Austria

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.

ACL 2025

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

mental health stigma

nlp datasets

language resources

corpus creation

interview

## Welcome to EMNLP 2022!
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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.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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

Large Language Models (LLMs) have become foundational in human-computer interaction, demonstrating remarkable linguistic capabilities across various tasks. However, there is a growing concern about their potential to perpetuate social biases present in their training data. In this paper, we comprehensively investigate the vulnerabilities of contemporary LLMs to various social bias attacks, including prefix injection, refusal suppression, and learned attack prompts. We evaluate popular models such as LLaMA-2, GPT-3.5, and GPT-4 across gender, racial, and religious bias types. Our findings reveal that models are generally more susceptible to gender bias attacks compared to racial or religious biases. We also explore novel aspects such as cross-bias and multiple-bias attacks, finding varying degrees of transferability across bias types. Additionally, our results show that larger models and pretrained base models often exhibit higher susceptibility to bias attacks. These insights contribute to the development of more inclusive and ethically responsible LLMs, emphasizing the importance of understanding and mitigating potential bias vulnerabilities. We offer recommendations for model developers and users to enhance the robustness of LLMs against social bias attacks.

Understanding Large Language Model Vulnerabilities to Social Bias Attacks

In this work, we investigate an important task named instruction-following text embedding, which generates dynamic text embeddings that adapt to user instructions, highlighting specific attributes of text. Despite recent advancements, existing approaches suffer from significant computational overhead, as they require re-encoding the entire corpus for each new instruction. To address this challenge, we propose GSTransform, a novel instruction-following text embedding framework based on Guided Space Transformation. Our key observation is that instruction-relevant information is inherently encoded in generic embeddings but remains underutilized. Instead of repeatedly encoding the corpus for each instruction, GSTransform is a lightweight transformation mechanism that adapts pre-computed embeddings in real time to align with user instructions, guided by a small amount of text data with instruction-focused label annotation. We conduct extensive experiments on three instruction-awareness downstream tasks across nine real-world datasets, demonstrating that GSTransform improves instruction-following text embedding quality over state-of-the-art methods while achieving dramatic speedups of 6$\sim$300$\times$ in real-time processing on large-scale datasets. The source code is available at https://github.com/YingchaojieFeng/GSTransform.

Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation

Simulations of human behavior based on large language models (LLMs) have the potential to revolutionize the social and behavioral sciences, if and only if they faithfully reflect real human behaviors. Prior work across many disciplines has evaluated the simulation capabilities of specific LLMs in specific experimental settings, but often produced disparate results. To move towards a more robust understanding, we introduce SimBench, the first large-scale benchmark to evaluate how well LLMs can simulate group-level human behaviors across diverse settings and tasks. SimBench compiles 20 datasets in a unified format, measuring diverse types of behavior (e.g., decision-making vs. self-assessment) across hundreds of thousands of diverse participants from different parts of the world. Using SimBench, we can ask fundamental questions regarding when, how, and why LLM simulations succeed or fail. For example, we show that, while even the best LLMs today have limited simulation ability, there is a clear log-linear scaling relationship with model size, and a strong correlation between simulation and scientific reasoning abilities. We also show that base LLMs, on average, are better at simulating high-entropy response distributions, while the opposite holds for instruction-tuned LLMs. By making progress measurable, we hope that SimBench can accelerate the development of better LLM simulators in the future.

SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors

Language models (LMs) require effective episodic grounding—the ability to learn from and apply past experiences—to perform well at physical planning tasks. While current approaches struggle with scalability and integration of episodic memory, which is particularly limited for medium-sized LMs (7B parameters), larger LMs (70-405B) offer untapped potential through their hierarchical representations and extensive pre-trained knowledge. Therefore, to unlock larger LMs' potential for grounding, we present a scalable weak-to-strong episodic learning framework that efficiently transfers episodic behaviors from smaller to larger LMs. It uses Monte Carlo tree search for structured experience collection with a novel distillation method that preserves LM capabilities while incorporating episodic memory. This enables larger LMs to leverage their inherent advantages for improved physical planning. Experiments show our solution outperforms top proprietary LMs by 3.45\% across diverse planning and question-answering tasks. Layer-wise probing reveals systematic improvements in task alignment, particularly in later LM layers. It shows stable generalization to even unseen scenarios, even as planning steps increase, whereas baselines deteriorate sharply beyond a complexity threshold of four planning steps.

Growing Through Experience: Scaling Episodic Grounding in Language Models

As text generation systems' outputs are increasingly anthropomorphic—perceived as human-like—scholars have also increasingly raised concerns about how such outputs can lead to harmful outcomes, such as users over-relying or developing emotional dependence on these systems. How to intervene on such system outputs to mitigate anthropomorphic behaviors and their attendant harmful outcomes, however, remains understudied. With this work, we aim to provide empirical and theoretical grounding for developing such interventions. To do so, we compile an inventory of interventions grounded both in prior literature and a crowdsourcing study where participants edited system outputs to make them less human-like. Drawing on this inventory, we also develop a conceptual framework to help characterize the landscape of possible interventions, articulate distinctions between different types of interventions, and provide a theoretical basis for evaluating the effectiveness of different interventions.

Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text Generation Systems

Automatic translation systems offer a powerful solution to bridge language barriers in scenarios where participants do not share a common language. However, these systems can introduce errors leading to misunderstandings and conversation breakdown. A key issue is that current systems fail to incorporate the rich contextual information necessary to resolve ambiguities and omitted details, resulting in literal, inappropriate, or misaligned translations. In this work, we present a framework to improve large language model-based translation systems by incorporating contextual information in bilingual conversational settings during training and inference. We validate our proposed framework on two task-oriented domains: customer chat and user-assistant interaction. Across both settings, our framework consistently results in better translations than state-of-the-art systems like GPT-4o and TowerInstruct, as measured by multiple automatic translation quality metrics on several language pairs. We also show that the resulting model leverages context in an intended and interpretable way, improving consistency between the conveyed message and the generated translations.

A Context-aware Framework for Translation-mediated Conversations

We analyze the evolution of English noun compounds, which we represent as vectors of time-specific values. We implement a wide array of methods to create a rich set of features, using them to classify compounds for present-day compositionality and to assess the informativeness of the corresponding linguistic patterns. Our best results use BERT – reflecting the similarity of compounds and sentence contexts – and we further capture relevant and complementary information across approaches. Leveraging these feature differences, we find that the development of low-compositional meanings is reflected by a parallel drop in compositionality and sustained semantic change. The same distinction is echoed in transformer processing: compositionality estimates require far less contextualization than semantic change estimates.

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Explaining Matters: Leveraging Definitions and Semantic Expansion for Sexism Detection

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Gendered Mental Health Stigma in Masked Language Models