Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddings (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies use LoRA to fine-tune existing LLMs, but this is limited by the data and training gap between them and embedding models. |
| Approach: | They propose a new 1.4B-parameter LLM trained from scratch and fine-tuned as a text embedder that integrates embeddings across different languages. |
| Outcome: | The proposed model improves performance on the Massive Text Embedding Benchmark (MTEB) and Chinese MTEB (May 19, 2025). |
Similar Papers
Improving Text Embeddings with Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for obtaining text embeddings require complex training pipelines . authors leverage proprietary LLMs to generate diverse synthetic data for text embeds based on 93 languages . |
| Approach: | They propose a method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. |
| Outcome: | The proposed method achieves strong performance on competitive text embedding benchmarks without using any labeled data. |
Training LLMs to be Better Text Embedders through Bidirectional Reconstruction (2025.emnlp-main)
Copied to clipboard
Chang Su, Dengliang Shi, Siyuan Huang, Jintao Du, Changhua Meng, Yu Cheng, Weiqiang Wang, Zhouhan Lin
| Challenge: | Existing text embedding approaches often leverage the embeddment of the final token, typically a reserved special token such as ‘[EOS]‘. |
| Approach: | They propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding. |
| Outcome: | The proposed training stage improves performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-of-the-art results across different LLM base models and scales. |
Bridging the Language Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs (2025.coling-main)
Copied to clipboard
Somnath Kumar, Vaibhav Balloli, Mercy Ranjit, Kabir Ahuja, Sunayana Sitaram, Kalika Bali, Tanuja Ganu, Akshay Nambi
| Challenge: | Large language models (LLMs) excel in diverse applications but still struggle with non-Latin scripts and low-resource languages. |
| Approach: | They propose a dynamic learning approach that optimizes prompt strategy, embedding model, and LLM per query at runtime. |
| Outcome: | The proposed approach achieves 10-15% improvements in multilingual performance over pre-trained models and 4x gains compared to fine-tuned, language-specific models. |
Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have been widely explored for embedding generation. |
| Approach: | They propose an embedding-based in-context prompt training strategy that leverages in-constext learning to generate high-quality embeddables while reducing computational burden. |
| Outcome: | The proposed method surpasses models trained on publicly available retrieval data and achieves state-of-the-art embedding performance on the MTEB benchmark. |
Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval (2024.acl-long)
Copied to clipboard
| Challenge: | Dense retrieval requires discriminative embeddings to represent the semantic relationship between query and document. |
| Approach: | They propose an unsupervised approach that performs unsupervised adaptation of large language models for dense retrieval. |
| Outcome: | The proposed model improves on a variety of dense retrieval benchmarks and is available on github. |
Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation (2025.coling-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are mainly trained on English data and struggle with low-resource languages. |
| Approach: | They propose to add a new language to Llama to improve classification accuracy for Persian tasks by aligning representations through bilingual pretraining and instruction datasets. |
| Outcome: | The proposed model performs on generation and classification tasks with no adverse impact and sometimes even improvements on English tasks. |
A Text is Worth Several Tokens: Text Embedding from LLMs Secretly Aligns Well with The Key Tokens (2025.acl-long)
Copied to clipboard
| Challenge: | et al., 2023) show that text embeddings from large language models can be aligned with key tokens in input text. |
| Approach: | They propose a sparse retrieval method based on aligned tokens for large language models . they show that this phenomenon is universal and is not affected by model architecture . |
| Outcome: | The proposed method can achieve 80% of the dense retrieval effect of the same model while reducing the computation significantly. |
Tokenizer-Aware Cross-Lingual Adaptation of Decoder-Only LLMs through Embedding Relearning and Swapping (2026.eacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been primarily focused on English, leaving the multilingual ability unexplored. |
| Approach: | They propose a technique that creates new tokenizers and tunes embeddings on fixed model weights for target language adaptation. |
| Outcome: | The proposed method is light-weight and performant but has limitations for older models and high resource languages. |
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement (2024.findings-acl)
Copied to clipboard
Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam, Sheng Shen, Gopala Anumanchipalli, Michael Mahoney, Kurt Keutzer, Amir Gholami
| Challenge: | Pretrained large language models are currently state-of-the-art for solving most tasks . however, many of them are in the low-data regime, making fine-tuning challenging . a new data augmentation strategy uses a teacher LLM to augment a small seed dataset . |
| Approach: | They propose a targeted and iterative data augmentation strategy that augments a teacher LLM to fine-tune a small seed dataset by adding additional data. |
| Outcome: | The proposed approach outperforms fine-tuning and other data augmentation strategies on a small seed dataset. |
Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets (2024.findings-emnlp)
Copied to clipboard
Israel Azime, Atnafu Tonja, Tadesse Belay, Mitiku Yohannes Fuge, Aman Wassie, Eyasu Jada, Yonas Chanie, Walelign Sewunetie, Seid Yimam
| Challenge: | Low-resource languages are left behind due to the unavailability of resources. |
| Approach: | They propose to integrate task-specific and generative datasets to improve language model performance for Amharic by fine-tuning an Amharican instruction fine-to-tuned model. |
| Outcome: | The proposed model shows promising results in different NLP tasks and compares translated instruction datasets with the original model. |