Efficient k-NN Search with Cross-Encoders using Adaptive Multi-Round CUR Decomposition (2023.findings-emnlp)
Copied to clipboard
| Challenge: | ANNCUR uses a cross-encoder only to perform k-NN search, but the approximation of the distances is often detrimental to the retrieval of top-k items. |
| Approach: | They propose a method that minimizes approximation error for k-nearest neighbor searches . they propose to use a cross-encoder only to perform k NN search . |
| Outcome: | The proposed method reduces approximation error for top-k neighbors by up to 70% . iteratively performs k-NN search using the available anchors, then adds them to the next set . |
Similar Papers
Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization (2022.emnlp-main)
Copied to clipboard
| Challenge: | Efficient k-nearest neighbor search is a fundamental task, foundational for many problems in NLP. |
| Approach: | They propose an approach that avoids the use of a dual-encoder for retrieval, relying solely on the cross-encoding model. |
| Outcome: | Empirically, for k > 10, our approach provides test-time recall-vs-computational cost trade-offs superior to the current widely-used methods that re-rank items retrieved using a dual-encoder or TF-IDF. |
Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrieval (2024.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that using only bi-encoders as an intermediate reranker can improve top-1 accuracy with negligible slowdown (7%). |
| Approach: | They propose a framework that compares a query and multiple embeddings of similar candidates through shallow self-attention layers, delivering rich representations contextualized to each other. |
| Outcome: | The proposed framework compares a query and multiple embeddings of similar candidates through shallow self-attention layers, delivering rich representations contextualized to each other. |
Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to decode a given context-candidate pair are expensive and time-consuming. |
| Approach: | They propose a new paradigm that keeps full attention over each pair while only encoding the context once. |
| Outcome: | The proposed paradigm achieves new state-of-the-art results on four benchmark datasets with high computational efficiency. |
Efficient Beam Search for Large Language Models Using Trie-Based Decoding (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) face memorybound performance bottlenecks due to their high memory requirements. |
| Approach: | They propose a trie-based parallel decoding method that shares a single KV cache across beams with common prefixes to dramatically reduce memory usage and enables efficient decoding. |
| Outcome: | The proposed method significantly reduces memory usage and enables efficient decoding without compromising generation quality. |
Accelerating Learned Sparse Indexes Via Term Impact Decomposition (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Novel inverted index-based learned sparse ranking models provide more effective, but less efficient, retrieval performance compared to traditional ranking models. |
| Approach: | They propose a technique that allows for automatic pruning of ranking models by storing metadata about index term importance scores. |
| Outcome: | The proposed technique accelerates top-k retrieval by 9.6X without loss in effectiveness. |
Multi-Vector Attention Models for Deep Re-ranking (2021.emnlp-main)
Copied to clipboard
| Challenge: | Document retrieval systems often use two styles of neural network models . dual encoder models are used for retrieval and deep re-ranking, while cross-attention models are typically used for shallow reranking. |
| Approach: | They propose a dual encoder and cross-attention neural network architectures that combine query and document representations to optimize retrieval accuracy. |
| Outcome: | The proposed architecture trades off retrieval accuracy with joint computation and offline document storage cost. |
NAIL: Lexical Retrieval Indices with Efficient Non-Autoregressive Decoders (2023.emnlp-main)
Copied to clipboard
| Challenge: | Neural document rerankers require dedicated hardware for serving, which is costly and often not feasible. |
| Approach: | They propose a method that captures 86% of the gains of a Transformer cross-attention model with a lexicalized scoring function that only requires 10-6% of . the model architecture is compatible with recent encoder-decoder and decoder-only large language models, such as T5, GPT-3 and PaLM. |
| Outcome: | The proposed model captures 86% of the gains of a Transformer cross-attention model with a lexicalized scoring function. |
Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing bi-encoder architectures do not allow any sharing between text and knowledge graphs . john sutter: experimental results show that enabling full interaction yields strong improvements. |
| Approach: | They propose cross-stitch bi-encoders that allow full interaction between text and KG . they say the amount of sharing is dynamically controlled via cross-attention-based gates . |
| Outcome: | Experimental results show that bi-encoder architectures yield strong improvements . cross-stitch mechanism allows sharing and updating representations between two encoders . |
Adaptive Nearest Neighbor Machine Translation (2021.acl-short)
Copied to clipboard
| Challenge: | kNN-MT uses pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy. |
| Approach: | They propose a method that combines a pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy. |
| Outcome: | The proposed method outperforms the existing model on four benchmark datasets and is open-source. |
Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval (2021.acl-long)
Copied to clipboard
| Challenge: | Existing retrieval models based on dense representations show better performance than sparse representations. |
| Approach: | They propose a method to mimic the queries to each of the documents by an iterative clustering process and represent the documents using multiple pseudo queries. |
| Outcome: | The proposed model achieves state-of-the-art results on a large dataset while remaining high efficiency. |