Map of Encoders – Mapping Sentence Encoders using Quantum Relative Entropy (2026.acl-long)
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| Challenge: | a method to compare and visualise sentence encoders at scale is proposed . we map encoder LLMs using QRE-based feature vectors, which are then projected to 2D . |
| Approach: | They propose a method to compare and visualise sentence encoders at scale by creating a map of encoder . they construct a QRE-based map of sentences covering 1101 publicly available sentence encoded sentences . |
| Outcome: | The proposed method compares sentence encoders at scale by creating a map of encoder models . it shows that the map accurately reflects relationships between encoder and unit base encoder . |
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| Challenge: | Sentence embeddings are useful for language processing tasks, but it is unclear how to produce them from encoder-decoder models. |
| Approach: | They investigate the effects of scaling up sentence encoders to 11B parameters on sentence embeddings from text-to-text transformers (T5) . |
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What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties (P18-1)
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| Challenge: | a lack of understanding of the properties of sentence embeddings is limiting the use of the techniques. |
| Approach: | They propose 10 probing tasks designed to capture simple linguistic features of sentences . they use three different encoders to train embeddings in eight different ways . |
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Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations (2024.naacl-long)
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Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu
| Challenge: | Sentence embeddings are typically learned to recognize the semantic relation between two text inputs. |
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Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity (2020.acl-main)
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| Challenge: | Existing word embeddings combine complementary strengths of their components to achieve unsupervised semantic similarity (STS). |
| Approach: | They propose to ensemble pre-trained sentence encoders into sentence meta-embeddings to achieve unsupervised Semantic Textual Similarity (STS) they adapt dimensionality reduction, generalized Canonical Correlation Analysis and cross-view auto-encoders to their work. |
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Learning Visually Grounded Sentence Representations (N18-1)
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| Challenge: | Unsupervised sentence representation models suffer from the grounding problem because of lack of association between symbols and external information. |
| Approach: | They train a sentence encoder to predict image features of a caption and use them as sentence representations. |
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Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning (P19-1)
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| Challenge: | Encoder-decoder models for unsupervised sentence representation learning discard decoder after training . decoded sentences are often used to make better predictions of words in a given sentence . |
| Approach: | They propose two types of decoding functions whose inverse can be easily derived without expensive inverse calculation. |
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On Measuring Social Biases in Sentence Encoders (N19-1)
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| Challenge: | Word embeddings such as word2vec and GloVe exhibit human-like implicit biases based on gender, race, and other social constructs. |
| Approach: | They propose a simple generaliza test to measure bias in word embeddings by comparing two sets of target-concept words to two sets . |
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General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference (2020.findings-emnlp)
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| Challenge: | Large pre-trained language models are currently used for many NLP tasks . however, inference for these models requires significant computational resources . |
| Approach: | They propose to use a shared text encoder to amortize the computational cost of inference over multiple tasks. |
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A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders (2021.emnlp-main)
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| Challenge: | Existing studies have used probing tasks to verify the presence of linguistic properties in vector representations, but it is unclear whether they can be manipulated to indirectly steer them. |
| Approach: | They validate a geometric mapping technique to transform linguistic properties without tuning . they use a pre-trained multilingual autoencoder to transform three linguistic property . |
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CASE – Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement (2026.eacl-long)
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| Challenge: | Recent approaches use semantic similarity to improve the quality of sentence embeddings, but it is difficult to measure the similarity between sentences. |
| Approach: | They propose a condition-aware sentence embedding method that uses an LLM encoder to create an embeddable sentence under a given condition. |
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