Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models
- URL: http://arxiv.org/abs/2307.08303v5
- Date: Mon, 17 Jun 2024 04:30:58 GMT
- Title: Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models
- Authors: Zhiyuan Peng, Xuyang Wu, Qifan Wang, Yi Fang,
- Abstract summary: We propose soft prompt tuning for augmenting Dense retrieval (DR) models.
For each task, we leverage soft prompt-tuning to optimize a task-specific soft prompt on limited ground truth data.
We design a filter to select high-quality example document-query pairs in the prompt to further improve the quality of weak tagged queries.
- Score: 29.735976068474105
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Dense retrieval (DR) converts queries and documents into dense embeddings and measures the similarity between queries and documents in vector space. One of the challenges in DR is the lack of domain-specific training data. While DR models can learn from large-scale public datasets like MS MARCO through transfer learning, evidence shows that not all DR models and domains can benefit from transfer learning equally. Recently, some researchers have resorted to large language models (LLMs) to improve the zero-shot and few-shot DR models. However, the hard prompts or human-written prompts utilized in these works cannot guarantee the good quality of generated weak queries. To tackle this, we propose soft prompt tuning for augmenting DR (SPTAR): For each task, we leverage soft prompt-tuning to optimize a task-specific soft prompt on limited ground truth data and then prompt the LLMs to tag unlabeled documents with weak queries, yielding enough weak document-query pairs to train task-specific dense retrievers. We design a filter to select high-quality example document-query pairs in the prompt to further improve the quality of weak tagged queries. To the best of our knowledge, there is no prior work utilizing soft prompt tuning to augment DR models. The experiments demonstrate that SPTAR outperforms the unsupervised baselines BM25 and the recently proposed LLMs-based augmentation method for DR.
Related papers
- SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts.
We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM.
We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
arXiv Detail & Related papers (2024-07-16T04:41:58Z) - DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering [4.364937306005719]
RAG has recently demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks such as Question-Answering (QA)
We have found that even though there is low relevance between some critical documents and query, it is possible to retrieve the remaining documents by combining parts of the documents with the query.
A two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers.
arXiv Detail & Related papers (2024-06-11T15:15:33Z) - On the Worst Prompt Performance of Large Language Models [93.13542053835542]
Performance of large language models (LLMs) is acutely sensitive to the phrasing of prompts.
We introduce RobustAlpacaEval, a new benchmark that consists of semantically equivalent case-level queries.
Experiments on RobustAlpacaEval with ChatGPT and six open-source LLMs from the Llama, Mistral, and Gemma families uncover substantial variability in model performance.
arXiv Detail & Related papers (2024-06-08T13:40:38Z) - Multi-Head RAG: Solving Multi-Aspect Problems with LLMs [13.638439488923671]
Retrieval Augmented Generation (RAG) enhances the abilities of Large Language Models (LLMs)
Existing RAG solutions do not focus on queries that may require fetching multiple documents with substantially different contents.
This paper introduces Multi-Head RAG (MRAG), a novel scheme designed to address this gap with a simple yet powerful idea.
arXiv Detail & Related papers (2024-06-07T16:59:38Z) - R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models [32.598670876662375]
Retrieval-augmented large language models (LLMs) leverage relevant content retrieved by information retrieval systems to generate correct responses.
Existing retriever-responder methods typically append relevant documents to the prompt of LLMs to perform text generation tasks.
We propose a new pipeline named "Reinforced Retriever-Reorder-Responder" to learn document orderings for retrieval-augmented LLMs.
arXiv Detail & Related papers (2024-05-04T12:59:10Z) - Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models [28.105271954633682]
We introduce a query-dependent parameter efficient fine-tuning (Q-PEFT) approach for text reranking to leak information to Large Language Models (LLMs)
We utilize the query to extract the top-$k$ tokens from input documents, serving as contextual clues.
We further augment Q-PEFT by substituting the retrieval mechanism with a multi-head attention layer to achieve end-to-end training and cover all the tokens in the documents.
arXiv Detail & Related papers (2024-04-06T06:44:41Z) - Benchmarking LLMs on the Semantic Overlap Summarization Task [9.656095701778975]
This paper comprehensively evaluates Large Language Models (LLMs) on the Semantic Overlap Summarization (SOS) task.
We report well-established metrics like ROUGE, BERTscore, and SEM-F1$ on two different datasets of alternative narratives.
arXiv Detail & Related papers (2024-02-26T20:33:50Z) - Large Language Models are Strong Zero-Shot Retriever [89.16756291653371]
We propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios.
Our method, the Language language model as Retriever (LameR), is built upon no other neural models but an LLM.
arXiv Detail & Related papers (2023-04-27T14:45:55Z) - Noise-Robust Dense Retrieval via Contrastive Alignment Post Training [89.29256833403167]
Contrastive Alignment POst Training (CAPOT) is a highly efficient finetuning method that improves model robustness without requiring index regeneration.
CAPOT enables robust retrieval by freezing the document encoder while the query encoder learns to align noisy queries with their unaltered root.
We evaluate CAPOT noisy variants of MSMARCO, Natural Questions, and Trivia QA passage retrieval, finding CAPOT has a similar impact as data augmentation with none of its overhead.
arXiv Detail & Related papers (2023-04-06T22:16:53Z) - AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators [98.11286353828525]
GPT-3.5 series models have demonstrated remarkable few-shot and zero-shot ability across various NLP tasks.
We propose AnnoLLM, which adopts a two-step approach, explain-then-annotate.
We build the first conversation-based information retrieval dataset employing AnnoLLM.
arXiv Detail & Related papers (2023-03-29T17:03:21Z) - Query2doc: Query Expansion with Large Language Models [69.9707552694766]
The proposed method first generates pseudo- documents by few-shot prompting large language models (LLMs)
query2doc boosts the performance of BM25 by 3% to 15% on ad-hoc IR datasets.
Our method also benefits state-of-the-art dense retrievers in terms of both in-domain and out-of-domain results.
arXiv Detail & Related papers (2023-03-14T07:27:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.