Improving Text Embeddings with Large Language Models: Model Fine-tuning and Evaluation

Authors: (1) Liang Wang, Microsoft Corporation, and Correspondence to (wangliang@microsoft.com); (2) Nan Yang, Microsoft Corporation, and correspondence to (nanya@microsoft.com); (3) Xiaolong Huang, Microsoft Corporation; (4) Linjun Yang, Microsoft Corporation; (5) Rangan Majumder, Microsoft Corporation; (6) Furu Wei, Microsoft Corporation and Correspondence to (fuwei@microsoft.com). Table of Links Abstract and 1 Introduction 2 Related Work 3 Method 3.1 Synthetic Data Generation 3.2 Training 4 Experiments 4.1 Statistics of the Synthetic Data 4.2 Model Fine-tuning and Evaluation 4.3 Main Results 4.4 Multilingual Retrieval 5 Analysis 5.1 Is Contrastive Pre-training Necessary? 5.2 Extending to Long Text Embeddings and 5.3 Analysis of Training Hyperparameters 6 Conclusion and References A Implementation Details B Test Set Contamination Analysis C Prompts for Synthetic Data Generation D Instructions for Training and Evaluation 4.2 Model Fine-tuning and Evaluation The pretrained Mistral-7b [19] checkpoint is fine-tuned for 1 epoch using the loss in Equation 2. We follow the training recipe from RankLLaMA [24] and utilize LoRA [17] with rank 16. To further reduce GPU memory requirement, techniques including gradient checkpointing, mixed precision training, and DeepSpeed ZeRO-3 are applied. For the training data, we utilize both the generated synthetic data and a collection of 13 public datasets, yielding approximately 1.8M examples after sampling. More details are available in Appendix A. To provide a fair comparison with some previous work, we also report results when the only labeled supervision is the MS-MARCO passage ranking [5] dataset. We evaluate the trained model on the MTEB benchmark [28]. Note that the retrieval category in MTEB corresponds to the 15 publicly available datasets in the BEIR benchmark [42]. Evaluation of one model takes about 3 days on 8 V100 GPUs due to the need to encode a large number of documents. Although our model can accommodate sequence length beyond 512, we only evaluate on the first 512 tokens for efficiency. Official metrics are reported for each category. For more details about the evaluation protocol, please refer to the original papers [28, 42]. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Liang Wang, Microsoft Corporation, and Correspondence to (wangliang@microsoft.com); (2) Nan Yang, Microsoft Corporation, and correspondence to (nanya@microsoft.com); (3) Xiaolong Huang, Microsoft Corporation; (4) Linjun Yang, Microsoft Corporation; (5) Rangan Majumder, Microsoft Corporation; (6) Furu Wei, Microsoft Corporation and Correspondence to (fuwei@microsoft.com). Authors: Authors: (1) Liang Wang, Microsoft Corporation, and Correspondence to (wangliang@microsoft.com); (2) Nan Yang, Microsoft Corporation, and correspondence to (nanya@microsoft.com); (3) Xiaolong Huang, Microsoft Corporation; (4) Linjun Yang, Microsoft Corporation; (5) Rangan Majumder, Microsoft Corporation; (6) Furu Wei, Microsoft Corporation and Correspondence to (fuwei@microsoft.com). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Method 3.1 Synthetic Data Generation 3.1 Synthetic Data Generation 3.2 Training 3.2 Training 4 Experiments 4.1 Statistics of the Synthetic Data 4.1 Statistics of the Synthetic Data 4.2 Model Fine-tuning and Evaluation 4.2 Model Fine-tuning and Evaluation 4.3 Main Results 4.3 Main Results 4.4 Multilingual Retrieval 4.4 Multilingual Retrieval 5 Analysis 5.1 Is Contrastive Pre-training Necessary? 5.1 Is Contrastive Pre-training Necessary? 5.2 Extending to Long Text Embeddings and 5.3 Analysis of Training Hyperparameters 5.2 Extending to Long Text Embeddings and 5.3 Analysis of Training Hyperparameters 6 Conclusion and References 6 Conclusion and References A Implementation Details A Implementation Details B Test Set Contamination Analysis B Test Set Contamination Analysis C Prompts for Synthetic Data Generation C Prompts for Synthetic Data Generation D Instructions for Training and Evaluation D Instructions for Training and Evaluation 4.2 Model Fine-tuning and Evaluation The pretrained Mistral-7b [19] checkpoint is fine-tuned for 1 epoch using the loss in Equation 2. We follow the training recipe from RankLLaMA [24] and utilize LoRA [17] with rank 16. To further reduce GPU memory requirement, techniques including gradient checkpointing, mixed precision training, and DeepSpeed ZeRO-3 are applied. For the training data, we utilize both the generated synthetic data and a collection of 13 public datasets, yielding approximately 1.8M examples after sampling. More details are available in Appendix A. To provide a fair comparison with some previous work, we also report results when the only labeled supervision is the MS-MARCO passage ranking [5] dataset. We evaluate the trained model on the MTEB benchmark [28]. Note that the retrieval category in MTEB corresponds to the 15 publicly available datasets in the BEIR benchmark [42]. Evaluation of one model takes about 3 days on 8 V100 GPUs due to the need to encode a large number of documents. Although our model can accommodate sequence length beyond 512, we only evaluate on the first 512 tokens for efficiency. Official metrics are reported for each category. For more details about the evaluation protocol, please refer to the original papers [28, 42]. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv