Austria

Knowledge Graphs represent real-world entities and the relationships between them. Multilingual Knowledge Graph Construction (mKGC) refers to the task of automatically constructing or predicting missing entities and links for knowledge graphs in a multilingual setting. In this work, we reformulate the mKGC task as a Question Answering (QA) task and introduce mRAKL: a Retrieval-Augmented Generation (RAG) based system to perform mKGC. We achieve this by using the head entity and linking relation in a question, and having our model predict the tail entity as an answer. Our experiments focus primarily on two low-resourced languages: Tigrinya and Amharic. We experiment with using higher-resourced languages, Arabic and English, to utilize cross-lingual transfer for mKGC. With a BM25 retriever, we find that the RAG-based approach improves performance over a no-context setting. Further, our ablation studies show that with an idealized retrieval system, mRAKL improves accuracy by up to 4.92 and 8.79 percentage points for Tigrinya and Amharic, respectively.

ACL 2025

mRAKL: Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages

knowledge graphs; low-resource; cross-lingual; rag

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
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ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

poster

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

Recent success of large language models (LLMs) in diverse domains showcases their potential to revolutionize scientific fields, including drug editing. Traditional drug editing relies on iterative conversations with domain experts, refining the drug until the desired property is achieved. This interactive and iterative process mirrors the strengths of LLMs, making them well-suited for drug editing. *In existing works, LLMs edit each molecule independently without leveraging knowledge from past edits.* However, human experts develop intuition about effective modifications over time through historical experience; accumulating past knowledge is pivotal for human experts, and so it is for LLMs. *In this work, we propose RL-Guider---a reinforcement-learning-agent to provide suggestions to LLMs; it uses the rich information provided from evaluating editing results made by the LLM based on the recommendations to improve itself over time.* RL-Guider is the first work that leverages both the comprehensive "world-level" knowledge of LLMs and the knowledge accumulated from historical feedback. As a result, RL-Guider mitigates several shortcomings of existing approaches and demonstrates superior performance.

RL-Guider: Leveraging Historical Decisions and Feedback for Drug Editing with Large Language Models

While large language models (LLMs) have been thoroughly evaluated for deductive and inductive reasoning, their proficiency in holistic rule learning in interactive environments remains less explored. We introduce RULEARN, a novel benchmark to assess the rule-learning abilities of LLM agents in interactive settings. In RULEARN, agents strategically interact with simulated environments to gather observations, discern patterns, and solve complex problems. To enhance the rule-learning capabilities for LLM agents, we propose IDEA, a novel reasoning framework that integrates the process of **I**nduction, **De**duction, and **A**bduction. The IDEA agent generates initial hypotheses from limited observations through abduction, devises plans to validate these hypotheses or leverages them to solve problems via deduction, and refines previous hypotheses through induction, dynamically establishing and applying rules that mimic human rule-learning behaviors. Our evaluation of the IDEA framework, which involves five representative LLMs, demonstrates significant improvements over the baseline. Furthermore, our study with human participants reveals notable discrepancies in rule-learning behaviors between humans and LLMs. We believe our benchmark will serve as a valuable and challenging resource, and IDEA will provide crucial insights for the development of LLM agents capable of human-like rule learning in real-world scenarios. Our code and data have been released at: https://github.com/KaiyuHe998/RULEARN_IDEA.

IDEA: Enhancing the Rule Learning Ability of Large Language Model Agent through Induction, Deduction, and Abduction

Large Language Models (LLMs) exhibit emergent in-context learning (ICL) capabilities, allowing them to adapt to unseen tasks based on example demonstrations. Traditional ICL embeds examples within the prompt, while activation steering, uses a vector derived from examples to guide the latent states of LLMs toward desired behaviors. However, traditional ICL is difficult to control quantitatively and consumes valuable context space. Existing activation steering methods apply a single sentence-level steering vector uniformly across all tokens, ignoring LLMs' token-wise, auto-regressive nature. This coarse control can lead to inconsistencies and suboptimal adjustments during generation. To address this problem, we introduce Dynamic Steering with Episodic Memory (DSEM), a novel training-free framework that aligns LLMs to given demonstrations by steering at the token level conditioned on the input query. DSEM employs a key-value memory to store associations between generated tokens and steering vectors. During inference, it uses a nearest-neighbor mechanism to dynamically compute steering vectors for each token chunk, enabling more precise and adaptive guidance. Our method surpasses strong baselines across diverse alignment tasks - including safety, style transfer, and role-playing - demonstrating improved alignment as demonstration size scales.

Dynamic Steering With Episodic Memory For Large Language Models

Large language models (LLMs) excel in generating unstructured text. However, they struggle with producing structured output while maintaining accuracy in zero-shot information extraction (IE), such as named entity recognition (NER) and relation extraction (RE). To address these challenges, we propose CROSSAGENTIE, a multi-agent framework that enhances zero-shot IE through multi-agent LLM collaboration. CROSSAGENTIE refines LLM predictions iteratively through two mechanisms: intra-group cross-type debate, which resolves entity-label conflicts through context-based evidence and confidence aggregation, and inter-group cross-task debate, where NER and RE mutually refine outputs via bidirectional feedback. Furthermore, we introduce template fine-tuning, distilling high-confidence multi-agent outputs into a single model, significantly reducing inference cost while preserving accuracy. Experiments across five NER and five RE datasets show that CROSSAGENTIE significantly outperforms state-of-the-art zero-shot baselines by a large margin. CROSSAGENTIE effectively addresses LLMs limitations in structured prediction with an effective and efficient approach for zero-shot information extraction.

CROSSAGENTIE: Cross-Type and Cross-Task Multi-Agent LLM Collaboration for Zero-Shot Information Extraction

Large Language Models (LLMs) often exhibit knowledge disparities across languages. Encouraging LLMs to abstain when faced with knowledge gaps is a promising strategy to reduce hallucinations in multilingual settings. Current abstention strategies for multilingual scenarios primarily rely on generating feedback in various languages using LLMs and performing self-reflection. However, these methods can be adversely impacted by inaccuracies and biases in the generated feedback. To address this, from a causal perspective, we introduce CausalAbstain, a method that helps LLMs determine whether to utilize multiple generated feedback responses and how to identify the most useful ones. Extensive experiments demonstrate that CausalAbstain effectively selects helpful feedback and enhances abstention decisions with interpretability in both native language (Casual-native) and multilingual (Causal-multi) settings, outperforming strong baselines on two benchmark datasets covering encyclopedic and commonsense knowledge QA tasks.

CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention

With the widespread of Large Language Models (LLMs), there has been an increasing need to detect LLM-generated texts, prompting extensive research in this area. However, existing detection methods mainly evaluate on static benchmarks, which neglect the evolving nature of LLMs. Relying on existing static benchmarks could create a misleading sense of security, overestimating the real-world effectiveness of detection methods. To bridge this gap, we introduce EvoBench, a dynamic benchmark considering a new dimension of generalization across continuously evolving LLMs. EvoBench categorizes the evolving LLMs into (1) updates over time and (2) developments like finetuning and pruning, covering 7 LLM families and their 29 evolving versions. To measure the generalization across evolving LLMs, we introduce a new EMG (Evolving Model Generalization) metric. Our evaluation of 14 detection methods on EvoBench reveals that they all struggle to maintain generalization when confronted with evolving LLMs. To mitigate the generalization problems, we further propose improvement strategies. For zero-shot detectors, we propose pruning the scoring model to extract shared features. For supervised detectors, we also propose a practical training strategy. Our research sheds light on critical challenges in real-world LLM-generated text detection and represents a significant step toward practical applications.

EvoBench: Towards Real-world LLM-Generated Text Detection Benchmarking for Evolving Large Language Models

Large language models (LLMs) have revolutionized natural language processing (NLP) tasks, yet their increasing size poses substantial challenges in terms of computational and memory resources. Block floating-point (BFP) arithmetic offers an effective solution by leveraging the strengths of both floating-point and fixed-point representations, leading to reductions in both storage and computational overhead. However, current low-bit BFP quantization approaches often struggle to handle extreme outliers, leading to significant accuracy degradation. To overcome this limitation, we introduce Extendable Exponent Sharing (EES), a novel BFP representation that extends the exponent bit width to capture a wider dynamic range. EES achieves this by embedding extendable exponent bits into the least significant mantissa bits, thereby increasing the shared exponent's bit width without incurring additional storage costs. To optimize the trade-off between accuracy and energy efficiency, EES employs a design space exploration strategy to optimize the configuration of extendable exponent bit widths. Experimental results show that EES outperforms representative baselines in both accuracy and computational efficiency.

Improving Efficiency in Large Language Models via Extendable Block Floating Point Representation

Style embeddings are useful for stylistic analysis and style transfer, yet they only exist for English. We introduce Multilingual StyleDistance (mStyleDistance), a method that can generate style embeddings in new languages using synthetic data and a contrastive loss. We create style embeddings in nine languages and a multilingual STEL-or-Content benchmark (Wegmann et al., 2022) that serves to assess their quality. We also employ our embeddings in an authorship verification task involving different languages. Our results show that mStyleDistance embeddings outperform existing style embeddings on these benchmarks and generalize well to unseen features and languages. We make our models and datasets publicly available.

mStyleDistance: Multilingual Style Embeddings and their Evaluation

Visual Question Answering (VQA) necessitates models to reason effectively across visual and textual modalities. However, existing Large Vision-Language Models (LVLMs) often fall short in achieving human-like reasoning due to a lack of integrated commonsense knowledge, limiting their robustness and accuracy in real-world scenarios where both explicit facts and implicit understanding are crucial. To address this challenge, we present MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge, a novel framework designed to enhance multimodal inference by integrating commonsense reasoning. MAGIC-VQA introduces a three-stage process: (1) Explicit Commonsense Knowledge Retrieval from external knowledge graphs, (2) By-Type Commonsense Knowledge Post-Processing to refine contextual relevance, and (3) Implicit Commonsense Knowledge Augmentation using a heterogeneous graph processed by a Graph Neural Network (GNN). These stages collectively enable nuanced, context-aware reasoning without extensive pre-training or intricate prompt tuning.
Our MAGIC-VQA significantly improves comprehensive benchmark datasets, surpassing existing models in tasks requiring advanced commonsense reasoning. MAGIC-VQA establishes a robust pathway for integrating commonsense knowledge into VQA, bridging the gap between vision-language inputs and high-level reasoning for improved reliability and contextual accuracy.

MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering

Entailment trees are essential for enhancing interpretability and transparency in tasks like question answering and natural language understanding. However, existing approaches often lack logical consistency, as they rely on static reward structures or ignore the intricate dependencies within multi-step reasoning. To address these limitations, we propose a method that integrates natural logic principles into reinforcement learning, enabling dynamic reward computation to guide entailment tree generation. Our approach ensures logical consistency across reasoning steps while improving interpretability and generalization. Experiments on EntailmentBank demonstrate significant improvements over state-of-the-art methods, highlighting the effectiveness of natural logic in structured reasoning.

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