Papers with principal component analysis
LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation (2025.emnlp-main)
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| Challenge: | Modern automatic speech recognition systems rely on encoder-decoder architectures and their encoders are a critical bottleneck for efficient deployment due to high computational intensity. |
| Approach: | They propose a low-rank compression scheme for ASR encoders that leverages the strong low-ranked properties observed in intermediate activations and approximates linear transformations with a chain of low-Rank matrix multiplications. |
| Outcome: | The proposed method reduces inference costs while maintaining transcription accuracy while preserving low-rank properties observed in intermediate activations. |
Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval (2021.emnlp-main)
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| Challenge: | Dense passage retrieval improves ranking accuracy in open-domain question answering but at the cost of large space and memory requirements. |
| Approach: | They propose a simple unsupervised pipeline that includes principal component analysis (PCA), product quantization, and hybrid search to improve space efficiency. |
| Outcome: | The proposed pipeline achieves good accuracy–space trade-offs, for example, 48 compression with less than 3% drop in top-100 retrieval accuracy on average or 96 compression without drop in space requirements. |
Probing the Uniquely Identifiable Linguistic Patterns of Conversational AI Agents (2024.findings-acl)
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| Challenge: | Recent advances in deep learning and natural language processing have led to the proliferation of conversational AI agents. |
| Approach: | They construct linguistic profiles for five CAAs and use authorship attribution techniques to identify uniquely identifieable linguistic patterns for each model. |
| Outcome: | The proposed model identifies unique identifiers (UILPs) for each CAA using authorship attribution techniques. |
A New Public Corpus for Clinical Section Identification: MedSecId (2022.coling-1)
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| Challenge: | a study aims to segment sections of clinical medical domain documentation . section identification is a process by which sections are demarcated and labeled . |
| Approach: | They use a set of 2,002 fully annotated medical notes from the MIMIC-III to segment sections in clinical medical domain documentation. |
| Outcome: | The proposed model shows that medical concepts are related across sections using principal component analysis. |
Principal Component Analysis as a Sanity Check for Bayesian Phylolinguistic Reconstruction (2024.lrec-main)
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| Challenge: | Existing methods to reconstruct the evolutionary history of languages rely on the tree model . however, this assumption is violated to varying degrees due to contact and other factors . |
| Approach: | They propose a Bayesian tree model that assumes languages descended from a common ancestor and underwent modifications over time. |
| Outcome: | The proposed method visualizes anomalies in the form of jogging using synthetic and real data. |
Monotonic Scaffolding as a Diagnostic Lens for Legal Reasoning in LLMs (2026.acl-long)
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| Challenge: | Modern evaluation of Legal QA systems is shifting from terminal accuracy toward process-aware analyses of model reasoning. |
| Approach: | They propose a diagnostic framework grounded in monotonic scaffolding where language models receive gold-standard, case-relevant information across stages aligned with the canonical legal framework FIRAC. |
| Outcome: | The proposed framework evaluates 3,123 Brazilian Bar Exam questions . it shows that terminal accuracy overestimates legal reasoning competence . |