Cross Encoders

A cross-encoder in the context of information retrieval is a type of model architecture designed to enhance the performance of retrieving the most relevant information from a large dataset based on a given query. Unlike traditional or simpler encoder models that process queries and documents separately, cross-encoders evaluate the relevance of a document to a query by jointly encoding both the query and the document together in a single pass. This approach allows the model to consider the intricate interactions between the query and the document, leading to a more nuanced understanding and often superior retrieval performance.

Key characteristics:

• Joint Encoding: Cross-encoders take both the query and a candidate document as input and combine them into a single input sequence, often with a special separator token in between. This allows the model to directly learn interactions between the query and the document.
• Fine-Grained Understanding: By considering the query and document together, cross-encoders can better capture the nuances of relevance, including context, semantic similarities, and specific details that might be missed when encoding them separately.
• Computational Intensity: While providing high accuracy, cross-encoders are computationally more intensive than other architectures like bi-encoders, because each query-document pair must be processed together. This can make them less efficient for applications that require scanning through very large datasets.
• Use in Ranking: Cross-encoders are particularly useful for the ranking stage of information retrieval, where a smaller subset of potentially relevant documents (pre-filtered by a more efficient method, like a bi-encoder) needs to be ranked accurately according to their relevance to the query.
• Training and Fine-tuning: Cross-encoders can be trained or fine-tuned on specific tasks or datasets, allowing them to adapt to the nuances of different domains or types of information retrieval tasks.

In practice, cross-encoders are often used in combination with other models in a two-step retrieval and ranking process. An initial, more efficient model (such as a bi-encoder) quickly narrows down the search space to a manageable number of candidate documents, and then a cross-encoder is applied to this subset to accurately rank the documents in order of relevance to the query. This approach balances the need for both efficiency and high accuracy in information retrieval systems.

Pseudo code:

import torch
from transformers import BertTokenizer, BertForSequenceClassification

class CrossEncoder(torch.nn.Module):
    def __init__(self, pretrained_model_name='bert-base-uncased'):
        super(CrossEncoder, self).__init__()
        self.tokenizer = BertTokenizer.from_pretrained(pretrained_model_name)
        # Assuming a binary classification model where 1 indicates relevance.
        self.model = BertForSequenceClassification.from_pretrained(pretrained_model_name, num_labels=2)

    def forward(self, query, document):
        # Tokenize query and document together, separating them with a [SEP] token
        inputs = self.tokenizer.encode_plus(query, document, return_tensors='pt', add_special_tokens=True, truncation=True, max_length=512)
        # Forward pass through the model
        outputs = self.model(**inputs)
        # Get the logits
        logits = outputs.logits
        # Apply softmax to get probabilities
        probabilities = torch.softmax(logits, dim=1)
        # Assuming label 1 corresponds to "relevant"
        relevance_probability = probabilities[:, 1]
        return relevance_probability

# Example usage
cross_encoder = CrossEncoder()

# Example query and document
query = "What is artificial intelligence?"
document = "Artificial intelligence is a branch of computer science that aims to create intelligent machines."

# Compute relevance score
relevance_score = cross_encoder(query, document)

print(f"Relevance Score: {relevance_score.item()}")