corpus expansion and dialect labelling. The platform features two key components: the first challenges users to rewrite sentences in their dialects, identifies them through a classifier and solicits feedback, and the other one asks users to match sentences to their geographical locations. Dia-Lingle combines active learning with gamified difficulty levels, strategically encouraging prolonged user engagement while efficiently enriching the dialect corpus. Usability evaluation shows that our interface demonstrates high levels of user satisfaction. We provide the link to Dia-Lingle: https://dia-lingle.ivia.ch/, and demo video: https://youtu.be/0QyJsB8ym64.

Dia-Lingle: A Gamified Interface for Dialectal Data Collection

Automated Alignment refers to a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention. However, it faces challenges such as algorithmic diversity and excessively convoluted workflows. We present AutoAlign, an open-source toolkit that offers:
(1) a unified framework integrating mainstream automated algorithms through a consistent interface, and
(2) an accessible workflow supporting one-click execution for prompt synthesis, automatic alignment signal construction, and iterative model training. Our toolkit enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components. It includes implementations for both highly efficient inference and training, as well as low-resource training. By standardizing automated alignment methodologies and providing accessible implementations, AutoAlign lowers the barriers to building customized aligned models and supports academic research.

AutoAlign: Get Your LLM Aligned with Minimal Annotations

As large language models (LLMs) continue to advance, there is a growing urgency to enhance the interpretability of their internal knowledge mechanisms. Consequently, many interpretation methods have emerged, aiming to unravel the knowledge mechanisms of LLMs from various perspectives. However, current interpretation methods differ in input data formats and interpreting outputs. The tools integrating these methods are only capable of supporting tasks with specific inputs, significantly constraining their practical applications. To address these challenges, we present an open-source **Know**ledge **M**echanisms **R**evealer&**I**nterpreter (**Know-MRI**) designed to analyze the knowledge mechanisms within LLMs systematically. Specifically, we have developed an extensible core module that can automatically match different input data with interpretation methods and consolidate the interpreting outputs. It enables users to freely choose appropriate interpretation methods based on the inputs, making it easier to comprehensively diagnose the model's internal knowledge mechanisms from multiple perspectives. Our code is available at https://github.com/nlpkeg/Know-MRI. We also provide a demonstration video on https://youtu.be/OiP4T3MC518.

Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models

The present popularity of generative language models has amplified interest in interactive methods to guide model outputs. Prompt refinement is considered one of the most effective means to influence output among these methods. We identify several challenges associated with prompting large language models, categorized into data- and model-specific, linguistic, and socio-linguistic challenges. A comprehensive examination of model outputs, including runner-up candidates and their corresponding probabilities, is needed to address these issues. The beam search tree, the prevalent algorithm to sample model outputs, can inherently supply this information. Consequently, we leverage an interactive visual method for investigating the beam search tree, facilitating analysis of the decisions made by the model during generation. Our explorative approach validates existing results and offers additional insights.

Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges

We introduce *Dialz*, a Python library for advancing research on steering vectors for open-source LMs. Steering vectors allow users to modify activations at inference time to amplify or weaken a 'concept', e.g. honesty or positivity, providing a more powerful alternative to prompting or fine-tuning. Dialz supports a diverse set of tasks, including creating contrastive pair datasets, computing and applying steering vectors, and visualizations. Unlike existing libraries, Dialz emphasizes modularity and usability, enabling both rapid prototyping and in-depth analysis. We demonstrate how Dialz can be used to reduce harmful outputs such as stereotypes, while also providing insights into model behaviour across different layers. We release Dialz with full documentation, tutorials, and support for popular open-source models to encourage further research in safe and controllable language generation. Dialz enables faster research cycles and facilitates insights into model interpretability, paving the way for safer, more transparent, and more reliable AI systems.

Dialz: A Python Toolkit for Steering Vectors

In this paper, we present LiDARR (**Li**nking **D**ocument **A**MRs with **R**eferents **R**esolvers), a web tool for semantic annotation at the document level using the formalism of Abstract Meaning Representation (AMR). LiDARR streamlines the creation of comprehensive knowledge graphs from natural language documents through semantic annotation. The tool features a visualization and interactive user interface, transforming document-level AMR annotation into an models-facilitated verification process. This is achieved through the integration of an AMR-to-surface alignment model and a coreference resolution model. Additionally, we incorporate PropBank rolesets into LiDARR to extend implicit roles in annotated AMR, allowing implicit roles to be linked through the coreference chains via AMRs.

LiDARR: Linking Document AMRs with Referents Resolvers

As large language models (LLMs) are gradually integrated into human life, assessing their underlying values becomes essential for understanding their risks and alignment with specific cultural preferences. Despite growing efforts, current value evaluation methods face two key challenges. C1. Evaluation Validity: Static benchmarks fail to reflect intended values or yield informative results due to data contamination or ceiling effect. C2. Result Interpretation: They typically reduce the pluralistic and often incommensurable values to one-dimensional scores, which hinders users from gaining meaningful insights and guidance. To address these challenges, we present Value Compass Leaderboard, the first interactive platform specially devised for comprehensive value diagnosis of LLMs. It (1) grounds evaluations in multiple basic value systems from social science; (2) develops a generative evolving evaluation schema that automatically and dynamically creates real-world test items that co-evolve with ever-advancing LLMs; (3) offers multi-faceted result interpretation, including: (i) fine-grained scores and case studies across 27 value dimensions for 33 leading LLMs, (ii) customized comparisons, and (iii) visualized analysis of LLMs' alignment with cultural values. We hope Value Compass Leaderboard. serves as a navigator for further enhancing LLMs' safety and alignment, further benefiting their responsible and adaptive development.

Value Compass Leaderboard: A Comprehensive, Generative and Self-Evolving Platform for LLMs' Value Evaluation

The computational complexity of large language model (LLM) inference significantly constrains their deployment efficiency on edge devices. In contrast, small language models offer faster decoding and lower resource consumption but often suffer from degraded response quality and heightened susceptibility to hallucinations. To address this trade-off, collaborative decoding, in which a large model assists in generating critical tokens, has emerged as a promising solution. This paradigm leverages the strengths of both model types by enabling high-quality inference through selective intervention of the large model, while maintaining the speed and efficiency of the smaller model. In this work, we present a novel collaborative decoding inference system that allows small models to perform on-device inference while selectively consulting a cloud-based large model for critical token generation. Remarkably, the system achieves a 60\% performance gain on CommonsenseQA using only a 0.5B model on an M1 MacBook, with under 7\% of tokens generation uploaded to the large model in the cloud.

Token Level Routing Inference System for Edge Devices

Step-by-step reasoning is crucial for solving complex visual tasks, yet existing approaches lack a comprehensive framework for evaluating this capability and do not emphasize step-wise problem-solving. To this end, we propose a comprehensive framework for advancing multi-step visual reasoning in large multimodal models (LMMs) through three key contributions. First, we introduce a Visual Reasoning Chain Benchmark, the most comprehensive benchmark for multi-step visual reasoning, covering eight diverse categories and over 4k reasoning steps. This enables rigorous evaluation of LMMs' ability to reason accurately and interpretably across multiple steps. Second, we propose a fine-grained reasoning metric that evaluates correctness and logical coherence at each step, providing deeper insights beyond traditional accuracy metrics. Third, we introduce LlamaV-o1, a state-of-the-art multimodal reasoning model trained using a multi-step curriculum learning approach. LlamaV-o1 is optimized for structured, step-by-step reasoning and significantly outperforms existing open-source models. It surpasses Llava-CoT with a 3.8% absolute gain across six benchmarks, achieving an average score of 67.3 while being 5x
faster during inference scaling. Our benchmark, model, and code is available at https://github.com/mbzuai-oryx/LlamaV-o1.

LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs

Large language models (LLMs) now exhibit near human-level performance in various tasks, but their performance drops drastically after a handful of high-resource languages due to the imbalance in pre-training data. Inspired by the human process of second language acquisition, particularly code-switching---the practice of language alternation in a conversation---we propose code-switching curriculum learning (CSCL) to enhance cross-lingual transfer for LLMs. CSCL mimics the stages of human language learning by progressively training models with a curriculum consisting of 1) token-level code-switching, 2) sentence-level code-switching, and 3) monolingual corpora. Using Qwen 2 as our underlying model, we demonstrate the efficacy of the CSCL in improving language transfer to Korean, achieving significant performance gains compared to monolingual continual pre-training methods. Ablation studies reveal that both token- and sentence-level code-switching significantly enhance cross-lingual transfer and that curriculum learning amplifies these effects. We also extend our findings into various languages, including Japanese (high-resource) and Indonesian (low-resource), and using two additional models (Gemma 2 and Phi 3.5). We further show that CSCL mitigates spurious correlations between language resources and safety alignment, presenting a robust, efficient framework for more equitable language transfer in LLMs. We observe that CSCL is effective for low-resource settings where high-quality, monolingual corpora for language transfer are hardly available.

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Dia-Lingle: A Gamified Interface for Dialectal Data Collection

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