United States

In multi-agent scenarios, the ability to anticipate and respond to opponents is essential, particularly in environments involving adversarial and collaborative interactions. In this paper, we introduce Explicit Models of Opponents (EMO) based on Large Language Models (LLMs), enabling agents to better predict and adapt to diverse, dynamic multi-agent interactions. Unlike traditional methods that often simplify multi-agent interactions using a single opponent model, EMO constructs an individual model for each opponent and aligns these models working in synergy through a bi-level feedback-refinement framework. We test EMO alongside several reasoning methods in multi-player deduction games, where agents must infer hidden information about their opponents. The results show that EMO significantly enhances agents&#39; decision-making, outperforming traditional single-model approaches. Our findings demonstrate that EMO can be a powerful tool for enhancing LLM-based agents in complex multi-agent systems.

NAACL 2025

LLM-Based Explicit Models of Opponents for Multi-Agent Games

llm-agent

opponent modeling

multi-agent

In multi-agent scenarios, the ability to anticipate and respond to opponents is essential, particularly in environments involving adversarial and collaborative interactions. In this paper, we introduce Explicit Models of Opponents (EMO) based on Large Language Models (LLMs), enabling agents to better predict and adapt to diverse, dynamic multi-agent interactions. Unlike traditional methods that often simplify multi-agent interactions using a single opponent model, EMO constructs an individual model for each opponent and aligns these models working in synergy through a bi-level feedback-refinement framework. We test EMO alongside several reasoning methods in multi-player deduction games, where agents must infer hidden information about their opponents. The results show that EMO significantly enhances agents' decision-making, outperforming traditional single-model approaches. Our findings demonstrate that EMO can be a powerful tool for enhancing LLM-based agents in complex multi-agent systems.

poster

### 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. 
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*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

The Knowledge Graph Completion (KGC) task aims to infer the missing entity from an incomplete triple. Existing embedding-based methods rely solely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities. On the other hand, text-based methods struggle with the semantic gap between KG triples and natural language. Apart from triples, entity contexts (e.g., labels, descriptions, aliases) also play a significant role in augmenting KGs. To address these limitations, we propose KGR3, a context-enriched framework for KGC. KGR3 is composed of three modules. Firstly, the Retrieval module gathers supporting triples from the KG, collects plausible candidate answers from a base embedding model, and retrieves context for each related entity. Then, the Reasoning module employs a large language model to generate potential answers for each query triple. Finally, the Re-ranking module combines candidate answers from the two modules mentioned above, and fine-tunes an LLM to provide the best answer. Extensive experiments on widely used datasets demonstrate that KGR3 consistently improves various KGC methods. Specifically, the best variant of KGR3 achieves absolute Hits@1 improvements of 12.3% and 5.6% on the FB15k237 and WN18RR datasets.

Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion

Recently, mobile AI agents have gained increasing attention. Given a task, mobile AI agents can interact with mobile devices in multiple steps and finally form a GUI flow that solves the task. However, existing agents tend to focus on most task-relevant elements at each step, leading to local optimal solutions and ignoring the overall GUI flow. To address this issue, we constructed a training dataset called MobileReach, which breaks the task into page reaching and operation subtasks. Furthermore, we propose ReachAgent, a two-stage framework that focuses on improving its task-completion abilities. It utilizes the page reaching and page operation subtasks, along with reward-based preference GUI flows, to further enhance the agent. Experimental results show that ReachAgent significantly improves the Intersection over Union (IoU) Accuracy and Text Accuracy by 7.12% and 7.69% on the step-level and 4.72% and 4.63% on the task-level compared to the SOTA agent. Our data and code will be released upon acceptance.

ReachAgent: Enhancing Mobile Agent via Page Reaching and Operation

Hypernetworks are a class of meta-networks that generate weights for main neural networks. Their unique parameter spaces necessitate exploring suitable optimization strategies to enhance performance, especially for language models. However, a comprehensive investigation into optimization strategies for hypernetworks remains absent. To address this gap, we analyze the loss landscape of hypernetworks and propose that restart optimization strategies can improve their performance for language models. We find that hypernetworks have inherently more complicated loss landscapes compared to conventional networks due to their distinct parameter spaces. Consequently, a restart strategy that periodically resets the learning rate can facilitate better convergence for hypernetworks. Through experiments on instruction tuning and multi-task training, we demonstrate that the restart strategy consistently enhances the performance of hypernetworks for language models, often more effectively than for conventional deep neural networks. Our findings highlight the importance of tailored optimization techniques to unlock the full potential of hypernetworks in natural language processing tasks.

Enhancing Language Model Hypernetworks with Restart: A Study on Optimization

Large language models, despite their remarkable success in recent years, still exhibit severe cultural bias. Therefore, in this paper, we introduce CultureInstruct, a large-scale instruction-tuning dataset designed to reduce cultural bias in LLMs. CultureInstruct is constructed with an automatic pipeline, utilizing public web sources and a specialized LLM to generate instruction. Our data comprises 430K instructions, ranging from classic NLP tasks to complex reasoning. CultureInstruct also covers 11 most relevant topics to cultural knowledge, making it highly diverse. Our experiments show that fine-tuning LLMs with CultureInstruct results in consistent improvements across three types of cultural benchmarks, including (i) general cultural knowledge, (ii) human opinions and values, and (iii) linguistic cultural bias. Our best model, Qwen2-Instruct 72B + CultureInstruct, outperforms GPT-4o Mini and GPT-4o with 18.47% and 13.07% average relative improvements on cultural benchmarks.

CultureInstruct: Curating Multi-Cultural Instructions at Scale

Recent advancements in large multimodal models (LMMs) have showcased impressive code generation capabilities, primarily evaluated through image-to-code benchmarks. However, these benchmarks are limited to specific visual programming scenarios where the logic reasoning and the multimodal understanding capacities are split apart. To fill this gap, we propose ScratchEval, a novel benchmark designed to evaluate the visual programming reasoning ability of LMMs. ScratchEval is based on Scratch, a block-based visual programming language widely used in children's programming education. By integrating visual elements and embedded programming logic, ScratchEval requires the model to process both visual information and code structure, thereby comprehensively evaluating its programming intent understanding ability. Our evaluation approach goes beyond the traditional image-to-code mapping and focuses on unified logical thinking and problem-solving abilities, providing a more comprehensive and challenging framework for evaluating the visual programming ability of LMMs. ScratchEval not only fills the gap in existing evaluation methods, but also provides new insights for the future development of LMMs in the field of visual programming.

ScratchEval: Are GPT-4o Smarter than My Child? Evaluating Large Multimodal Models with Visual Programming Challenges

Generative language models have shown significant potential in single-turn Text-to-SQL. However, their performance does not extend equivalently to multi-turn Text-to-SQL. This is primarily due to generative language models' inadequacy in handling the complexities of context information and dynamic schema linking in multi-turn interactions. In this paper, we propose a framework named Track-SQL, which enhances generative language models with dual-extractive modules designed to track schema and contextual changes in multi-turn Text-to-SQL. Specifically, Track-SQL incorporates a \emph{Semantic-enhanced Schema Extractor} and a \emph{Schema-aware Context Extractor}. Experimental results demonstrate that Track-SQL achieves state-of-the-art performance on the SparC and CoSQL datasets. Furthermore, detailed ablation studies reveal that Track-SQL significantly improves execution accuracy in multi-turn interactions by 7.1\% and 9.55\% on these datasets, respectively. Our implementation will be open-sourced at \url{https://github.com/DMIRLAB-Group/Track-SQL}.

Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL

Workflows play a crucial role in enhancing enterprise efficiency by orchestrating complex processes with multiple tools or components. However, hand-crafted workflow construction requires expert knowledge, presenting significant technical barriers. Recent advancements in Large Language Models (LLMs) have improved the generation of workflows from natural language instructions (aka NL2Workflow), yet existing single LLM agent-based methods face performance degradation on complex tasks due to the need for specialized knowledge and the strain of task-switching. To tackle these challenges, we propose WorkTeam, a multi-agent NL2Workflow framework comprising a supervisor, orchestrator, and filler agent, each with distinct roles that collaboratively enhance the conversion process. As there are currently no publicly available NL2Workflow benchmarks, we also introduce the HW-NL2Workflow dataset, which includes 3,695 real-world business samples for training and evaluation. Experimental results show that our approach significantly increases the success rate of workflow construction, providing a novel and effective solution for enterprise NL2Workflow services.

WorkTeam: Constructing Workflows from Natural Language with Multi-Agents

Chinese Search Query Spell Correction is a task designed to autonomously identify and correct typographical errors within queries in the search engine. Despite the availability of comprehensive datasets like Microsoft Speller and Webis, their monolingual nature and limited scope pose significant challenges in evaluating modern pre-trained language models such as BERT and GPT. To address this, we introduce \textbf{QSpell 250K}, a large-scale benchmark specifically developed for Chinese Query Spelling Correction. QSpell 250K offers several advantages: 1) It contains over 250K samples, which is ten times more than previous datasets. 2) It covers a broad range of topics, from formal entities to everyday colloquialisms and idiomatic expressions. 3) It includes both Chinese and English, addressing the complexities of code-switching. Each query undergoes three rounds of high-fidelity annotation to ensure accuracy. Our extensive testing across three popular models demonstrates that QSpell 250K effectively evaluates the efficacy of representative spelling correctors. We believe that QSpell 250K will significantly advance spelling correction methodologies. The accompanying data and code will be made publicly available.

QSpell 250K: A Large-Scale, Practical Dataset for Chinese Search Query Spell Correction

Automating planning with LLMs presents transformative opportunities for traditional industries, yet remains underexplored. In commercial construction, the complexity of automated scheduling often requires manual intervention to ensure precision. We propose CONSTRUCTA, a novel framework leveraging LLMs to optimize construction schedules in complex projects like semiconductor fabrication. CONSTRUCTA addresses key challenges by: (1) integrating construction-specific knowledge through static RAG; (2) employing context-sampling techniques inspired by architectural expertise to provide relevant input; and (3) deploying Construction DPO to align schedules with expert preferences using RLHF. Experiments on proprietary data demonstrate performance improvements of +42.3% in missing value prediction, +79.1% in dependency analysis, and +28.9% in automated planning compared to baseline methods, showcasing its potential to revolutionize construction workflows and inspire domain-specific LLM advancements.

CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models

Assuring the data quality of tabular datasets is essential for the efficiency of the diverse tabular downstream tasks (like summarization and fact-checking). Data-wrangling tasks effectively address the challenges associated with structured data processing to improve the quality of tabular data. Traditional statistical methods handle numeric data efficiently but often fail to understand the semantic context of the textual data in tables. Deep learning approaches are resource-intensive, requiring task and dataset-specific training. Addressing these shortcomings, we present an automated system that leverages LLMs to generate executable code for data-wrangling tasks like missing value imputation, error detection, and error correction. Our system aims to identify inherent patterns in the data while leveraging external knowledge, effectively addressing both memory-independent and memory-dependent tasks.

LLM-Based Explicit Models of Opponents for Multi-Agent Games

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Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion

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