Papers with large-scale applications
Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning (D19-55)
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| Challenge: | Question Paraphrase Retrieval (QPR) systems can be used to answer rare and noisy reformulations of common questions by mapping them to a set of canonical forms. |
| Approach: | They propose a Question Paraphrase Retrieval (QPR) system that retrieves equivalent questions that result in the same answer as the original question. |
| Outcome: | The proposed system outperforms the standard loss function in NIR with noisy labels on two QPR datasets. |
Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling (D18-1)
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| Challenge: | Existing efforts to train pre-trained language models have brought significant improvements to various NLP applications. |
| Approach: | They propose to compress bulky LMs while preserving useful information for a specific task. |
| Outcome: | The proposed method can detach any layer without affecting others, and stretch shallow and wide LMs to be deep and narrow. |
Free Lunch for Efficient Textual Commonsense Integration in Language Models (2023.acl-long)
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| Challenge: | Recent years have witnessed the emergence of textual commonsense knowledge bases, aimed at providing more nuanced and context-rich knowledge. |
| Approach: | They propose to group training samples with similar commonsense descriptions into a single batch and reuse the encoded description across multiple samples. |
| Outcome: | The proposed method reduces the computational cost while preserving performance on larger datasets and on devices with more memory capacity. |
Revisiting Document Representations for Large-Scale Zero-Shot Learning (2021.naacl-main)
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| Challenge: | Existing methods for visual recognition use visual attributes carefully annotated by humans. |
| Approach: | They propose a semi-automatic mechanism for visual sentence extraction that leverages document section headers and clustering structure of visual sentences. |
| Outcome: | The proposed method improves on the ImageNet dataset with 10,000 unseen classes. |
Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings (2023.emnlp-main)
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Mattia Atzeni, Mikhail Plekhanov, Frederic Dreyer, Nora Kassner, Simone Merello, Louis Martin, Nicola Cancedda
| Challenge: | Entity linking methods based on dense retrieval are often not efficient in large-scale applications as they are sensitive to the structure of the embedding space. |
| Approach: | They propose a method to infuse structural information into the space of entity representations by using prior knowledge of entity types. |
| Outcome: | The proposed method outperforms other type-aware approaches and matches generative models with 18 times more parameters. |
RRInf: Efficient Influence Function Estimation via Ridge Regression for Large Language Models and Text-to-Image Diffusion Models (2025.emnlp-main)
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| Challenge: | Influence function estimates the impact of training data on model predictions . high computational cost has hindered their applicability in large-scale applications. |
| Approach: | They propose a method to quantify the impact of training data on model predictions . they use a ridge regression problem to transform the estimation into a problem . |
| Outcome: | The proposed method outperforms existing methods on noisy data detection and influential data identification tasks. |
Instant Personalized Large Language Model Adaptation via Hypernetwork (2026.acl-long)
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Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen, Zheng Li, Rongzhi Zhang, Pei Chen, Fengran Mo, Zheyuan Liu, Qingkai Zeng, Qingyu Yin, Meng Jiang
| Challenge: | Existing parameter-efficient fine-tuning methods require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates. |
| Approach: | They propose a scalable framework that maps a user's profile directly to a full set of adapter parameters. |
| Outcome: | The proposed framework outperforms prompt-based personalization and OPPU while using substantially fewer computational resources at deployment. |
TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks (2025.findings-acl)
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| Challenge: | Existing models with limited performance and limited training can be difficult to use in large-scale applications. |
| Approach: | They propose a training-free model routing method that optimizes synergy among multiple LLMs for open-domain text generation tasks. |
| Outcome: | The proposed method outperforms 13 baseline models and reduces costs by 17.20%. |
HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims (2025.acl-long)
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| Challenge: | Identifying checkworthy claims is the first step, but detection methods struggle with content that is (1) multimodal, (2) from diverse domains, and (3) synthetic. |
| Approach: | They propose a dataset for multimodal checkworthiness detection with 27K real-world and synthetic image/claim pairs. |
| Outcome: | The proposed dataset compares lightweight text-based encoders to multimodal models but only focus on claim-like content. |