Papers with nearest-neighbor search
Improving Tool Retrieval by Leveraging Large Language Models for Query Generation (2025.coling-industry)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown great promise in common sense language understanding, conversational fluency, and reasoning. |
| Approach: | They propose to use Large Language Models to generate a retrieval query and embed it into the prompt to find relevant tools via a nearest-neighbor search. |
| Outcome: | The proposed method improves retrieval for in-domain (seen tools) and out-of-domain settings. |
Retrieval-based Language Models and Applications (2023.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will provide a comprehensive overview of retrieval-based language models. |
| Approach: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |
| Outcome: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |
Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings (2020.tacl-1)
Copied to clipboard
| Challenge: | Existing methods to extract parallel sentences from unaligned text yield surprisingly good results. |
| Approach: | They propose an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text using multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. |
| Outcome: | The proposed method outperforms existing methods and outperformed previous state-of-the-art methods by boosting translation performance by up to 3.5 BLEU on the WMT’14 French-English and WMT'16 German-English tasks. |
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing RE models are incapable of handling implicit expressions and long-tail relation types due to language complexity and data sparsity. |
| Approach: | They propose a method to enhance relation extraction using k nearest neighbors (kNN-RE) kNN is a nearest-neighbor search tool that allows the model to consult training relations at test time . |
| Outcome: | The proposed model outperforms the best model to date on ACE05, SciERC, and Wiki80 datasets and outperformed the best on i2b2 and Wik80 dataset. |
NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings (2025.findings-emnlp)
Copied to clipboard
| Challenge: | NER Retriever uses a user-defined type description to retrieve documents mentioning entities of that type. |
| Approach: | They propose a zero-shot retrieval framework for ad-hoc Named Entity Recognition . a user-defined type description is used to retrieve documents mentioning entities of that type . |
| Outcome: | The proposed framework outperforms lexical and dense retrieval baselines on three benchmarks. |
Chinese Automatic Readability Assessment Using Adaptive Pre-training and Linguistic Feature Fusion (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for classification of reading difficulty of texts are insufficiently trained and lack of linguistic features. |
| Approach: | They propose a method that combines adaptive pre-training with feature fusion to capture different text difficulties and an interactive attention mechanism to integrate linguistic and deep features. |
| Outcome: | The proposed method achieves state-of-the-art (SOTA) performance on Chinese textbook dataset and can be applied to other languages. |