7 papers
Chain-of-Retrieval Augmented Generation
Liang Wang, Haonan Chen, Nan Yang +3
This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer. Conventional R…
Examining False Positives under Inference Scaling for Mathematical Reasoning
Yu Wang, Nan Yang, Liang Wang +2
Recent advancements in language models have led to significant improvements in mathematical reasoning across various benchmarks. However, most of these benchmarks rely on automatic…
MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings
Haonan Chen, Hong Liu, Yuping Luo +4
Multimodal embedding models, built upon causal Vision Language Models (VLMs), have shown promise in various tasks. However, current approaches face three key limitations: the use o…
Generative Representational Instruction Tuning
Niklas Muennighoff, Hongjin Su, Liang Wang +5
All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational in…
mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data
Haonan Chen, Liang Wang, Nan Yang +4
Multimodal embedding models have gained significant attention for their ability to map data from different modalities, such as text and images, into a unified representation space.…
LongEmbed: Extending Embedding Models for Long Context Retrieval
Dawei Zhu, Liang Wang, Nan Yang +4
Embedding models play a pivot role in modern NLP applications such as IR and RAG. While the context limit of LLMs has been pushed beyond 1 million tokens, embedding models are stil…