Papers with embedding alignment
English-Malay Cross-Lingual Embedding Alignment using Bilingual Lexicon Augmentation (2022.acl-srw)
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| Challenge: | Embedings that are pre-trained monolingually are limited to tasks only in its own language. |
| Approach: | They propose to create English-Malay cross-lingual word embeddings using embedd alignment by exploiting existing language resources. |
| Outcome: | The proposed approach improves the quality of the existing English-Malay bilingual lexicon and the effect of Malay word coverage on the quality. |
Domain-Specific Word Embeddings with Structure Prediction (2023.tacl-1)
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| Challenge: | Current word embedding methods do not provide a way to use or predict information on structure between sub-corpora, time or domain. |
| Approach: | They propose a word embedding method that provides general word representations for the whole corpus, domain-specific representations and embeddable alignment simultaneously. |
| Outcome: | The proposed method provides better performance than baselines on a dataset of science and philosophy articles. |
Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages (2025.acl-srw)
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| Challenge: | Low-resource languages (LRLs) face significant challenges in natural language processing due to limited data. |
| Approach: | They evaluate adapter-based methods for adapting mLMs to low-resource languages . they use unstructured text and structured knowledge from ConceptNet to evaluate adapters . |
| Outcome: | The proposed methods outperform large language models and LLaMA-3 and deepSeek-R1 models on low training data. |
Multi-Granularity Contrasting for Cross-Lingual Pre-Training (2021.findings-acl)
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| Challenge: | Existing approaches to pre-training focus on embedding alignment, but they neglect the modeling of bidirectional contexts. |
| Approach: | They propose a framework to learn languageuniversal representations using multi-granularity contrasting framework . they encode semantic equivalents from different languages into similar representations . |
| Outcome: | The proposed framework can achieve significant performance gains in machine translation and cross-lingual language understanding. |
OLIVE: Object Level In-Context Visual Embeddings (2024.acl-long)
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| Challenge: | Existing vision-language models lack fine-grained object-level understanding and grounding . existing models implicitly align text tokens with image patch tokens, which is ineffective for embedding alignment at the same granularity and introduces noisy spurious background features. |
| Approach: | They propose a method to prompt large language models with in-context visual object vectors . this method allows for controllable object-level reasoning . |
| Outcome: | The proposed method achieves competitive referring object classification and captioning performance while offering zero-shot generalization and robustness to visually challenging contexts. |
Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment (2025.emnlp-main)
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Jingcheng Deng, Zhongtao Jiang, Liang Pang, Zihao Wei, Liwei Chen, Kun Xu, Yang Song, Huawei Shen, Xueqi Cheng
| Challenge: | Experimental results demonstrate that our method significantly outperforms traditional contrastive learning approaches when using the same amount of data. |
| Approach: | They propose a new contrastive learning method built on embedding conditional probability distributions that integrates two tasks: information compression and conditional distribution alignment. |
| Outcome: | The proposed method outperforms traditional contrastive learning approaches and achieves comparable performance to state-of-the-art models when using the same amount of data. |
ALGEN: Few-shot Inversion Attacks on Textual Embeddings via Cross-Model Alignment and Generation (2025.acl-long)
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| Challenge: | Recent studies have proven that private textual data is vulnerable to inversion attacks . authors present a method to reduce the cost of inversion and improve performance . |
| Approach: | They propose a method that aligns victim embeddings to attack space and reconstructs text . they find that none of the defense mechanisms are effective against inversion attacks . |
| Outcome: | The proposed method lowers the cost of inversion and improves performance across languages and domains. |