Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval

• URL: http://arxiv.org/abs/2404.04163v2
• Date: Sat, 26 Oct 2024 00:04:38 GMT
• Title: Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval
• Authors: João Coelho, Bruno Martins, João Magalhães, Jamie Callan, Chenyan Xiong,
• Abstract summary: We build on previous research that demonstrated loss of information in the middle of input sequences for causal language models. We examine positional biases at various stages of training for an encoder-decoder model, including language model pre-training, contrastive pre-training, and contrastive fine-tuning.
• Score: 31.9252824152673
• License:
• Abstract: This study investigates the existence of positional biases in Transformer-based models for text representation learning, particularly in the context of web document retrieval. We build on previous research that demonstrated loss of information in the middle of input sequences for causal language models, extending it to the domain of representation learning. We examine positional biases at various stages of training for an encoder-decoder model, including language model pre-training, contrastive pre-training, and contrastive fine-tuning. Experiments with the MS-MARCO document collection reveal that after contrastive pre-training the model already generates embeddings that better capture early contents of the input, with fine-tuning further aggravating this effect.

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