Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder (2102.09206v3)

Abstract: Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. Autoencoder-based LLMs are appealing in dense retrieval as they train the encoder to output high-quality embedding that can reconstruct the input texts. However, in this paper, we provide theoretical analyses and show empirically that an autoencoder LLM with a low reconstruction loss may not provide good sequence representations because the decoder may take shortcuts by exploiting language patterns. To address this, we propose a new self-learning method that pre-trains the autoencoder using a \textit{weak} decoder, with restricted capacity and attention flexibility to push the encoder to provide better text representations. Our experiments on web search, news recommendation, and open domain question answering show that our pre-trained model significantly boosts the effectiveness and few-shot ability of dense retrieval models. Our code is available at https://github.com/microsoft/SEED-Encoder/.

• The paper presents the SEED-Encoder, which employs a weak decoder to force the encoder to capture richer semantics for dense retrieval tasks.
• It reports significant gains in metrics such as mean reciprocal rank and recall across diverse datasets compared to models like BERT and ELECTRA.
• The approach improves long-sequence embedding efficiency by reducing semantic redundancy and optimizing system resource usage in retrieval applications.

An Evaluation of the SEED-Encoder Approach for Text Retrieval Tasks

The paper "Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder" presents a significant inquiry into the optimization of text sequence embeddings for dense retrieval scenarios. The authors identify an issue in autoencoder LLMs used for generating embeddings, whereby a powerful decoder often bypasses the encoder's semantic encoding by leveraging natural language patterns. To address this challenge, they propose a novel pre-training method called SEED-Encoder, which leverages a deliberately weakened decoder to promote the generation of more informative encodings by the encoder.

Key Concepts and Methodology

In dense retrieval applications such as web search and question answering, it becomes imperative to encode both the query and the corpus in a manner where their semantic similarity is preserved in a transformed representation space. Traditional methods employing autoencoder models suffer due to an over-reliance on complex decoders, which can result in semantic redundancy in representations extracted by the encoder.

The SEED-Encoder presents a method predicated on introducing constraints into the encoding-decoding process. By limiting the decoder's capacity and restricting its contextual awareness (i.e., attention span), the encoder is forced into a bottleneck scenario wherein it must encapsulate document semantics more effectively. This approach theoretically and empirically overcomes the shortcoming of typical autoencoder dependencies on language patterns, thereby bolstering the encoder's output representation quality.

Empirical Insights and Performance

Experiments conducted across various domains, such as MS MARCO passage and document retrieval, MIND for news recommendation, and open-domain question answering on the NQ dataset, illustrate SEED-Encoder's superiority over other pre-trained models like standard BERT, ELECTRA, and ERNIE2.0 in retrieval task performance. In particular, SEED-Encoder demonstrated significant improvements in both mean reciprocal rank (MRR) and recall metrics across all datasets in comparison to these baselines.

Noteworthy is SEED-Encoder's effectiveness in long-sequence embeddings where typical encoders fail. The SEED-Encoder retains lower cosine similarity among random long sequence pairs, indicating distinct and diverse representation learning. This robustness translates to practical benefits, such as optimized system resource utilization in dense retrieval tasks by reducing necessary document embeddings without compromising retrieval accuracy.

Theoretical Implications and Future Directions

The theoretical implications of employing a weaker decoder extend beyond immediate practical applications. By forcing information through a tighter bottleneck, the model encourages more effective utilization of available parameters, potentially leading to insights into further reductions in model size without affecting efficacy—a critical consideration for deploying models in resource-constrained environments.

Future work can explore more intricate configurations of weak decoders and transfer the architecture's principles to other sequence-based NLP tasks. Additionally, studying the impact of alternative bottleneck strategies and exploring hybrid architectures that blend the strengths of SEED-Encoder with transformer variants optimized for tasks outside of dense retrieval could provide fertile grounds for subsequent research developments.

In conclusion, the SEED-Encoder demonstrates a promising direction for text encoding in dense retrieval tasks, offering enhancements in representation accuracy and computational efficiency. Its theoretically grounded approach to model design highlights a pivotal consideration for future research and development in LLM pre-training.