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Prompting Large Language Models to Generate Effective Dense and Sparse Representations for Zero-Shot Document Retrieval

Core Concepts
Large language models can be effectively prompted to generate both dense embedding and sparse bag-of-words representations for zero-shot document retrieval, outperforming previous unsupervised LLM-based retrieval methods.
The paper introduces PromptReps, a method that prompts large language models (LLMs) to generate dense and sparse representations for zero-shot document retrieval, without any further training. Key highlights: Existing LLM-based retrieval methods either require costly re-ranking of a small set of documents or rely on unsupervised contrastive training to transform LLMs into text embedding models. PromptReps leverages prompt engineering to directly obtain both dense and sparse representations from LLMs, enabling effective full-corpus retrieval. The authors evaluate PromptReps on the BEIR benchmark using various open-source LLMs, finding that the retrieval effectiveness increases with the scaling of LLM size. PromptReps outperforms previous LLM-based embedding methods, and the hybrid of dense and sparse representations achieves state-of-the-art zero-shot retrieval performance without any supervised training. The results demonstrate that prompt engineering can stimulate LLMs' inherent text encoding abilities, making them effective text representations for retrieval tasks.
The paper does not provide specific numerical data points, but rather discusses the overall retrieval effectiveness of the proposed PromptReps method compared to baselines.
"PromptReps combines the advantages of both categories: no need for training and the ability to retrieve from the whole corpus." "Our experimental evaluation on the BEIR zero-shot document retrieval datasets illustrates that this simple prompt-based LLM retrieval method can achieve a similar or higher retrieval effectiveness than state-of-the-art LLM embedding methods that are trained with large amounts of unsupervised data, especially when using a larger LLM."

Deeper Inquiries

How can the prompt engineering techniques used in PromptReps be extended to other text-based tasks beyond document retrieval, such as text classification or generation

Prompt engineering techniques used in PromptReps can be extended to other text-based tasks beyond document retrieval by adapting the prompts to suit the specific requirements of the task at hand. For text classification, prompts can be designed to elicit representations that capture the key features of the text relevant to the classification task. This could involve prompts that guide the LLM to generate representations emphasizing certain aspects of the text that are indicative of different classes or categories. By prompting the LLM to focus on specific elements or characteristics of the text, it can learn to encode information in a way that is more conducive to classification tasks. For text generation, prompts can be tailored to encourage the LLM to generate coherent and contextually relevant text. By providing prompts that set the tone, style, or content of the generated text, the LLM can produce outputs that align more closely with the desired output for the generation task. This approach can help control the output of the LLM and steer it towards generating text that meets specific criteria or follows certain patterns. In both cases, the key is to design prompts that guide the LLM to learn representations that are well-suited to the task requirements. By customizing prompts to target the specific needs of text classification or generation tasks, the LLM can be effectively leveraged for a wide range of text-based applications beyond document retrieval.

What are the potential limitations or drawbacks of relying solely on prompt engineering to transform LLMs into effective text representations, and how could these be addressed

While prompt engineering in PromptReps offers a simple and effective way to transform LLMs into robust text representations for document retrieval, there are potential limitations and drawbacks to consider. One limitation is the reliance on the quality and specificity of the prompts. If the prompts are not well-designed or do not capture the essential information in the text, the resulting representations may be suboptimal. This could lead to reduced retrieval effectiveness and performance. Another drawback is the potential for bias or overfitting in the prompt design. If the prompts are biased towards certain types of information or if they are too specific to the training data, the LLM may learn representations that are not generalizable to new or unseen data. This could limit the applicability of the method to diverse datasets or tasks. To address these limitations, it is essential to carefully design prompts that are diverse, representative of the task requirements, and generalizable across different contexts. Additionally, incorporating techniques such as prompt tuning, where prompts are adjusted based on feedback or validation results, can help improve the robustness and adaptability of the prompt engineering approach.

Given the strong performance of PromptReps, how might this approach be combined with or integrated into existing supervised or unsupervised retrieval methods to further enhance retrieval effectiveness

The strong performance of PromptReps can be further enhanced by combining or integrating this approach with existing supervised or unsupervised retrieval methods. One way to do this is to use PromptReps as a pre-processing step to generate initial text representations, which can then be fed into traditional retrieval models for refinement or ranking. By leveraging the strengths of both PromptReps and existing methods, it is possible to achieve improved retrieval effectiveness and efficiency. Additionally, PromptReps can be used in conjunction with supervised learning techniques to fine-tune the representations generated by the LLM. By incorporating labeled data and training the model on specific tasks or datasets, the representations can be optimized for the retrieval task at hand. This hybrid approach combines the benefits of unsupervised prompt engineering with the precision of supervised learning, leading to enhanced performance in document retrieval. Furthermore, integrating PromptReps into a multi-stage retrieval pipeline, where different methods are applied sequentially to refine the retrieval results, can help address the limitations of individual approaches and improve overall performance. By strategically combining PromptReps with existing retrieval methods, it is possible to create a comprehensive and effective retrieval system that leverages the strengths of each approach.