United States

The rise of powerful large language models (LLMs) has spurred a new trend in building LLM-based autonomous agents for solving complex tasks, especially multi-agent systems. Despite the remarkable progress, we notice that existing works are heavily dependent on human-designed frameworks, which greatly limits the functional scope and scalability of agent systems. How to automatically extend the specialized agent to multi-agent systems to improve task-solving capability still remains a significant challenge. In this paper, we introduce EVOAGENT, a generic method to automatically extend specialized agents to multi-agent systems via the evolutionary algorithm, thereby improving the effectiveness of LLM-based agents in solving tasks. Specifically, we consider the existing agent frameworks as the initial individual and then apply a series of evolutionary operators (e.g., mutation, crossover, selection, etc.) to generate multiple agents with diverse settings. Experimental results across various tasks show that EVOAGENT can significantly enhance the tasksolving capability of LLM-based agents, and can be generalized to any LLM-based agent framework to extend them into multi-agent systems. Resources are available at https://evo-agent.github.io/.

NAACL 2025

EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms

automatic multi-agent generation

multi-agent system

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.

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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.

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. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- 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 rapid advancement of Large Language Models (LLMs) has led to the development of benchmarks that consider temporal dynamics, however, there remains a gap in understanding how well these models can generalize across temporal contexts due to the inherent dynamic nature of language and information. This paper introduces the concept of temporal generalization in LLMs, including bias in past and future generalizations. Then we introduce FreshBench, a new evaluation framework that employs fresh text and event prediction for assessing LLMs' temporal adaptability, ensuring the evaluation process free from data leakage and subjective bias. The experiment shows significant temporal biases and a decline in performance over time.

Is Your LLM Outdated? A Deep Look at Temporal Generalization

Natural Language Inference (NLI) is crucial for evaluating models' Natural Language Understanding (NLU) and reasoning abilities. The development of NLI, in part, has been driven by the creation of large datasets, which require significant human effort. This has spurred interest in semi-supervised learning (SSL) that leverages both labeled and unlabeled data. However, the absence of hypotheses and class labels in NLI tasks complicates SSL. Prior work has used class-specific fine-tuned large language models (LLMs) to generate hypotheses and assign pseudo-labels but discarded many LLM-constructed samples during training to ensure the quality. In contrast, we propose to leverage all LLM-constructed samples by handling potentially noisy samples by injecting the moments of labeled samples during training to properly adjust the level of noise. Our method outperforms strong baselines on multiple NLI datasets in low-resource settings.

Leveraging Moment Injection for Enhanced Semi-supervised Natural Language Inference with Large Language Models

We examine the pre-training dynamics of language models, focusing on their ability to copy text from preceding context—a fundamental skill for various LLM applications, including in-context learning (ICL) and retrieval-augmented generation (RAG). We propose a novel perspective that Transformer-based language models develop copying abilities similarly to grokking, which refers to sudden generalization on test set long after the model fit to the training set. Our experiments yield three arguments: (1) The pre-training loss decreases rapidly, while the context copying ability of models initially lags and then abruptly saturates. (2) The speed of developing copying ability is independent of the number of tokens trained, similarly to how grokking speed is unaffected by dataset size as long as the data distribution is preserved. (3) Induction heads, the attention heads responsible for copying, form from shallow to deep layers during training, mirroring the development of circuits in deeper layers during grokking. We contend that the connection between grokking and context copying can provide valuable insights for more effective language model training, ultimately improving in-context performance. For example, we demonstrated that techniques that enhance grokking, such as regularization, either accelerate or enhance the development of context copying.

Language Models ``Grok'' to Copy

Recent advancements in large language models have revolutionized text generation with their remarkable capabilities. These models can produce controlled texts that closely adhere to specific requirements when prompted appropriately. However, designing an optimal prompt to control multiple attributes simultaneously can be challenging. A common approach is to linearly combine single-attribute models, but this strategy often overlooks attribute overlaps and can lead to conflicts. Therefore, we propose a novel combination strategy inspired by the Law of Total Probability and Conditional Mutual Information Minimization on generative language models. This method has been adapted for single-attribute control scenario and is termed the Palette of Language Models due to its theoretical linkage between attribute strength and generation style, akin to blending colors on an artist's palette. Moreover, positive correlation and attribute enhancement are advanced as theoretical properties to guide a rational combination strategy design. We conduct experiments on both single control and multiple control settings, and achieve surpassing results.

Palette of Language Models: A Solver for Controlled Text Generation

We study the post-training of large language models (LLMs) with human preference data. Recently, direct preference optimization and its variants have shown considerable promise in aligning language models, eliminating the need for reward models and online sampling. Despite these benefits, these methods rely on explicit assumptions about the Bradley-Terry (BT) model, which makes them prone to overfitting and results in suboptimal performance, particularly on reasoning-heavy tasks. To address these challenges, we propose a principled preference fine-tuning algorithm called InfoPO, which effectively and efficiently aligns large language models using preference data. InfoPO eliminates the reliance on the BT model and prevents the likelihood of the chosen response from decreasing. Extensive experiments confirm that InfoPO consistently outperforms established baselines on widely used open benchmarks, particularly in reasoning tasks.

InfoPO: On Mutual Information Maximization for Large Language Model Alignment

This paper conducts a longitudinal study over eleven months to address the limitations of prior research on the Open Ko-LLM Leaderboard, which have relied on empirical studies with restricted observation periods of only five months. By extending the analysis duration, we aim to provide a more comprehensive understanding of the progression in developing Korean large language models (LLMs). Our study is guided by three primary research questions: (1) What are the specific challenges in improving LLM performance across diverse tasks on the Open Ko-LLM Leaderboard over time? (2) How does model size impact task performance correlations across various benchmarks? (3) How have the patterns in leaderboard rankings shifted over time on the Open Ko-LLM Leaderboard?. By analyzing 1,769 models over this period, our research offers a comprehensive examination of the ongoing advancements in LLMs and the evolving nature of evaluation frameworks.

Understanding LLM Development Through Longitudinal Study: Insights from the Open Ko-LLM Leaderboard

Text2Sql is a task that converts natural language questions into SQL queries. In previous research on LLM fine-tuning, researchers typically input both the entire database schema and the natural language question into the model. This approach has two issues: 1) the model's context is limited when dealing with a large number of database tables; 2) the question is often related to only a few tables, leading to excessive irrelevant information that distracts the model. To address these issues, we employed pure fine-tuning strategy to reduce redundancy. The model fine-tuned with pure prompts, using prompts that are only 53% of the baseline length, outperforms the baseline (fine-tuned with all tables in the prompt) by 8.2% and 8.6% in Test-suite accuracy (TS) and exact-set-match accuracy (EM), respectively, on the Spider dev set. Under the most refined Spider dev set of prompts, the model achieves TS and EM scores of 73.5% and 75.4%, respectively, approaching state-of-the-art (SOTA) levels. To leverage the capabilities of the model with pure prompts, we applied pure knowledge distillation strategy to transfer its abilities. The distilled student model achieved a 1.9% improvement in TS, while the teacher model's prompt length was only 23% of that of the student model.

Text2Sql: Pure Fine-Tuning and Pure Knowledge Distillation

We propose the Mixture of Frozen Experts (MoFE) architecture, which integrates Parameter-efficient Fine-tuning (PEFT) and the Mixture of Experts (MoE) architecture to enhance both training efficiency and model scalability. By freezing the Feed Forward Network (FFN) layers within the MoE framework, MoFE significantly reduces the number of trainable parameters, improving training efficiency while still allowing for effective knowledge transfer from the expert models. This facilitates the creation of models proficient in multiple domains. We conduct experiments to evaluate the trade-offs between performance and efficiency, compare MoFE with other PEFT methodologies, assess the impact of domain expertise in the constituent models, and determine the optimal training strategy. The results show that, although there may be some trade-offs in performance, the efficiency gains are substantial, making MoFE a reasonable solution for real-world, resource-constrained environments.

MoFE: Mixture of Frozen Experts Architecture

Trading range breakout is a key method in the technical analysis of financial trading, widely employed by traders in financial markets such as stocks, futures, and foreign exchange. However, distinguishing between true and false breakout and providing the correct rationale cause significant challenges to investors. Traditional quantitative methods require large amounts of data and cannot directly present the reasoning process to users, making them less than perfect in this field. Recently, large language models have achieved success in various downstream applications, but their effectiveness in the domain of financial breakout detection has been subpar. The reason is that the unique data and specific knowledge are required in breakout detection. To address these issues, we created the first financial breakout dataset and introduce FinanceGPT-B, the premier large language model for financial breakout detection. Furthermore, we have developed a novel framework for large language models, namely multi-stage structure, effectively reducing mistakes in downstream applications. Experimental results indicate that compared to GPT-3.5, FinanceGPT-B improves the average accuracy of answers and rational by 49.97%, with the multi-stage structure contributing 9.72% to the improvement. Additionally, it outperforms ChatGPT-4 by 42.38%.

FinanceGPT-B: A Large Language Model with Multi-Stage Structure for Financial Breakout Detection

Multimodal content generation has become an area of considerable interest. However, existing methods are hindered by limitations related to model constraints and training strategies: (1) Most current approaches rely on training models from scratch, resulting in inefficient training processes when extending these models; (2) There is a lack of constraints on adjacent steps within the models, leading to slow sampling and poor generation stability across various sampling methods. To address the issues, we introduce Multimodal Generation with Consistency Transferring (MGCT). The method introduces two key improvements: (1) A Model Consistency Transferring (MCT) strategy to acquire low-cost prior knowledge, increasing training efficiency and avoiding error accumulation; (2) A Layer Consistency Transferring (LCT) between adjacent steps, enhancing denoising capabilities at each step and improving model stability across various generation methods. These strategies ensure the consistency of jointly generated multimodal content and improving training efficiency. Experiments show that the algorithm enhances the model's ability to capture actions and depict backgrounds more effectively. In both the AIST++ and Landscape datasets, it improves video generation speed by approximately 40% and quality by about 39.3%, while also achieving a slight 3% improvement in audio quality over the baseline.

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Is Your LLM Outdated? A Deep Look at Temporal Generalization

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