Austria

Despite advances in unsupervised log anomaly detection, current models require dataset-specific training, causing costly procedures, limited scalability, and performance bottlenecks. Furthermore, numerous models lack cognitive reasoning abilities, limiting their transferability to similar systems. Additionally, these models often encounter the **&quot;identical shortcut&quot;** predicament, erroneously predicting normal classes when confronted with rare anomaly logs due to reconstruction errors. To address these issues, we propose **MLAD**, a novel **M**ulti-system **L**og **A**nomaly **D**etection model incorporating semantic relational reasoning. Specifically, we extract cross-system semantic patterns and encode them as high-dimensional learnable vectors. Subsequently, we revamp attention formulas to discern keyword significance and model the overall distribution through vector space diffusion. Lastly, we employ a Gaussian mixture model to highlight rare word uncertainty, optimizing the vector space with maximum expectation. Experiments on real-world datasets demonstrate the superiority of MLAD.

ACL 2025

Towards Multi-System Log Anomaly Detection

multi-system generalization

log anomaly detection

gaussian mixture model

Despite advances in unsupervised log anomaly detection, current models require dataset-specific training, causing costly procedures, limited scalability, and performance bottlenecks. Furthermore, numerous models lack cognitive reasoning abilities, limiting their transferability to similar systems. Additionally, these models often encounter the **"identical shortcut"** predicament, erroneously predicting normal classes when confronted with rare anomaly logs due to reconstruction errors. To address these issues, we propose **MLAD**, a novel **M**ulti-system **L**og **A**nomaly **D**etection model incorporating semantic relational reasoning. Specifically, we extract cross-system semantic patterns and encode them as high-dimensional learnable vectors. Subsequently, we revamp attention formulas to discern keyword significance and model the overall distribution through vector space diffusion. Lastly, we employ a Gaussian mixture model to highlight rare word uncertainty, optimizing the vector space with maximum expectation. Experiments on real-world datasets demonstrate the superiority of MLAD.

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)

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to ACL 2025". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you need to log in with the **email address you registered with**. 

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

Referred to as LLM-as-judge, a generative large language model (LLM) has demonstrated considerable efficacy as an evaluator in various tasks, including Machine Translation (MT) by predicting scores or identifying error types for individual sentences. However, its dependability in practical application has yet to be demonstrated, as there is only an approximated match due to the task's open-ended nature. To address this problem, we introduce a straightforward and novel meta-evaluation strategy PromptCUE and evaluate cutting-edge LLM-as-judge models such as GEMBA-MQM. We identify their fundamental deficits, including certain label biases and the inability to assess near-perfect translations.

To improve reliability, we investigate more trustworthy and less biased models using multidimensional prompt engineering. Our findings indicate that the combination of span-level error quantification and a rubric-style prompt tailored to the characteristics of LLMs has efficiently addressed the majority of the challenges current LLM-as-judge models face. Furthermore, it demonstrates a considerably enhanced alignment with human values. Accordingly, we present Rubric-MQM, the LLM-as-judge for high-end models and an updated version of GEMBA-MQM.

RUBRIC-MQM : Span-Level LLM-as-judge in Machine Translation For High-End Models

Retrieval Augmented Generation (RAG) has emerged as a powerful application of Large Language Models (LLMs), revolutionizing information search and consumption. RAG systems combine traditional search capabilities with LLMs to generate comprehensive answers to user queries, ideally with accurate citations. However, in our experience of developing a RAG product, LLMs often struggle with source attribution, aligning with other industry studies reporting citation accuracy rates of only about 74\% for popular generative search engines. To address this, we present efficient post-processing algorithms to improve citation accuracy in LLM-generated responses, with minimal impact on latency and cost. Our approaches cross-check generated citations against retrieved articles using methods including keyword + semantic matching, fine tuned model with BERTScore, and a lightweight LLM-based technique. Our experimental results demonstrate a relative improvement of 15.46\% in the overall accuracy metrics of our RAG system. This significant enhancement potentially enables a shift from our current larger language model to a relatively smaller model that is approximately 12x more cost-effective and 3x faster in inference time, while maintaining comparable performance. This research contributes to enhancing the reliability and trustworthiness of AI-generated content in information retrieval and summarization tasks which is critical to gain customer trust especially in commercial products.

CiteFix: Enhancing RAG Accuracy Through Post-Processing Citation Correction

This paper introduces Light-R1, an opensource suite for training long reasoning modelsusing reproducible and cost-effective methodology. Given the proprietary nature of data usedin the DeepSeek-R1 series, we develop an alternative approach leveraging exclusively publicdata and models. Our curriculum training progressively increases data difficulty, combinedwith multi-staged post-training. Our LightR1-32B model, trained from Qwen2.5-32BInstruct, outperforms DeepSeek-R1-DistillQwen-32B in math reasoning. Experimental results show that this curriculum approachbecomes more effective when distinct, diverse datasets are available for different training stages: fine-tuning DeepSeek-R1-Distilledmodels (pre-tuned by DeepSeek team on proprietary data) with 3,000 challenging examplesfrom our curriculum dataset yielded state-ofthe-art 7B and 14B models, while the 32Bmodel, Light-R1-32B-DS performed comparably to QwQ-32B and DeepSeek-R1. Furthermore, we extend our work by applying GRPOon long reasoning models. Our final Light-R1-14B-DS achieves SOTA performance among14B models in math, with AIME24 & 25 scoresof 74.0 and 60.2 respectively, surpassing many32B models and DeepSeek-R1-Distill-Llama70B. Despite math-focused training, Light-R1-14B-DS demonstrates strong cross-domain generalization. Light-R1 represents a significantadvancement in making sophisticated reasoning models more accessible and implementablein real-world applications. Our models, training data and code have been made available.

Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond

Discovering and evaluating long-form talk content such as videos and podcasts poses a significant challenge for users, as it requires a considerable time investment. Previews offer a practical solution by providing concise snippets that showcase key moments of the content, enabling users to make more informed and confident choices. We propose an LLM-based approach for generating podcast episode previews and deploy the solution at scale, serving hundreds of thousands of podcast previews in a real-world application. Comprehensive offline evaluations and online A/B testing demonstrate that LLM-generated previews consistently outperform a strong baseline built on top of various ML expert models, showcasing a significant reduction in the need for meticulous feature engineering. The offline results indicate notable enhancements in understandability, contextual clarity, and interest level, and the online A/B test shows a 4.6% increase in user engagement with preview content, along with a 5x boost in processing efficiency, offering a more streamlined and performant solution compared to the strong baseline of feature-engineered expert models.

Transforming Podcast Preview Generation: From Expert Models to LLM-Based Systems

Transformer-based large language models (LLMs) encounter challenges in processing long sequences on edge devices due to the quadratic complexity of attention mechanisms and growing memory demands from Key-Value (KV) cache. Existing KV cache optimizations struggle with irreversible token eviction in long-output tasks, while alternative sequence modeling architectures prove costly to adopt within established Transformer infrastructure. We present EdgeInfinite, a memory-efficient solution for infinite contexts that integrates compressed memory into Transformer-based LLMs through a trainable memory-gating module. This approach maintains full compatibility with standard Transformer architectures, requiring fine-tuning only a small part of parameters, and enables selective activation of the memory-gating module for long and short context task routing. The experimental result shows that EdgeInfinite achieves comparable performance to baseline Transformer-based LLM on long context benchmarks while optimizing memory consumption and time to first token.

EdgeInfinite: A Memory-Efficient Infinite-Context Transformer for Edge Devices

Analyzing large volumes of case law to uncover evolving legal principles, across multiple cases, on a given topic is a demanding task for legal professionals. Structured topical reports provide an effective solution by summarizing key issues, principles, and judgments, enabling comprehensive legal analysis on a particular topic. While prior works have advanced query-based individual case summarization, none have extended to automatically generating multi-case structured reports. To address this, we introduce LexGenie, an automated LLM-based pipeline designed to create structured reports using the entire body of case law on user-specified topics within the European Court of Human Rights jurisdiction. LexGenie retrieves, clusters, and organizes relevant passages by topic to generate a structured outline and cohesive content for each section. Expert evaluation confirms LexGenie's utility in producing structured reports that enhance efficient, scalable legal analysis.

LexGenie: Automated Generation of Structured Reports for European Court of Human Rights Case Law

Foundational models endowed with emergent abilities are increasingly deployed as autonomous agents to navigate intricate environments. Despite their capability to comprehend human intentions, even when paired with reasoning traces, they struggle to achieve robust autonomy. In this work, we introduce **LEAP & LEAN**, a novel paradigm designed to enhance the performance of Large Language Models (LLMs) as autonomous agents. LEAP employs look-ahead planning to refine action selection, while LEAN streamlines navigation through agile prompt construction, enabling more efficient task completion. Together, LEAP & LEAN address the explore-exploit dilemma, fostering optimal decision-making and improving task performance. We evaluate our framework across diverse, multi-faceted task-oriented domains~(WebShop, ALFWorld, and TravelPlanner) using both proprietary and open-source LLM agents. Notably, without any fine-tuning, our framework outperforms agents trained via imitation learning, reinforcement learning, and reasoning-based approaches. Our findings underscore the importance of action and prompt curation to create robust and efficient fully autonomous LLM agents.

LEAP & LEAN: Look-ahead Planning and Agile Navigation for LLM Agents

Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rather than treatment planning. We identify three critical limitations in current approaches: they generate treatment plans in a single pass rather than following the sequential reasoning process used by clinicians; they rarely incorporate patient-specific historical context; and they fail to effectively distinguish between subjective and objective clinical information. Motivated by the SOAP methodology (Subjective, Objective, Assessment, Plan), we introduce MedPlan, a novel framework that structures LLM reasoning to align with real-life clinician workflows. Our approach employs a two-stage architecture that first generates a clinical assessment based on patient symptoms and objective data, then formulates a structured treatment plan informed by this assessment and enriched with patient-specific information through retrieval-augmented generation. Comprehensive evaluation demonstrates that our method significantly outperforms baseline approaches in both assessment accuracy and treatment plan quality. Our demo system and code are available at https://github.com/JustinHsu1019/MedPlan.

MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation

Error correction is an important capability when applying large language models (LLMs) to facilitate user typing on mobile devices. In this paper, we use LLMs to synthesize a high-quality dataset of error correction pairs to evaluate and improve LLMs for mobile applications. 
We first prompt LLMs with error correction domain knowledge to build a scalable and reliable addition to the existing data synthesis pipeline. 
We then adapt the synthetic data distribution to match the mobile application domain by reweighting the samples. 
The reweighting model is learnt by predicting (a handful of) live A/B test metrics when deploying LLMs in production, given the LLM performance on offline evaluation data and scores from a small privacy-preserving on-device language model.
Finally, we present best practices for mixing our synthetic data with other data sources to improve model performance on error correction in both offline evaluation and production live A/B testing.

Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications

Occupational Health (OH) triage is a systematic process for evaluating and prioritising workplace health concerns to determine appropriate care and interventions. This research addresses critical triage challenges through our novel AI agent orchestration framework, OccuTriage, developed in collaboration with Healthcare Provider. Our framework simulates healthcare professionals' reasoning using specialized LLM agents, retrieval augmentation with domain-specific knowledge, and a bidirectional decision architecture. Experimental evaluation on 2,589 OH cases demonstrates OccuTriage outperforms single-agent approaches with a 20.16% average discordance rate compared to baseline rates of 43.05%, while matching or exceeding human expert performance (25.11%). The system excels in reducing under-triage rates, achieving 9.84% and 3.1% for appointment and assessor type decisions respectively. These results establish OccuTriage's efficacy in performing complex OH triage while maintaining safety and optimizing resource allocation.

Downloads

Next from ACL 2025

RUBRIC-MQM : Span-Level LLM-as-judge in Machine Translation For High-End Models

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES