Austria

A common use of NLP is to facilitate the understanding of large document collections, with models based on Large Language Models (LLMs) replacing probabilistic topic models. Yet the effectiveness of LLM-based approaches in real-world applications remains under explored. This study measures the knowledge users acquire with topic models—including traditional, unsupervised and supervised LLM- based approaches—on two datasets. While LLM-based methods generate more human- readable topics and show higher average win probabilities than traditional models for data exploration, they produce overly generic topics for domain-specific datasets that do not easily allow users to learn much about the documents. Adding human supervision to LLM-based topic models improves data exploration by addressing hallucination and genericity but requires more human efforts. In contrast, traditional models like Latent Dirichlet Allocation (LDA) remain effective for exploration but are less user-friendly. This paper provides best practices—there is no one right model, the choice of models is situation-specific—and suggests potential improvements for scalable LLM- based topic models.

ACL 2025

LLMs Struggle to Describe the Haystack without Human Help: A Social Science-Inspired Evaluation of Topic Models

topic modeling evaluation

large language model for topic modeling

interpretability and analysis of models for nlp

topic modeling

computational social science

**Welcome to ACL-IJCNLP 2021!**

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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

Large Language Models (LLMs) are increasingly deployed in real-world applications that demand complex reasoning. To track progress, robust benchmarks are required to evaluate their capabilities beyond superficial pattern recognition. However, current LLM reasoning benchmarks often face challenges such as insufficient interpretability, performance saturation or data contamination. To address these challenges, we introduce GAMEBoT, a gaming arena designed for rigorous and transparent assessment of LLM reasoning capabilities. GAMEBoT decompose complex reasoning in games into predefined modular subproblems. This decomposition allows us to design a suite of Chain-of-Thought (CoT) prompts infused with domain knowledge to guide LLMs in addressing these subproblems before action selection. Furthermore, we develop a suite of rule-based algorithms to generate ground truth for these subproblems, enabling rigorous validation of the LLMs’ intermediate reasoning steps. This approach facilitates evaluation of both the quality of final actions and the accuracy of the underlying reasoning process. GAMEBoT also naturally alleviates the risk of data contamination through dynamic games and head-to-head LLM competitions. We benchmark 17 prominent LLMs across eight games, encompassing various strategic abilities and game characteristics. Our results suggest that GAMEBoT presents a significant challenge, even when LLMs are provided with detailed CoT prompts.

Beyond Outcomes: Transparent Assessment of LLM Reasoning in Games

Despite progress in Arabic large language models, such as Jais and AceGPT, their evaluation on commonsense reasoning has largely relied on machine-translated datasets, which lack cultural depth and may introduce Anglocentric biases. Commonsense reasoning is shaped by geographical and cultural contexts, and existing English datasets fail to capture the diversity of the Arab world. To address this, we introduce \datasetname, a commonsense reasoning dataset in Modern Standard Arabic (MSA), covering cultures of 13 countries across the Gulf, Levant, North Africa, and the Nile Valley. The dataset was built from scratch by engaging native speakers to write and validate culturally relevant questions for their respective countries. \datasetname spans 12 daily life domains with 54 fine-grained subtopics, reflecting various aspects of social norms, traditions, and everyday experiences. Zero-shot evaluations show that open-weight language models with up to 32B parameters struggle to comprehend diverse Arab cultures, with performance varying across regions. These findings highlight the need for more culturally aware models and datasets tailored to the Arabic-speaking world.

Commonsense Reasoning in Arab Culture

Learning from preference feedback is essential for aligning large language models (LLMs) with human values and improving the quality of generated responses. However, existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale. In this work, we present PUGC, a novel framework that leverages implicit human Preferences in unlabeled User-Generated Content (UGC) to generate preference data. Although UGC is not explicitly created to guide LLMs in generating human-preferred responses, it often reflects valuable insights and implicit preferences from its creators that has the potential to address readers' questions. PUGC transforms UGC into user queries and generates responses from the policy model. The UGC is then leveraged as a reference text for response scoring, aligning the model with these implicit preferences. This approach improves the quality of preference data while enabling scalable, domain-specific alignment. Experimental results on Alpaca Eval 2 show that models trained with DPO and PUGC achieve a 9.37% performance improvement over traditional methods, setting a 35.93% state-of-the-art length-controlled win rate using Mistral-7B-Instruct. Further studies highlight gains in reward quality, domain-specific alignment effectiveness, robustness against UGC quality, and theory of mind capabilities. Our code and dataset are available at https://zhaoxuan.info/PUGC.github.io/.

Aligning Large Language Models with Implicit Preferences from User-Generated Content

Recent calls for pluralistic alignment of Large Language Models (LLMs) encourage adapting models to diverse user preferences. However, most prior work on personalized reward models heavily rely on additional identity information, such as demographic details or a predefined set of preference categories. To this end, we introduce SynthesizeMe, an approach to inducing synthetic user personas from user interactions for personalized reward modeling. SynthesizeMe first generates and verifies reasoning to explain user preferences, then induces synthetic user personas from that reasoning, and finally filters to informative prior user interactions in order to build personalized prompts for a particular user. We show that using SynthesizeMe induced prompts improves personalized LLM-as-a-judge accuracy by 4.4% on Chatbot Arena. Combining SynthesizeMe derived prompts with a reward model achieves top performance on PersonalRewardBench: a new curation of user-stratified interactions with chatbots collected from 854 users of Chatbot Arena and PRISM.

SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMs

Legal text poses distinctive challenges for natural language processing. The legal import of a term may depend on omissions, cross-references, or silence, Further, legal text is often susceptible to multiple valid, conflicting interpretations; as the saying goes: a good lawyer's answer to any question is "it depends."

This work introduces a new, hand-coded dataset for the interpretation of privacy policies. It includes privacy policies from 149 firms, including materials incorporated by reference. The policies are annotated across 64 dimension that reflect the applicable legal rules and contested terms from EU and US privacy regulation and litigation. Our annotation methodology is designed to capture the capture core challenges peculiar to legal language, including indeterminacy, interdependence between clauses, meaningful silence, and the implications of legal defaults. We present a set of baseline results for the dataset using current large language models.

Building a Long Text Privacy Policy Corpus with Multi-Class Labels

Reasoning is an intricate process that transcends both language and vision; yet, despite its inherently modality-agnostic nature, develop-
ing effective multilingual and multimodal reasoning capabilities remains a substantial challenge for Multimodal Large Language Models (MLLMs). They struggle to activate complex reasoning behaviours, delivering step-wise explanation, questioning and reflection, particularly in multilingual settings where high-quality supervision across languages is lacking. Recent works have introduced eclectic strategies to enhance MLLMs’ reasoning; however, they remain related to a single language.
To make MLLMs’ reasoning capabilities aligned among languages and improve modality performances, we propose R2-MultiOmnia, a modular approach that instructs the models to abstract key elements of the reasoning process and then refine reasoning trajectories via self-correction. Specifically, we instruct the models producing multimodal synthetic resources by bridging modalities and then self-improving their capabilities. To stabilise learning and the reasoning processes structure, we propose Curriculum Learning Reasoning Stabilisation with structured output rewards to gradually refine the models’ capabilities to learn and deliver robust reasoning processes. Experiments show that R2-MultiOmnia improves multimodal reasoning, gets aligned performances among the languages approaching strong models.

x-SAL: Leading Symbolic Reasoning across Languages via Cross-lingual Symbolic-Aided Language Model

Large Language Models (LLMs) have achieved remarkable success in Natural Language Processing (NLP), yet their cross-lingual consistency remains a significant challenge. This paper introduces a novel methodology for efficiently identifying inherent cross-lingual weaknesses in LLMs. Our approach leverages beam search and LLM-based simulation to generate bilingual question pairs that expose performance discrepancies between English and target languages. We construct a new dataset of over 6,000 bilingual pairs across 16 languages using this methodology, demonstrating its effectiveness in revealing weaknesses even in state-of-the-art models. The extensive experiments demonstrate that our method precisely and cost-effectively pinpoints cross-lingual weaknesses, consistently revealing over 50% accuracy drops in target languages across a wide range of models. Moreover, further experiments investigate the relationship between linguistic similarity and cross-lingual weaknesses, revealing that linguistically related languages share similar performance patterns and benefit from targeted post-training. Code is available at https://github.com/xzx34/Cross-Lingual-Pitfalls.

Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models

The paper explores the performance of LLMs in the context of multi-dimensional analytic writing assessments, i.e. their ability to provide both scores and comments based on multiple assessment criteria. Using a corpus of literature reviews written by L2 graduate students and assessed by human experts against 9 analytic criteria, we prompt several popular LLMs to perform the same task under various conditions. To evaluate the quality of feedback comments, we apply a novel feedback comment quality evaluation framework. This framework is interpretable, cost-efficient, scalable, and reproducible, compared to existing methods that rely on manual judgments. We find that LLMs can generate reasonably good and generally reliable multi-dimensional analytic assessments. We release our corpus and code for reproducibility.

LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing

Despite progress in comment-aware multimodal and multilingual summarization for English and Chinese, research in Indian languages remains limited. This study addresses this gap by introducing COSMMIC, a pioneering comment-sensitive multimodal, multilingual dataset featuring nine major Indian languages. COSMMIC comprises 4,959 article-image pairs and 24,484 reader comments, with ground-truth summaries available in all included languages. Our approach enhances summaries by integrating reader insights and feedback. We explore summarization and headline generation across four configurations: (1) using article text alone, (2) incorporating user comments, (3) utilizing images, and (4) combining text, comments, and images. To assess the dataset’s effectiveness, we employ state-of-the-art language models such as LLama3 and GPT-4. We conduct a comprehensive study to evaluate different component combinations, including identifying supportive comments, filtering out noise using a dedicated comment classifier using IndicBERT, and extracting valuable insights from images with a multilingual CLIP-based classifier. This helps determine the most effective configurations for natural language generation (NLG) tasks. Unlike many existing datasets that are either text-only or lack user comments in multimodal settings, COSMMIC uniquely integrates text, images, and user feedback. This holistic approach bridges gaps in Indian language resources, advancing NLP research and fostering inclusivity.

COSMMIC: Comment-Sensitive Multimodal Multilingual Indian Corpus for Summarization and Headline Generation

Predicting semantic textual similarity (STS) is a complex and ongoing challenge in natural language processing (NLP). Over the years, researchers have developed a variety of supervised and unsupervised approaches to calculate STS automatically. Additionally, various benchmarks, which include STS datasets, have been established to consistently evaluate and compare these STS methods. However, they largely focus on high-resource languages, mixed with datasets annotated focusing on relatedness instead of similarity and containing automatically translated instances. Therefore, no dedicated benchmark for multilingual STS exists. To solve this gap, we introduce the Multilingual Semantic Textual Similarity Benchmark (MUSTS), which spans 13 languages, including low-resource languages. By evaluating more than 25 models on MUSTS, we establish the most comprehensive benchmark of multilingual STS methods. Our findings confirm that STS remains a challenging task, particularly for low-resource languages.

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Beyond Outcomes: Transparent Assessment of LLM Reasoning in Games

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