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Marius Mosbach

Postdoctorate - McGill University
Supervisor

Publications

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Parishad BehnamGhader
Vaibhav Adlakha
Marius Mosbach
Large decoder-only language models (LLMs) are the state-of-the-art models on most of today's NLP tasks and benchmarks. Yet, the community is… (see more) only slowly adopting these models for text embedding tasks, which require rich contextualized representations. In this work, we introduce LLM2Vec, a simple unsupervised approach that can transform any decoder-only LLM into a strong text encoder. LLM2Vec consists of three simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. We demonstrate the effectiveness of LLM2Vec by applying it to 3 popular LLMs ranging from 1.3B to 7B parameters and evaluate the transformed models on English word- and sequence-level tasks. We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB). Moreover, when combining LLM2Vec with supervised contrastive learning, we achieve state-of-the-art performance on MTEB among models that train only on publicly available data. Our strong empirical results and extensive analysis demonstrate that LLMs can be effectively transformed into universal text encoders in a parameter-efficient manner without the need for expensive adaptation or synthetic GPT-4 generated data.
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Parishad BehnamGhader
Vaibhav Adlakha
Marius Mosbach
Multilingual Language Model Adaptive Fine-Tuning: A Study on African Languages
Jesujoba Oluwadara Alabi
Marius Mosbach
Dietrich Klakow
and XLM-R) and three NLP tasks (NER, news topic classification, and sentiment classification) shows that our approach is competitive to ap… (see more)plying LAFT on individual languages while requiring significantly less disk space. Finally, we show that our adapted PLM also improves the zero-shot cross-lingual transfer abilities of parameter efficient fine-tuning methods.