Austria

Knowledge tracing (KT) in programming education presents unique challenges due to the complexity of coding tasks and the diverse methods students use to solve problems. Although students&#39; questions often contain valuable signals about their understanding and misconceptions, traditional KT models often neglect to incorporate these questions as inputs to address these challenges. This paper introduces SQKT (Students&#39; Question-based Knowledge Tracing), a knowledge tracing model that leverages students&#39; questions and automatically extracted skill information to enhance the accuracy of predicting students&#39; performance on subsequent problems in programming education. Our method creates semantically rich embeddings that capture not only the surface-level content of the questions but also the student&#39;s mastery level and conceptual understanding. Experimental results demonstrate SQKT&#39;s superior performance in predicting student completion across various Python programming courses of differing difficulty levels. In in-domain experiments, SQKT achieved a 33.1% absolute improvement in AUC compared to baseline models. The model also exhibited robust generalization capabilities in cross-domain settings, effectively addressing data scarcity issues in advanced programming courses. SQKT can be used to tailor educational content to individual learning needs and design adaptive learning systems in computer science education.

ACL 2025

Knowledge Tracing in Programming Education Integrating Students&#39; Questions

knowledge tracing

deep learning

education

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Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Knowledge tracing (KT) in programming education presents unique challenges due to the complexity of coding tasks and the diverse methods students use to solve problems. Although students' questions often contain valuable signals about their understanding and misconceptions, traditional KT models often neglect to incorporate these questions as inputs to address these challenges. This paper introduces SQKT (Students' Question-based Knowledge Tracing), a knowledge tracing model that leverages students' questions and automatically extracted skill information to enhance the accuracy of predicting students' performance on subsequent problems in programming education. Our method creates semantically rich embeddings that capture not only the surface-level content of the questions but also the student's mastery level and conceptual understanding. Experimental results demonstrate SQKT's superior performance in predicting student completion across various Python programming courses of differing difficulty levels. In in-domain experiments, SQKT achieved a 33.1% absolute improvement in AUC compared to baseline models. The model also exhibited robust generalization capabilities in cross-domain settings, effectively addressing data scarcity issues in advanced programming courses. SQKT can be used to tailor educational content to individual learning needs and design adaptive learning systems in computer science education.

Knowledge Tracing in Programming Education Integrating Students' Questions

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)

Composed Image Retrieval (CIR) enables users to search for images using multimodal queries that combine text and reference images. While metric learning methods have shown promise, they rely on deterministic point embeddings that fail to capture the inherent uncertainty in the input data, in which user intentions may be imprecisely specified or open to multiple interpretations. We address this challenge by reformulating CIR through our proposed Composed Probabilistic Embedding (CoPE) framework, which represents both queries and targets as Gaussian distributions in latent space rather than fixed points. Through careful design of probabilistic distance metrics and hierarchical learning objectives, CoPE explicitly captures uncertainty at both instance and feature levels, enabling more flexible, nuanced, and robust matching that can handle polysemy and ambiguity in search intentions. Extensive experiments across multiple benchmarks demonstrate that CoPE effectively quantifies both quality and semantic uncertainties within Composed Image Retrieval, achieving state-of-the-art performance on recall rate. Code: https://github.com/tanghme0w/ACL25-CoPE.

Modeling Uncertainty in Composed Image Retrieval via Probabilistic Embedding

Floor plans serve as a graphical language through which architects sketch and communicate their design ideas. Actually, in the Architecture, Engineering, and Construction (AEC) design stages, generating floor plans is a complex task requiring domain expertise and alignment with user requirements. However, existing evaluation methods for floor plan generation rely mainly on statistical metrics like FID, GED, and PSNR, which often fail to evaluate using domain knowledge. As a result, even high-performing models on these metrics struggle to generate viable floor plans in practice. To address this, (1) we propose ArchiMetricsNet, the first floor plan dataset that includes functionality, flow, and overall evaluation scores, along with detailed textual analyses. We trained FloorPlan-MPS (Multi-dimensional Preference Score) on it. (2) We develope FloorPlan-LLaMa, a floor plan generation model based on autoregressive framework. To integrate architects' professional expertise and preferences, FloorPlan-MPS serves as the reward model during the RLHF (Reinforcement Learning from Human Feedback) process, aligning FP-LLaMa with the needs of the architectural community. (3) Comparative experiments demonstrate that our method outperforms baseline models in both text-conditional and class-conditional tasks. Validation by professional architects confirms that our approach yields more rational plans and aligns better with human preferences.

FloorPlan-LLaMa: Aligning Architects' Feedback and Domain Knowledge in Architectural Floor Plan Generation

Understanding domain-specific theorems often requires more than just text-based reasoning; effective communication through structured visual explanations is crucial for deeper comprehension. While large language models (LLMs) demonstrate strong performance in text-based theorem reasoning, their ability to generate coherent and pedagogically meaningful visual explanations remains an open challenge. In this work, we introduce TheoremExplainAgent, an agentic approach for generating long-form theorem explanation videos (over 5 minutes) using Manim animations. To systematically evaluate multimodal theorem explanations, we propose TheoremExplainBench, a benchmark covering 240 theorems across multiple STEM disciplines, along with 5 automated evaluation metrics. Our results reveal that agentic planning is essential for generating detailed long-form videos, and the o3-mini agent achieves a success rate of 93.8% and an overall score of 0.77. However, our quantitative and qualitative studies show that most of the videos produced exhibit minor issues with visual element layout. Furthermore, multimodal explanations expose deeper reasoning flaws that text-based explanations fail to reveal, highlighting the importance of multimodal explanations.

TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understanding

Research in linguistics shows that non-verbal cues, such as gestures, play a crucial role in spoken discourse. For example, speakers perform hand gestures to indicate topic shifts, helping listeners identify transitions in discourse. In this work, we investigate whether the joint modeling of gestures using human motion sequences and language can improve spoken discourse modeling in language models. To integrate gestures into language models, we first encode 3D human motion sequences into discrete gesture tokens using a VQ-VAE. These gesture token embeddings are then aligned with text embeddings through feature alignment, mapping them into the text embedding space. To evaluate the gesture-aligned language model on spoken discourse, we construct text infilling tasks targeting three key discourse cues grounded in linguistic research: discourse connectives, stance markers, and quantifiers. Results show that incorporating gestures enhances marker prediction accuracy across the three tasks, highlighting the complementary information that gestures can offer in modeling spoken discourse. We view this work as an initial step toward leveraging non-verbal cues to advance spoken language modeling in language models.

Enhancing Spoken Discourse Modeling in Language Models Using Gestural Cues

Multi-turn interactions between large language models (LLMs) and users naturally include implicit feedback signals. If an LLM responds in an unexpected way to an instruction, the user is likely to signal it by rephrasing the request, expressing frustration, or pivoting to an alternative task. Such signals are task-independent and occupy a relatively constrained subspace of language, allowing the LLM to identify them even if it fails on the actual task. We introduce ReSpect, a method to learn from such signals in past interactions via retrospection without additional annotations. We deploy ReSpect in a new multimodal interaction scenario, where humans instruct a multimodal LLM to solve an abstract reasoning task with a combinatorial solution space. Through thousands of interactions with humans, we show how ReSpect gradually improves task completion rate from 31% to 82%, all without any external annotation.

Retrospective Learning from Interactions

Sign language processing has traditionally relied on task-specific models, limiting the potential for transfer learning across tasks. Pre-training methods for sign language have typically focused on either supervised pre-training, which cannot take advantage of unlabeled data, or context-independent (frame or video segment) representations, which ignore the effects of relationships across time in sign language. We introduce SHuBERT (Sign Hidden-Unit BERT), a self-supervised contextual representation model learned from approximately 1,000 hours of American Sign Language video. SHuBERT adapts masked token prediction objectives to multi-stream visual sign language input, learning to predict multiple targets corresponding to clustered hand, face, and body pose streams. SHuBERT achieves state-of-the-art performance across multiple tasks including sign language translation, isolated sign language recognition, and fingerspelling detection.

SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction

Text-to-image diffusion models have recently enabled the creation of visually compelling, detailed images from textual prompts. However, their ability to accurately represent various cultural nuances remains an open question. In our work, we introduce CULTDIFF benchmark, evaluating whether state-of-the-art diffusion models can generate culturally specific images spanning ten countries. We show that these models often fail to generate cultural artifacts in architecture, clothing, and food, especially for underrepresented country regions, by conducting a fine-grained analysis of different similarity aspects, revealing significant disparities in cultural relevance, description fidelity, and realism compared to real-world reference images. With the collected human evaluations, we develop a neural-based image-image similarity metric, namely, CULTDIFF-S, to predict human judgment on real and generated images with cultural artifacts. Our work highlights the need for more inclusive generative AI systems and equitable dataset representation over a wide range of cultures.

Diffusion Models Through a Global Lens: Are They Culturally Inclusive?

Recent English Common Crawl datasets like FineWeb-Edu and DCLM achieved significant benchmark gains via aggressive model-based filtering, but at the cost of removing 90% of data. This limits their suitability for long token horizon training, such as 15T tokens for Llama 3.1. In this paper, we show how to achieve better trade-offs between accuracy and data quantity by a combination of classifier ensembling, synthetic data rephrasing, and reduced reliance on heuristic filters. When training 8B parameter models for 1T tokens, using a high-quality subset of our data improves MMLU by 5.6 over DCLM, demonstrating the efficacy of our methods for boosting accuracies over a relatively short token horizon. Furthermore, our full 6.3T token dataset matches DCLM on MMLU, but contains four times more unique real tokens than DCLM. This unlocks state-of-the-art training over a long token horizon: an 8B parameter model trained for 15T tokens, of which 7.2T came from our dataset, is better than the Llama 3.1 8B model: +5 on MMLU, +3.1 on ARC-Challenge, and +0.5 on average across ten diverse tasks. The dataset is available at https://data.commoncrawl.org/contrib/Nemotron/Nemotron-CC/index.html.

Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset

Training LLMs with Mixture-of-Experts (MoE) architecture on long sequences poses significant challenges due to the all-to-all communication bottleneck of expert parallelism. While existing approaches attempt to hide the communication costs in computation through token-level pipelining within MoE layers, their effectiveness is limited by the insufficient computation. We present FoldMoE, a high-performance MoE training system that enables token-level overlapping across entire Transformer blocks through novel attention-MoE pipelining. We propose an efficient pipeline schedule, and a novel token buffering design to decouple attention and MoE layer partitioning, along with a time-uniform micro-batching strategy for enhanced efficiency. Evaluations on GPT-MoE models with sequences up to 32K tokens show that FoldMoE achieves up to 1.49x and 2.72x speedup over state-of-the-art token-level overlapping and non-overlapping baselines respectively.

FoldMoE: Efficient Long Sequence MoE Training via Attention-MoE Pipelining

Due to the immense resource demands and the involved complex techniques, it is still challenging for successfully pre-training a large language models (LLMs) with state-of-the-art performance. In this paper, we explore the key bottlenecks and designs during pre-training, and make the following contributions: (1) a comprehensive investigation into the factors contributing to training instability; (2) a robust optimization approach designed to mitigate training instability effectively; (3) an elaborate data pipeline that integrates data synthesis, data curriculum, and data selection. By integrating the above techniques, we create a rather low-cost training recipe and use it to pre-train YuLan-Mini, a fully-open base model with 2.4B parameters on 1.08T tokens. Remarkably, YuLan-Mini achieves top-tier performance among models of similar parameter scale, with comparable performance to industry-leading models that require significantly more data. To facilitate reproduction, we release the full details of training recipe and data composition. Project details can be accessed at the following link: https://anonymous.4open.science/r/YuLan-Mini/README.md.

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Modeling Uncertainty in Composed Image Retrieval via Probabilistic Embedding

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