Austria

Large Vision-Language Models (LVLMs) have transformed image captioning, shifting from concise captions to detailed descriptions. We introduce LOTUS, a leaderboard for evaluating detailed captions, addressing three main gaps in existing evaluations: lack of standardized criteria, bias-aware assessments, and user preference considerations. LOTUS comprehensively evaluates various aspects, including caption quality (e.g., alignment, descriptiveness), risks (e.g., hallucination), and societal biases (e.g., gender bias) while enabling preference-oriented evaluations by tailoring criteria to diverse user preferences. Our analysis of recent LVLMs reveals no single model excels across all criteria, while correlations emerge between caption detail and bias risks. Preference-oriented evaluations demonstrate that optimal model selection depends on user priorities.

ACL 2025

LOTUS: A Leaderboard for Detailed Image Captioning from Quality to Societal Bias and User Preferences

user-oriented evaluation

detailed caption evaluation

large vision-language models

societal bias

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.* 
[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)

Out-of-scope (OOS) intent detection is a critical challenge in task-oriented dialogue systems (TODS), as it ensures robustness to unseen and ambiguous queries. In this work, we propose a novel but simple modular framework that combines uncertainty modeling with fine-tuned large language models (LLMs) for efficient and accurate OOS detection. 
The first step applies uncertainty estimation to the output of an in-scope intent detection classifier, which is currently deployed in a real-world TODS handling tens of thousands of user interactions daily. The second step then leverages an emerging LLM-based approach, where a fine-tuned LLM is triggered to make a final decision on instances with high uncertainty.
Unlike prior approaches, our method effectively balances computational efficiency and performance, combining traditional approaches with LLMs and yielding state-of-the-art results on key OOS detection benchmarks, including real-world OOS data acquired from a deployed TODS.

Efficient Out-of-Scope Detection in Dialogue Systems via Uncertainty-Driven LLM Routing

The advent of Large Language Models (LLMs) has transformed how complex tasks across various domains can be automated. One of the industry trends today is Agentic AI, which leverages LLMs to operate multiple tools and provide automatic configuration.
In the domain of cloud computing, Agentic AI might be used, for example, with the generation of Kubernetes manifests -- structured configuration files that define containerized environments.
However, effectively applying LLMs to domain-specific tasks often reveals knowledge gaps that impact the accuracy and reliability of the generated output.

To address these challenges, we propose KGen, a pipeline for generating K8s manifests directly from user-described intents expressed in natural language using LLMs. Our approach leverages an extensive n-shot learning analysis to choose the appropriate number of examples that can better guide the adopted models in generating the manuscripts while also looking at the computational cost. Our results validate the use of LLM in this task and show that (as expected) increasing the number of n-shot examples can improve the quality of the generated configurations when adopting more specialized models, such as Mixtral-8x7B (which uses the Mixture of Experts approach) and Prometheus-8x7B-v2.0, but (surprisingly) for more general-purpose models like Llama3-8B and Llama3-70B, it can lead to smaller number of valid K8s manifests. 
These results underscore the complexities of adapting LLMs for domain-specific structured generation and emphasize the need for an in-depth analysis to determine the most effective setup, also suggesting that smaller models sometimes outperform their larger counterparts for each domain-specific task.

A Perspective on LLM Data Generation with Few-shot Examples: from Intent to Kubernetes Manifest

Anomaly Detection (AD) focuses on detecting samples that differ from the standard pattern, making it a vital tool in process control. Logical anomalies may appear visually normal yet violate predefined constraints on object presence, arrangement, or quantity, depending on reasoning and explainability. We introduce LogicQA, a framework that enhances AD by providing industrial operators with explanations for logical anomalies. LogicQA compiles automatically generated questions into a checklist and collects responses to identify violations of logical constraints. LogicQA is training-free, annotation-free, and operates in a few-shot setting. We achieve state-of-the-art (SOTA) Logical AD performance on public benchmarks, MVTec LOCO AD, with an AUROC of 87.6% and an F1-max of 87.0% along with the explanations of anomalies. Also, our approach has shown outstanding performance on semiconductor SEM corporate data, further validating its effectiveness in industrial applications.

LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions

The pretraining of code LLMs typically begins with general data and progresses to domain specific data through sequential stages. In thelatter stages, a challenging issue is that the data of a target domain can be limited in size, and conventional approach of increasing the number of epochs does not lead to a performance
gain. In this paper, we propose a novel packing method, which is extracting overlapping contexts from the training data using variable length stride. Our method can mitigate the data scarcity issue by providing more diverse and abundant examples of next token prediction than non-overlapping contexts. While the training time of our approach is increased proportionally to the amount of augmented examples, we present space-efficient implementations to store overlapping contexts. Extensive experiments with real datasets show that our approach outperforms the conventional approach of controlling the number of epochs in terms of the pass@k rate.

Overlapping Context with Variable-Length Stride Increases Diversity when Training Large Language Model for Code

Large Language Models (LLMs) have shown capabilities in various natural language processing tasks, yet they often struggle with logical reasoning, particularly when dealing with complex natural language statements. To address this challenge, approaches that combine LLMs with symbolic reasoners have been proposed, where the LLM translates the natural language statements into symbolic representations, which are then verified by an external symbolic solver. However, ensuring syntactic correctness in these translations remains a significant challenge. To address this, we propose to constrain the outputs of the LLMs using Grammar-Constrained Decoding, showing that it consistently improves both syntactic correctness and semantic accuracy in logical parsing tasks. Our findings suggest that grammar constraints can serve as an effective substitute for in-context examples, especially beneficial for resource-constrained applications using smaller models.

Grammar-Constrained Decoding Makes Large Language Models Better Logical Parsers

We present AUTOSUMM, a large language model (LLM)-based summarization system deployed in a regulated banking environment to generate accurate, privacy-compliant summaries of customer-advisor conversations. The system addresses challenges unique to this domain, including speaker attribution errors, hallucination risks, and short or low-information transcripts. Our architecture integrates dynamic transcript segmentation, thematic coverage tracking, and a domain specific multi-layered hallucination detection module that combines syntactic, semantic, and entailment-based checks. Human-in-the-loop feedback from over 300 advisors supports continuous refinement and auditability.

Empirically, AUTOSUMM achieves a 94% factual consistency rate and a significant reduction in hallucination rate. In production, 89% of summaries required no edits, and only 1% required major corrections. A structured model version management pipeline ensures stable upgrades with minimal disruption. We detail our deployment methodology, monitoring strategy, and ethical safeguards, showing how LLMs can be reliably integrated into high-stakes, regulated workflows.

AUTOSUMM: A Comprehensive Framework for LLM-Based Conversation Summarization

Ensuring clinical data privacy while preserving utility is critical for AI-driven healthcare and data analytics. Existing de-identification (De-ID) methods, including rule-based techniques, deep learning models, and large language models (LLMs), often suffer from recall errors, limited generalization, and inefficiencies, limiting their real-world applicability. We propose a fully automated, multi-modal framework, RedactOR for de-identifying structured and unstructured electronic health records, including clinical audio records. Our framework employs cost-efficient De-ID strategies, including intelligent routing, hybrid rule and LLM based approaches, and a two-step audio redaction approach. We present a retrieval-based entity relexicalization approach to ensure consistent substitutions of protected entities, thereby enhancing data coherence for downstream applications. We discuss key design desiderata, de-identification and relexicalization methodology, and modular architecture of RedactOR and its integration with Oracle Health Clinical AI system. Evaluated on the i2b2 2014 De-ID dataset using standard metrics with strict recall, our approach achieves competitive performance while optimizing token usage to reduce LLM costs. Finally, we discuss key lessons and insights from deployment in real-world AI-driven healthcare data pipelines.

RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification

Large language models (LLMs) have been widely used for relevance assessment in information retrieval. However, our study demonstrates that combining two distinct small language models (SLMs) with different architectures can outperform LLMs in this task. Our approach—QUPID—integrates a generative SLM with an embedding-based SLM, achieving higher relevance judgment accuracy while reducing computational costs compared to state-of-the-art LLM solutions. This computational efficiency makes QUPID highly scalable for real-world search systems processing millions of queries daily. In experiments across diverse document types, our method demonstrated consistent performance improvements (Cohen's Kappa of 0.646 versus 0.387 for leading LLMs) while offering 60x faster inference times. Furthermore, when integrated into production search pipelines, QUPID improved nDCG@5 scores by 1.9\%. These findings underscore how architectural diversity in model combinations can significantly enhance both search relevance and operational efficiency in information retrieval systems.

QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines

Recently, Large Language Models (LLMs) have been widely studied by researchers for their roles in various downstream NLP tasks. As a fundamental task in the NLP field, Chinese Grammatical Error Correction (CGEC) aims to correct all potential grammatical errors in the input sentences. Previous studies have shown that LLMs' performance as correctors on CGEC remains unsatisfactory due to the challenging nature of the task. To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC. Considering the rich grammatical knowledge stored in LLMs and their powerful semantic understanding capabilities, we utilize LLMs as explainers to provide explanation information to the CGEC small models during error correction, aiming to enhance performance. We also use LLMs as evaluators to bring more reasonable CGEC evaluations, thus alleviating the troubles caused by the subjectivity of the CGEC task. In particular, our work is also an active exploration of how LLMs and small models better collaborate in downstream tasks. Extensive experiment and detailed analyses on widely used datasets verify the effectiveness of our intuition and the proposed methods.

Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction

The discovery of novel antibiotics is critical to address the growing antimicrobial resistance (AMR). However, pharmaceutical industries face high costs (over $1 billion), long timelines, and a high failure rate, worsened by the rediscovery of known compounds. We propose an LLM-based pipeline that acts as an alert system, detecting prior evidence of antibiotic activity to prevent costly rediscoveries. The system integrates literature on organisms and chemicals into a Knowledge Graph (KG), ensuring taxonomic resolution, synonym handling, and multi-level evidence classification. We tested the pipeline on a private list of 73 potential antibiotic-producing organisms, disclosing 12 negative hits for evaluation. The results highlight the effectiveness of the pipeline for evidence reviewing, reducing false negatives, and accelerating decision-making. The KG for negative hits as well as the user interface for interactive exploration are available at https://github.com/idiap/abroad-kg-store and https://github.com/idiap/abroad-demo-webapp.

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Efficient Out-of-Scope Detection in Dialogue Systems via Uncertainty-Driven LLM Routing

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