Austria

Existing approaches to fine-grained emotion classification (FEC) often operate in Euclidean space, where the flat geometry limits the ability to distinguish semantically similar emotion labels (e.g., *annoyed* vs. *angry*). While prior research has explored hyperbolic geometry to capture fine-grained label distinctions, it typically relies on predefined hierarchies and overlooks semantically confusable negatives. In this work, we propose HyCoEM, a semantic alignment framework that leverages the Lorentz model of hyperbolic space. Our approach jointly embeds text and label representations into hyperbolic space via the exponential map, and employs a contrastive loss to bring text embeddings closer to their true labels while pushing them away from adaptively selected, semantically similar negatives. This enables the model to learn label embeddings without relying on a predefined hierarchy and better captures subtle distinctions by incorporating information from both positive and challenging negative labels. Experimental results on two benchmark FEC datasets demonstrate the effectiveness of our approach over baseline methods.

ACL 2025

Semantic alignment in hyperbolic space for fine-grained emotion classification

single label classification

fine-grained emotion classification

representation learning

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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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.

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

Mental-health stigma remains a pervasive social problem that hampers treatment-seeking and recovery. 
Existing resources for training neural models to finely classify such stigma are limited, relying primarily on social-media or synthetic data without theoretical underpinnings. 
To remedy this gap, we present an expert-annotated, theory-informed corpus of human-chatbot interviews, comprising 4,141 snippets from 684 participants with documented socio-cultural backgrounds. 
Our experiments benchmark state-of-the-art neural models and empirically unpack the challenges of stigma detection. 
This dataset can facilitate research on computationally detecting, neutralizing, and counteracting mental-health stigma. 
Our corpus is openly available at https://github.com/HanMeng2004/Mental-Health-Stigma-Interview-Corpus.

What is Stigma Attributed to? A Theory-Grounded, Expert-Annotated Interview Corpus for Demystifying Mental-Health Stigma

The detection of sexism in online content remains an open problem, as harmful language disproportionately affects women and marginalized groups. While automated systems for sexism detection have been developed, they still face two key challenges: data sparsity and the nuanced nature of sexist language. Even in large, well-curated datasets like the Explainable Detection of Online Sexism (EDOS), severe class imbalance hinders model generalization. Additionally, the overlapping and ambiguous boundaries of fine-grained categories introduce substantial annotator disagreement, reflecting the difficulty of interpreting nuanced expressions of sexism. To address these challenges, we propose two prompt-based data augmentation techniques: Definition-based Data Augmentation (DDA), which leverages category-specific definitions to generate semantically-aligned synthetic examples, and Contextual Semantic Expansion (CSE), which targets systematic model errors by enriching examples with task-specific semantic features. To further improve reliability in fine-grained classification, we introduce an ensemble strategy that resolves prediction ties by aggregating complementary perspectives from multiple language models. Our experimental evaluation on the EDOS dataset demonstrates state-of-the-art performance across all tasks, with notable improvements of macro F1 by 1.5 points for binary classification (Task A) and 4.1 points for fine-grained classification (Task C).

Explaining Matters: Leveraging Definitions and Semantic Expansion for Sexism Detection

The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations. With the evolution of multi-modal large language models ((M)LLMs), this dream is closer to reality, as (M)LLM-based Agents using computers, mobile phones and web browsers by operating within the environments and interfaces (e.g., Graphical User Interface (GUI) and Command Line Interface (CLI)) provided by operating systems (OS) to automate tasks have significantly advanced. This paper presents a comprehensive survey on these advanced agents, designated as OS Agents. We begin by elucidating the fundamentals of OS Agents, exploring their key components and capabilities. We then examine methodologies for constructing OS Agents, focusing on domain-specific foundation models and agent frameworks. A detailed review of evaluation metrics and benchmarks highlights how OS Agents are assessed across diverse platforms and tasks. Finally, we discuss current challenges and identify promising directions for future research. An open-source GitHub repository is maintained as a dynamic resource to foster further innovation in this field.

OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use

Extensive LLM applications demand efficient structured generations, particularly for LR(1) grammars, to produce outputs in specified formats (e.g., JSON). 
Existing methods primarily parse LR(1) grammars into a pushdown automaton (PDA), leading to runtime execution overhead for context-dependent token processing, especially inefficient under large inference batches.
To address these issues, we propose $\text{Pre}^3$ that exploits deterministic pushdown automata (DPDA) to optimize the constrained LLM decoding efficiency.
First, by **pre**computing **pre**fix-conditioned edges during the **pre**processing, $\text{Pre}^3$ enables ahead-of-time edge analysis and thus makes parallel transition processing possible.
Futher, leveraging the prefix-conditioned edges, $\text{Pre}^3$ introduces a novel approach that transforms LR(1) transition graphs into DPDA, eliminating the need for runtime path exploration and achieving edge transitions with minimal overhead.
$\text{Pre}^3$ can be seamlessly integrated into standard LLM inference frameworks, improving time per output token (TPOT) by up to 40\% and throughput by up to 36\% in our experiments. Our code is available at https://github.com/ModelTC/lightllm.

Pre³: Enabling Deterministic Pushdown Automata for Faster Structured LLM Generation

We introduce DataMorgana, a tool for generating synthetic Q\&A benchmarks tailored to RAG applications in enterprise settings. DataMorgana enables customization of the generated benchmark according to the expected diverse traffic of the RAG application. It allows for specifying question types and their associated distribution via a lightweight configuration mechanism. 
We demonstrate via a series of quantitative and qualitative experiments that DataMorgana surpasses existing tools in terms of lexical, syntactic, and semantic diversity of the generated benchmark while maintaining high quality.
We run our experiments over domain-specific and general-knowledge public datasets, as well as two private datasets from governmental RAG applications: one for citizens and the other for government employees. The private datasets have been shared with us by AI71, an AI company, which has integrated DataMorgana into its offerings. In addition, DataMorgana has been offered to about 150 researchers worldwide as part of the SIGIR'2025 LiveRAG Challenge held in Spring 2025.

Generating Q&A Benchmarks for RAG Evaluation in Enterprise Settings

Modern Large Language Models (LLMs) have shown human-like abilities in many language tasks, sparking interest in comparing LLMs' and humans' language processing. In this paper, we try to answer two questions: 1. What makes garden-path sentences hard to understand for humans? 2. Do the same reasons make garden-path sentences hard for LLMs as well? Based on psycholinguistic research, we formulate hypotheses on why garden-path sentences are hard, and test these hypotheses on human participants and a large suite of LLMs using comprehension questions. Our findings reveal that both LLMs and humans struggle with specific syntactic complexities, with some models showing high correlation with human comprehension. To complement our findings, we test LLM comprehension of garden-path constructions with paraphrasing and text-to-image generation tasks, and find that the results mirror the sentence comprehension question results, further validating our findings on LLM understanding of these constructions.

When the LM misunderstood the human chuckled: Analyzing garden path effects in humans and language models

Language models are often miscalibrated, leading to confidently incorrect answers. We introduce GRACE, a benchmark for language model calibration that incorporates comparison with human calibration. GRACE consists of question-answer pairs, in which each question contains a series of clues that gradually become easier, all leading to the same answer; models must answer correctly as early as possible as the clues are revealed. This setting permits granular measurement of model calibration based on how early, accurately, and confidently a model answers. After collecting these questions, we host live human vs. model competitions to gather 1,749 data points on human and model teams' timing, accuracy, and confidence. We propose a metric, CalScore, that uses GRACE to analyze model calibration errors and identify types of model miscalibration that differ from human behavior. We find that although humans are less accurate than models, humans are generally better calibrated. Since state-of-the-art models struggle on GRACE, it effectively evaluates progress on improving model calibration.

GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration

Large language models (LLMs) have achieved remarkable progress in multilingual machine translation (MT), demonstrating strong performance even with limited parallel data. 
However, effectively fine-tuning LLMs for MT is challenging due to parameter interference, which arises from the conflicting demands of different language pairs and the risk of overwriting pre-trained knowledge. 
To address this issue, we propose \textbf{MLAS-LoRA}, a novel multiple language-aware LoRA knowledge transfer framework. 
MLAS-LoRA efficiently adapts LLMs to MT by selectively transferring knowledge from a large teacher to a small student model. 
Our approach first evaluates the awareness of neurons and extracts linguistic knowledge in the teacher model to both the general MT task and specific language pairs.
We then propose a multiple language-specific LoRA architecture to inject the extracted knowledge into the student model. 
During fine-tuning, only the parameters of the relevant language-general and language-specific LoRA modules are updated. 
Experimental results on diverse multilingual language pairs demonstrate that MLAS-LoRA significantly outperforms strong baselines by +1.7 BLEU on average, including standard fine-tuning and other parameter-efficient methods.

MLAS-LoRA: Language-Aware Parameters Detection and LoRA-Based Knowledge Transfer for Multilingual Machine Translation

Conditional dichotomy, the contrast between two outputs conditioned on the same context, is vital for applications such as debate, defeasible inference, and causal reasoning. Existing methods that rely on semantic similarity often fail to capture the nuanced oppositional dynamics essential for these applications. Motivated by these limitations, we introduce a novel task, Conditional Dichotomy Quantification (ConDQ), which formalizes the direct measurement of conditional dichotomy and provides carefully constructed datasets covering debate, defeasible natural language inference, and causal reasoning scenarios. To address this task, we develop the Dichotomy-oriented Geometric Embedding (DoGE) framework, which leverages complex-valued embeddings and a dichotomous objective to model and quantify these oppositional relationships effectively. Extensive experiments validate the effectiveness and versatility of DoGE, demonstrating its potential in understanding and quantifying conditional dichotomy across diverse NLP applications. Our code and datasets are available at https://github.com/cui-shaobo/conditional-dichotomy-quantification.

Conditional Dichotomy Quantification via Geometric Embedding

Despite the increasing interest in the reasoning abilities of Large Language Models (LLMs), existing work shows limitations in assessing logic abilities independently from lexical memory. We address this gap with Mystery-Zebra. This robust two-part benchmark (4,290 puzzles) challenges the logic abstraction abilities of LLMs in two setups: (1) a lexical obfuscation setup tests the dependence of LLMs on lexical content based on two canonical grid puzzles widely spread on the Internet; (2) a set of new grid puzzles in 42 different sizes and 12 difficulty levels tests how the formal difficulty degree of a puzzle affects LLMs.
We test open and closed-weight LLMs on both parts of the benchmark. The results on part two suggest that model sizes up to 70B parameters have only a minor influence when solving newly generated puzzles, while performance mainly relates to the number of items in the puzzle. The results on the first part of the benchmark suggest that the applied obfuscation strategies help to mitigate effects of logic puzzles being part of LLM training data, showing a drastic drop in performance for obfuscated versions of well-known puzzles. In addition we conduct a case-study on the first part of the benchmark predicting the position of single items, unveiling that the reasoning abilities of LLMs are mainly limited to a few consecutive steps of reasoning.

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