Papers with ANN
Fast Exact Retrieval for Nearest-neighbor Lookup (FERN) (2024.naacl-srw)
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| Challenge: | Exact nearest neighbor search is computationally intensive and complex . Attention has shifted towards Approximate Nearest-Neighbor (ANN) retrieval techniques . |
| Approach: | They propose an algorithm for logarithmic Fast Exact Retrieval for Nearest-neighbor lookup which achieves O(dlog N) look-up with 100% recall on 10 million d=128 uniformly generated vectors. |
| Outcome: | The proposed algorithm achieves O(dlog N) look-up with 100% recall on 10 million d=128 uniformly generated vectors. |
User Interest Modelling in Argumentative Dialogue Systems (2022.lrec-1)
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| Challenge: | Existing studies on user interest in dialogue systems depend on explicit user feedback. |
| Approach: | They propose a model to implicitly estimate user interest during argumentative dialogues based on semantically clustered data. |
| Outcome: | The proposed model achieves a classification accuracy of 74.9% and tested with different Artificial Neural Networks (ANN) which new argument would fit the user interest best. |
pEBR: A Probabilistic Approach to Embedding Based Retrieval (2025.emnlp-industry)
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| Challenge: | Existing embedding-based retrieval systems rely on heuristic and suboptimal cutoffs for item retrieval. |
| Approach: | They propose a probabilistic Embedding-Based Retrieval framework that learns a shared semantic representation space for both queries and items. |
| Outcome: | The proposed framework improves retrieval precision and recall, and ablation studies show it captures the differences between head-to-tail queries. |
Alleviating Performance Degradation Caused by Out-of-Distribution Issues in Embedding-Based Retrieval (2025.findings-emnlp)
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Haotong Bao, Jianjin Zhang, Qi Chen, Weihao Han, Zhengxin Zeng, Ruiheng Chang, Mingzheng Li, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Recent studies reveal query out-of-distribution issues degrading ANN performance . a distribution regularizer is introduced into the encoder training objective to encourage alignment between query and base embeddings. |
| Approach: | They introduce a distribution regularizer into the encoder training objective to encourage alignment between query and base embeddings. |
| Outcome: | The proposed method consistently improves retrieval performance across multiple datasets. |
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts (D18-1)
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| Challenge: | Existing sentences classification models often classify sentences in isolation without considering the context in which sentences appear. |
| Approach: | They propose a hierarchical sequential labeling network to make use of contextual information within surrounding sentences to help classify the current sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art methods by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts. |
SpikeVoice: High-Quality Text-to-Speech Via Efficient Spiking Neural Network (2024.acl-long)
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| Challenge: | SpikeVoice performs high-quality Text-To-Speech (TTS) via SNN . major obstacle to using SNN for such generative tasks lies in the demand for models to grasp long-term dependencies. |
| Approach: | They propose a brain-inspired Spiking Neural Network (SNN) which performs high-quality Text-To-Speech (TTS) via SNN and explores the potential of SNN to "speak". |
| Outcome: | The proposed model achieves comparable results to Artificial Neural Networks (ANN) with only 10.5% energy consumption of ANN. |
Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition (D19-1)
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| Challenge: | Named entity recognition (NER) is a fundamental NLP task. |
| Approach: | They propose a gazetteer-based attentive neural network which can enhance region-based NER . they first model the mention-context association and then an auxiliary gazetteers . |
| Outcome: | The proposed approach can achieve state-of-the-art on ACE2005 named entity recognition benchmark. |
LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval (2026.findings-acl)
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| Challenge: | Large language models (LLMs) embeddings are typically high-dimensional, leading to substantial storage and retrieval overhead. |
| Approach: | They propose a learning-free method that transforms an LLM embedding into a binary embeddable using Isolation Kernel (IKE). |
| Outcome: | The proposed method performs 16.7 faster retrieval and 16 lower memory usage than the original LLM embeddings while maintaining comparable accuracy. |
Transfer Learning for Named-Entity Recognition with Neural Networks (L18-1)
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| Challenge: | Existing approaches to named-entity recognition (NER) require additional lead time for developing and fine-tuning the rules. |
| Approach: | They propose to transfer an ANN model trained on a large labeled dataset to another dataset with a limited number of labels to improve upon the state-of-the-art results for patient note de-identification. |
| Outcome: | The proposed model can be transferred to a dataset with a limited number of labels, and improves on the state-of-the-art results on patient note de-identification. |
Drift-Adapter: A Practical Approach to Near Zero-Downtime Embedding Model Upgrades in Vector Databases (2025.emnlp-main)
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| Challenge: | Upgrading embedding models in production environments requires re-encoding the entire corpus and rebuilding the Approximate Nearest Neighbor (ANN) index. |
| Approach: | They propose a lightweight, learnable transformation layer designed to bridge embedding spaces between models by mapping new queries into the legacy embeddable space. |
| Outcome: | The proposed transformation layer recovers 95–99% of the retrieval recall of a full re-embedding, adding less than 10,s query latency. |
TopoRAG: Graph-based RAG via Topology-aware Approximate Nearest Neighbor Search (2026.findings-acl)
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| Challenge: | Recent studies extend RAG with graph-structured knowledge, enhancing retrieval to capture relational context beyond isolated text chunks. |
| Approach: | They propose a retrieval framework that integrates structural constraints into ANN search . they propose heuristic neighbor expansion which augments the retrieved set by traversing immediate neighbors . |
| Outcome: | The proposed framework improves precision and reduces context redundancy compared to existing methods. |