Austria

The recent progress in Vision-Language Models (VLMs) has broadened the scope of multimodal applications. However, evaluations often remain limited to functional tasks, neglecting abstract dimensions such as personality traits and human values. To address this gap, we introduce Value-Spectrum, a novel Visual Question Answering (VQA) benchmark aimed at assessing VLMs based on Schwartz&#39;s value dimensions that capture core human values guiding people’s preferences and actions. We design a VLM agent pipeline to simulate video browsing and construct a vector database comprising over 50,000 short videos from TikTok, YouTube Shorts, and Instagram Reels. These videos span multiple months and cover diverse topics, including family, health, hobbies, society, technology, etc. Benchmarking on Value-Spectrum highlights notable variations in how VLMs handle value-oriented content. Beyond identifying VLMs&#39; intrinsic preferences, we also explore the ability of VLM agents to adopt specific personas when explicitly prompted, revealing insights into the adaptability of the model in role-playing scenarios. These findings highlight the potential of Value-Spectrum as a comprehensive evaluation set for tracking VLM preferences in value-based tasks and abilities to simulate diverse personas. The complete code and data are available at https://github.com/Jeremyyny/Value-Spectrum.

ACL 2025

Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media Contexts

model preference

multi-modality

vision and language

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The recent progress in Vision-Language Models (VLMs) has broadened the scope of multimodal applications. However, evaluations often remain limited to functional tasks, neglecting abstract dimensions such as personality traits and human values. To address this gap, we introduce Value-Spectrum, a novel Visual Question Answering (VQA) benchmark aimed at assessing VLMs based on Schwartz's value dimensions that capture core human values guiding people’s preferences and actions. We design a VLM agent pipeline to simulate video browsing and construct a vector database comprising over 50,000 short videos from TikTok, YouTube Shorts, and Instagram Reels. These videos span multiple months and cover diverse topics, including family, health, hobbies, society, technology, etc. Benchmarking on Value-Spectrum highlights notable variations in how VLMs handle value-oriented content. Beyond identifying VLMs' intrinsic preferences, we also explore the ability of VLM agents to adopt specific personas when explicitly prompted, revealing insights into the adaptability of the model in role-playing scenarios. These findings highlight the potential of Value-Spectrum as a comprehensive evaluation set for tracking VLM preferences in value-based tasks and abilities to simulate diverse personas. The complete code and data are available at https://github.com/Jeremyyny/Value-Spectrum.

technical paper

### 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)

One of the research focuses of large language models (LLMs) is the ability to generate action plans. Recent studies have revealed that the performance of LLMs can be significantly improved by integrating external tools. Based on this, we propose a benchmark framework called PlanningArena, which aims to simulate real application scenarios and provide a series of apps and API tools that may be involved in the actual planning process. This framework adopts a modular task structure and combines user portrait analysis to evaluate the ability of LLMs in correctly selecting tools, logical reasoning in complex scenarios, and parsing user information. In addition, we deeply diagnose the task execution effect of LLMs from both macro and micro levels. The experimental results show that even the most outstanding GPT-4o and DeepSeekV3 models only achieved a total score of 56.5% and 41.9% in PlanningArena, respectively, indicating that current LLMs still face challenges in logical reasoning, context memory, and tool calling when dealing with different structures, scenarios, and their complexity. Through this benchmark, we further explore the path to optimize LLMs to perform planning tasks.

PlanningArena: A Modular Benchmark for Multidimensional Evaluation of Planning and Tool Learning

Various evaluation metrics have been proposed for Grammatical Error Correction (GEC), but many, particularly reference-free metrics, lack explainability. This lack of explainability hinders researchers from analyzing the strengths and weaknesses of GEC models and limits the ability to provide detailed feedback for users. To address this issue, we propose attributing sentence-level scores to individual edits, providing insight into how specific corrections contribute to the overall performance. For the attribution method, we use Shapley values, from cooperative game theory, to compute the contribution of each edit. Experiments with existing sentence-level metrics demonstrate high consistency across different edit granularities and show approximately 70% alignment with human evaluations. In addition, we analyze biases in the metrics based on the attribution results, revealing trends such as the tendency to ignore orthographic edits.

Improving Explainability of Sentence-level Metrics via Edit-level Attribution for Grammatical Error Correction

We present FlagEvalMM, an open-source evaluation framework designed to comprehensively assess multimodal models across a diverse range of vision-language understanding and generation tasks, such as visual question answering, text-to-image/video generation, and image-text retrieval. We decouple model inference from evaluation through an independent evaluation service, thus enabling flexible resource allocation and seamless integration of new tasks and models. Moreover, FlagEvalMM utilizes advanced inference acceleration tools (e.g., vLLM, SGLang) and asynchronous data loading to significantly enhance evaluation efficiency. Extensive experiments show that FlagEvalMM offers accurate and efficient insights into model strengths and limitations, making it a valuable tool for advancing multimodal research. The framework is publicly accessible at https://github.com/flageval-baai/FlagEvalMM, with a demonstration video available at https://youtu.be/L7EtacjoM0k.

FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation

Pretrained language models have significantly advanced the state of the art in generating distributed representations of text. However, they do not account for the wide variety of available expert-generated language resources and lexicons that explicitly encode linguistic/domain knowledge. Such lexicons can be paired with learned embeddings to further enhance NLP prediction and linguistic inquiry. In this work we present Textagon, a Python package for generating parallel representations for text based on predefined lexicons and selecting representations that provide the most information. We discuss the motivation behind the software, its implementation, as well as two case studies for its use to demonstrate operational utility.

Textagon: Boosting Language Models with Theory-guided Parallel Representations

Generative large language models (LLMs) have become crucial for modern NLP research and applications across various languages. However, the development of foundational models specifically tailored to the Russian language has been limited, primarily due to the significant computational resources required. This paper introduces the GigaChat family of Russian LLMs, available in various sizes, including base models and instruction-tuned versions. We provide a detailed report on the model architecture, pre-training process, and experiments to guide design choices. In addition, we evaluate their performance on Russian and English benchmarks and compare GigaChat with multilingual analogs. The paper presents a system demonstration of the top-performing models accessible via an API, a Telegram bot, and a Web interface. Furthermore, we have released three open GigaChat models in open source, with the aim of expanding NLP research opportunities and supporting the development of industrial solutions for the Russian language.

GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture

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.

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

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.

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PlanningArena: A Modular Benchmark for Multidimensional Evaluation of Planning and Tool Learning

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