PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval
CoRR(2024)
Abstract
The current use of large language models (LLMs) for zero-shot document
ranking follows one of two ways: 1) prompt-based re-ranking methods, which
require no further training but are feasible for only re-ranking a handful of
candidate documents due to the associated computational costs; and 2)
unsupervised contrastive trained dense retrieval methods, which can retrieve
relevant documents from the entire corpus but require a large amount of paired
text data for contrastive training. In this paper, we propose PromptReps, which
combines the advantages of both categories: no need for training and the
ability to retrieve from the whole corpus. Our method only requires prompts to
guide an LLM to generate query and document representations for effective
document retrieval. Specifically, we prompt the LLMs to represent a given text
using a single word, and then use the last token's hidden states and the
corresponding logits associated to the prediction of the next token to
construct a hybrid document retrieval system. The retrieval system harnesses
both dense text embedding and sparse bag-of-words representations given by the
LLM. 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.
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