Papers with scalable method
Scalable and Interpretable Semantic Change Detection (2021.naacl-main)
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| Challenge: | Several cluster-based methods for word usage change detection are unscalable in terms of memory consumption and computation time. |
| Approach: | They propose a scalable method for word usage-change detection that uses contextual embeddings to aggregate word usages into clusters. |
| Outcome: | The proposed method offers high performance and interpretability while being unscalable. |
Code Execution as Grounded Supervision for LLM Reasoning (2025.emnlp-main)
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| Challenge: | Existing methods for generating high-quality CoT data rely on costly human annotations and error-prone CoT. |
| Approach: | They propose a method that extracts verifiable, step-by-step reasoning traces from code execution and transforms them into a natural language CoT reasoning. |
| Outcome: | The proposed method produces highly accurate reasoning data and reduces overall token length during inference by reducing meaningless repetition and overthinking. |
Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion (2026.acl-long)
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| Challenge: | Existing methods for audio captioning lack fine-grained detail and contextual accuracy due to limited unimodal or superficial information. |
| Approach: | They propose a two-stage automated pipeline that uses pretrained models to extract contextual cues from video . a large language model synthesizes these inputs to generate detailed and context-aware captions . |
| Outcome: | The proposed method is scalable and generates detailed and context-aware captions on large-scale audio datasets. |