Austria

Agents controlled by large language models (LLMs) have the potential to assist humans in natural language tasks across various domains and applications, if they are provided with access to confidential data of their principal. When such digital assistants interact with their potentially adversarial environment, confidentiality of the data is at stake. Given a natural language request, we investigate whether an LLM-controlled agent can control access to internal data by considering confidentiality in its response, in a manner similar to humans. For evaluation, we created a synthetic dataset consisting of confidentiality-aware planning and deduction tasks in an organizational access control setting. The dataset was developed from human input, LLM-generated content, and existing datasets. It includes various everyday scenarios in which access to confidential or private information is requested. We utilize our dataset to evaluate the ability to infer confidentiality-aware behavior in such scenarios by differentiating between legitimate and illegitimate access requests. We compare a prompting-based and a fine-tuning-based approach, to evaluate the performance of Llama 3 and GPT-4o-mini in this domain. In addition, we conducted a user study to establish a baseline for human evaluation performance in these tasks. We find humans reached an accuracy of up to 79%. Prompting techniques, such as chain-of-thought and few-shot prompting, yield promising results, but still fall short of real-world applicability and do not surpass human baseline performance. However, we find that fine-tuning significantly improves the agent’s ability to make access decisions up to an accuracy of 98%, making it a promising approach for future confidentiality-aware applications when data is available.

ACL 2025

Can a Large Language Model Keep My Secrets? A Study on LLM-Controlled Agents

access control

llms for security

datasets

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

IJCNLP-AACL 2025

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

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)

In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners. Tasks like generating urban planning texts, retrieving related information, and evaluating planning documents pose unique challenges. To enhance the efficiency of urban professionals and overcome these obstacles, we introduce PlanGPT, the first specialized AI agent framework tailored for urban and spatial planning. Developed through collaborative efforts with professional urban planners, PlanGPT integrates a customized local database retrieval system, domain-specific knowledge activation capabilities, and advanced tool orchestration mechanisms. Through its comprehensive agent architecture, PlanGPT coordinates multiple specialized components to deliver intelligent assistance precisely tailored to the intricacies of urban planning workflows. Empirical tests demonstrate that PlanGPT framework has achieved advanced performance, providing comprehensive support that significantly enhances professional planning efficiency.

PlanGPT: Enhancing Urban Planning with a Tailored Agent Framework

Exploration, the act of broadening user experiences beyond their established preferences, is challenging in large-scale recommendation systems due to feedback loops and limited signals on user exploration patterns. Large Language Models (LLMs) offer potential solutions by leveraging their world knowledge to recommend novel content outside these loops. A key challenge is aligning LLMs with user preferences while preserving their knowledge and reasoning. To enhance planning for new user interests using LLMs, this paper introduces a novel approach that combines hierarchical planning with LLM inference-time scaling. This method aims to improve recommendation relevancy without compromising novelty. We decouple novelty and user-alignment, training separate LLMs for each objective. We then scale up the novelty-focused LLM's inference and select the best-of-n predictions using the user-aligned LLM. Live experiments demonstrate efficacy, showing significant gains in both user satisfaction (measured by watch activity and active user counts) and exploration diversity.

User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems

Transparency in AI healthcare decision-making
is crucial. By incorporating rationales to explain reason for each predicted label, users
could understand Large Language Models
(LLMs)’s reasoning to make better decision.
In this work, we introduce a new task - Sentiment Reasoning - for both speech and text
modalities, and our proposed multimodal multitask framework and the world’s largest multimodal sentiment analysis dataset. Sentiment Reasoning is an auxiliary task in sentiment analysis where the model predicts both
the sentiment label and generates the rationale behind it based on the input transcript.
Our study conducted on both human transcripts
and Automatic Speech Recognition (ASR) transcripts shows that Sentiment Reasoning helps
improve model transparency by providing rationale for model prediction with quality semantically comparable to humans while also
improving model’s classification performance
(+2% increase in both accuracy and macro-
F1) via rationale-augmented fine-tuning. Also,
no significant difference in the semantic quality of generated rationales between human and
ASR transcripts. All code, data (five languages - Vietnamese, English, Chinese, German, and
French) and models are published online.

Sentiment Reasoning for Healthcare

Social media platforms possess considerable potential in the realm of exploring mental health. Previous research has indicated that major life events can greatly impact individuals' mental health. However, due to the complexity and ambiguity nature of life events, shedding its light on social media data is quite challenging. In this paper, we are dedicated to uncovering life events mentioned in posts on social media. We hereby provide a carefully-annotated social media event dataset, PsyEvent, which encompasses 12 major life event categories that are likely to occur in everyday life. This dataset is human-annotated under iterative procedure and boasts a high level of quality. Furthermore, by applying the life events extracted from posts to downstream tasks such as early risk detection of depression and suicide risk prediction, we have observed a considerable improvement in performance. This suggests that extracting life events from social media can be beneficial for the analysis of individuals' mental health.

Tracking Life’s Ups and Downs: Mining Life Events from Social Media Posts for Mental Health Analysis

We present a new dataset for chart question answering (CQA) constructed from visualization notebooks. The dataset features real-world, multi-view charts paired with natural language questions grounded in analytical narratives. Unlike prior benchmarks, our data reflects ecologically valid reasoning workflows. Benchmarking state-of-the-art multimodal large language models reveals a significant performance gap, with GPT-4.1 achieving an accuracy of 69.3\%, underscoring the challenges posed by this more authentic CQA setting.

Chart Question Answering from Real-World Analytical Narratives

As Large Language Models (LLMs) become increasingly widespread, understanding how specific training data shapes their outputs is crucial for transparency, accountability, privacy, and fairness. To explore how LLMs recall and replicate learned information, we introduce a systematic approach centered on analyzing low-perplexity sequences—high-probability text spans generated by the model. Our pipeline reliably extracts such long sequences across diverse topics while avoiding degeneration, then traces them back to their sources in the training data. Surprisingly, we find that a substantial portion of these low-perplexity spans cannot be mapped to the corpus. For those that do match, we analyze the types of memorization involved and present the distribution of unique documents contributing to these mappings, highlighting the extent of verbatim recall.

Low-Perplexity LLM-Generated Sequences and Where To Find Them

Tables are extensively utilized to represent and store data, however, they often lack explicit semantics necessary for machine interpretation of their contents. Semantic table interpretation is essential for integrating structured data with knowledge graphs, yet existing methods face challenges with Russian-language tables due to limited labeled data and linguistic peculiarities. This paper introduces a contrastive learning approach to minimize reliance on manual labeling and enhance the accuracy of column annotation for rare semantic types. The proposed method adapts contrastive learning for tabular data through augmentations and employs a distilled multilingual BERT model trained on the unlabeled RWT corpus (comprising 7.4 million columns). The resulting table representations are incorporated into the RuTaBERT pipeline, reducing computational overhead. Experimental results demonstrate a micro-F1 score of 97% and a macro-F1 score of 92%, surpassing several baseline approaches. These findings emphasize the efficiency of the proposed method in addressing data sparsity and handling unique features of the Russian language. The results further confirm that contrastive learning effectively captures semantic similarities among columns without explicit supervision, which is particularly vital for rare data types.

CoLeM: A framework for semantic interpretation of Russian-language tables based on contrastive learning

Large Language Models (LLMs) demonstrate strong performance on different language tasks, but tend to hallucinate -- generate plausible but factually incorrect outputs. Recently, several approaches to integrate Knowledge Graphs (KGs) into LLM inference were published to reduce hallucinations. This paper presents a systematic literature review (SLR) of such approaches. Following established SLR methodology, we identified relevant work by systematically search in different academic online libraries and applying a selection process. Nine publications were chosen for in-depth analysis. Our synthesis reveals differences and similarities of how the KG is accessed, traversed, and how the context is finally assembled. KG integration can significantly improve LLM performance on benchmark datasets and additionally to mitigate hallucination enhance reasoning capabilities, explainability, and access to domain-specific knowledge. We also point out current limitations and outline directions for future work.

Mitigating Hallucination by Integrating Knowledge Graphs into LLM Inference -- a Systematic Literature Review

Due to the sensitive nature of personally identifiable information (PII), its owners may have the authority to control its inclusion or request its removal from large-language model (LLM) training. 
Beyond this, PII may be added or removed from training datasets due to evolving dataset curation techniques, because they were newly scraped for retraining, or because they were included in a new downstream fine-tuning stage. We find that the amount and ease of PII memorization is a dynamic property of a model that evolves throughout training pipelines and depends on commonly altered design choices. We characterize three such novel phenomena: (1) similar-appearing PII seen later in training can elicit memorization of earlier-seen sequences in what we call assisted memorization, and this is a significant factor (in our settings, up to 1/3); (2) adding PII can increase memorization of other PII; and (3) removing PII can lead to other PII being memorized.

Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training

We introduce the first set of Universal Dependencies (UD) annotations for Spanish learner writing from the UC Davis COWSL2H corpus. Our annotations include lemmatization, POS tagging, and syntactic dependencies. We adapt the existing UD framework for Spanish L1 to account for learner-specific features such as code-switching and non-canonical syntax. A suite of parsing evaluation experiments shows that parsers trained on learner data together with moderate sizes of Spanish L1 data can yield reasonable performance. Our annotations and parsers will be openly accessible to motivate future development of learner-oriented language technologies.

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PlanGPT: Enhancing Urban Planning with a Tailored Agent Framework

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