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Making Large Language Models A Better Foundation For Dense Retrieval

Published 24 Dec 2023 in cs.CL | (2312.15503v1)

Abstract: Dense retrieval needs to learn discriminative text embeddings to represent the semantic relationship between query and document. It may benefit from the using of LLMs, given LLMs' strong capability on semantic understanding. However, the LLMs are pre-trained by text generation tasks, whose working pattern is completely different from representing texts as embeddings. As a result, it is imperative to study how to adapt LLMs properly so that they can be effectively initialized as the backbone encoder for dense retrieval. In this paper, we propose a novel approach, called LLaRA (LLM adapted for dense RetrievAl), which works as a post-hoc adaptation of LLM for the dense retrieval application. LLaRA consists of two pretext tasks: EBAE (Embedding-Based Auto-Encoding) and EBAR (Embedding-Based Auto-Regression), where the text embeddings from LLM are used to reconstruct the tokens for the input sentence and predict the tokens for the next sentence, respectively. LLaRA turns out to be simple, lightweight, and highly effective. It is applied to adapt LLaMA-2-7B (base) on the Wikipedia corpus, where it substantially improves the model's fine-tuned performances on a variety of dense retrieval benchmarks, like MSMARCO and BEIR. Our model and code will be made publicly available at BGE repository.

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Citations (24)
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Summary

  • The paper proposes LLaRA, a post-hoc adaptation process that refines LLM embeddings to meet the demands of dense retrieval.
  • It introduces two pretext tasks, EBAE and EBAR, to shift the focus from local generative tasks to capturing global semantic relationships.
  • Experiments on MS MARCO and BEIR demonstrate significant improvements, achieving superior retrieval metrics compared to established methods.

Overview of "Making LLMs A Better Foundation For Dense Retrieval"

The paper, "Making LLMs A Better Foundation For Dense Retrieval," presents a methodology designed to refine LLMs for dense retrieval tasks. This approach, termed LLaRA (LLM adapted for dense Retrieval Application), emerges in response to challenges faced when directly applying LLMs, pre-trained on generative tasks, to dense retrieval scenarios.

Methodology: LLaRA

The central contribution of this work is the LLaRA framework, a post-hoc adaptation process for LLMs. LLaRA introduces two pretext tasks: Embedding-Based Auto-Encoding (EBAE) and Embedding-Based Auto-Regression (EBAR). Both tasks are structured to modify the embeddings produced by LLMs, shifting their focus from local semantic contexts (as suited for text generation) to global semantic representations, which better serve dense retrieval purposes.

  • EBAE (Embedding-Based Auto-Encoding): This task prompts the LLM to generate embeddings that can accurately reconstruct the original input sentence, ensuring the embedding captures the entire semantic context.
  • EBAR (Embedding-Based Auto-Regression): Here, the LLM is tasked with using embeddings to predict the following sentence, aligning more closely with the semantic relationships required in retrieval tasks.

The framework utilizes these embeddings to perform retrieval tasks efficiently and accurately, without requiring additional decoding steps.

Experimental Results

The paper reports significant improvements across multiple benchmarks post-adaptation:

  1. MS MARCO Passage Retrieval: LLaRA attains an MRR@10 of 43.1, outperforming several established methods, including those using knowledge distillation and large-scale models such as GTR-XXL.
  2. MS MARCO Document Retrieval: The methodology yields an MRR@100 of 47.5, indicating superior performance even when compared to incumbent approaches such as PROP and ANCE.
  3. BEIR Zero-Shot Retrieval Benchmark: In diverse retrieval scenarios, LLaRA demonstrates high generality and versatility, showcasing an average NDCG@10 of 56.1, surpassing various specialized models.

Implications and Future Directions

The findings suggest that with proper adaptation, LLMs can significantly enhance dense retrieval systems, making them more robust and versatile across various tasks. Importantly, this adaptation does not necessitate labeled data, leveraging unsupervised learning mechanisms to yield competitive performance improvements.

From a practical standpoint, LLaRA's efficiency potentially lowers the compute and resource requirements typically associated with large-scale pre-training, enabling broader use in industry and academia. The approach could catalyze further research into optimizing LLMs for diverse NLP applications beyond their initial generative capabilities.

Future research directions might explore integrating LLaRA with even larger LLMs or combining it with fine-tuning techniques that exploit larger labeled datasets, further boosting retrieval accuracy and application scope. Additionally, its application could extend to identify and refine more nuanced tasks within the retrieval landscape, opening avenues for innovations in complex real-world scenarios.

In conclusion, by overcoming the intrinsic limitations of using LLMs for dense retrieval, this paper lays a foundation for more efficient and effective retrieval systems, suggesting a promising trajectory for both academic exploration and practical implementations in information retrieval contexts.

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