Leveraging Large Language Models to Efficiently Generate Multilingual Training Data for Dense Retrieval

Khái niệm cốt lõi

Synthetic training data generation using large language models can effectively substitute for expensive human-labeled data in improving multilingual dense retrieval models.

Tóm tắt

The paper presents a novel approach called SWIM-IR for generating a large-scale synthetic multilingual dataset for training dense retrieval models. The key highlights are:

The authors propose Summarize-then-Ask Prompting (SAP), which uses a two-stage process with an LLM to generate informative queries in the target language by first extracting a relevant summary from the input passage.

Using SAP, the authors construct SWIM-IR, a dataset of 28 million synthetic query-passage pairs across 33 languages, including both high and low-resource languages. This dataset is one of the largest multilingual synthetic training datasets for dense retrieval.

The authors develop SWIM-X, a series of multilingual dense retrieval models fine-tuned on the SWIM-IR dataset without any human supervision. SWIM-X models are shown to be competitive with or outperform human-supervised baselines on three standard multilingual retrieval benchmarks: XOR-Retrieve, MIRACL, and XTREME-UP.

Extensive experiments and analyses are conducted to understand the effectiveness of SAP, the optimal amount of synthetic data required, and the language transfer capabilities of the SWIM-IR dataset across the Indic language family.

The paper demonstrates that synthetic data generation using large language models can be a cost-effective alternative to human-labeled data for improving multilingual dense retrieval models.

Thống kê

With about 850,000 residents, the Comoros is one of the least-populous countries in the world.
In 2001, 34% of the population of Comoros was considered urban.

Trích dẫn

"Synthetic training data generation is promising (e.g., InPars or Promptagator), but has been investigated only for English."
"To improve the quality of the generated query, we propose SAP (Summarize-then-Ask Prompting), where we optimize the prompt to break down the query generation with LLM in two stages."
"SWIM-IR provides synthetic training (query-passage) pairs for improving dense retrieval models without requiring any human supervision."

Thông tin chi tiết chính được chắt lọc từ

Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval

by Nand... lúc arxiv.org 04-17-2024

https://arxiv.org/pdf/2311.05800.pdf

Yêu cầu sâu hơn

How can the proposed SAP approach be extended to other language generation tasks beyond retrieval, such as machine translation or text summarization?

The SAP (Summarize-then-Ask Prompting) approach proposed in the context can be extended to other language generation tasks beyond retrieval, such as machine translation or text summarization, by adapting the methodology to suit the specific requirements of each task.
For machine translation, SAP can be utilized by first summarizing the source text in the original language and then generating the translated text in the target language. This approach can help the model focus on the most relevant information in the source text before generating the translation, potentially improving the quality and accuracy of the translated output.
In the case of text summarization, SAP can be applied by summarizing the input text to capture the key points and then generating a concise summary based on the extracted information. By breaking down the text summarization task into summarization and generation stages, SAP can assist the model in producing more informative and coherent summaries.
Overall, the SAP approach can be adapted and extended to various language generation tasks by incorporating summarization as an intermediate step to enhance the quality and relevance of the generated output.

What are the potential biases or limitations in the synthetic data generated by the LLM, and how can they be mitigated?

The synthetic data generated by the Large Language Models (LLMs) like PaLM 2 in the context may have potential biases or limitations that can impact the performance and generalization of the models. Some of the key biases and limitations in synthetic data generated by LLMs include:

Linguistic Biases: LLMs may exhibit biases towards certain language patterns or structures, leading to biased outputs in synthetic data generation.

Content Biases: The synthetic data may reflect biases present in the training data used to pre-train the LLM, potentially perpetuating stereotypes or misinformation.

Domain Specificity: The synthetic data may not cover a diverse range of domains or topics, leading to limited generalization capabilities in real-world applications.

Quality of Summarization: The quality of the extractive summarization performed by the LLM may vary, impacting the relevance and informativeness of the generated queries.

To mitigate these biases and limitations in synthetic data generated by LLMs, several strategies can be employed:

Diverse Training Data: Incorporating diverse and representative training data sources can help reduce biases and improve the coverage of different language patterns and topics.

Bias Detection and Correction: Implementing bias detection algorithms to identify and mitigate biases in the synthetic data can help improve the fairness and accuracy of the generated outputs.

Human-in-the-Loop Validation: Involving human annotators to validate the quality and relevance of the synthetic data can help identify and correct biases or limitations in the generated content.

Regular Model Evaluation: Continuously evaluating the performance of the LLM in generating synthetic data and fine-tuning the model based on feedback can help address biases and improve the overall quality of the generated data.

By implementing these strategies, the biases and limitations in synthetic data generated by LLMs can be effectively mitigated, enhancing the reliability and effectiveness of the models in various language generation tasks.

Given the success of SWIM-IR in multilingual dense retrieval, how can similar techniques be applied to improve performance on other multilingual NLP tasks like question answering or text classification?

The success of SWIM-IR in multilingual dense retrieval demonstrates the potential of synthetic data generation using LLMs for improving performance in various multilingual NLP tasks. Similar techniques can be applied to enhance performance in other tasks like question answering or text classification by following these strategies:

Task-Specific Data Generation: Adapt the synthetic data generation process to suit the requirements of question answering or text classification tasks. This may involve modifying the prompt structure or incorporating task-specific constraints during data generation.

Fine-Tuning on Task-Relevant Data: Utilize the synthetic data generated by LLMs like PaLM 2 to fine-tune models specifically for question answering or text classification tasks. This can help the models learn task-specific patterns and improve performance.

Incorporating Task-Specific Prompts: Design prompts that are tailored to the question answering or text classification tasks, focusing on generating queries or summaries that are relevant and informative for these tasks.

Evaluation on Benchmark Datasets: Evaluate the performance of the fine-tuned models on standard multilingual question answering or text classification benchmarks to assess their effectiveness and compare them against existing baselines.

By applying similar techniques used in SWIM-IR to question answering and text classification tasks, it is possible to leverage synthetic data generation for improving model performance, enhancing multilingual capabilities, and advancing the field of multilingual NLP across various applications.