Hugging Face Cross-Encoders can rerank your RAG results by treating the query and each retrieved document as a single input pair, allowing for a much more nuanced understanding of relevance than traditional embedding similarity.
Let’s see this in action. Imagine we have a simple RAG system that retrieves documents based on keyword or vector similarity. We’ve got a query, say "What are the benefits of a large language model?", and our initial retriever pulls back these three snippets:
- "Large language models (LLMs) are a type of AI that can understand and generate human-like text. They are trained on vast amounts of data."
- "The benefits of using LLMs include improved efficiency in tasks like summarization and translation. They can also enhance creativity and aid in research."
- "LLMs require significant computational resources for training and inference, and can sometimes generate biased or factually incorrect information."
If we just used a standard embedding similarity, snippet 2 might score highest, which is good. But what if snippet 1, which is more of a definition, also scores quite high due to keyword overlap with "large language model"? Or what if snippet 3, which discusses drawbacks, has a few keywords that coincidentally match the query terms, giving it a slightly higher score than it deserves? This is where reranking comes in.
A Cross-Encoder model takes the query and a candidate document together as a single input and outputs a score representing their joint relevance. Unlike a Bi-Encoder (which is what most standard RAG retrieval uses, where query and document are encoded separately), a Cross-Encoder’s attention mechanism can directly compare words and phrases between the query and the document.
Here’s how you’d implement it using Hugging Face’s transformers library. First, install it:
pip install transformers torch
Then, you can use a pre-trained cross-encoder model. A good starting point is a model fine-tuned for semantic similarity or question answering. Let’s use cross-encoder/ms-marco-MiniLM-L-6-v2.
from transformers import CrossEncoder, CrossEncoderTokenizer, util
import torch
# Load a pre-trained cross-encoder model and tokenizer
model_name = 'cross-encoder/ms-marco-MiniLM-L-6-v2'
model = CrossEncoder(model_name)
tokenizer = CrossEncoderTokenizer.from_pretrained(model_name)
query = "What are the benefits of a large language model?"
documents = [
"Large language models (LLMs) are a type of AI that can understand and generate human-like text. They are trained on vast amounts of data.",
"The benefits of using LLMs include improved efficiency in tasks like summarization and translation. They can also enhance creativity and aid in research.",
"LLMs require significant computational resources for training and inference, and can sometimes generate biased or factually incorrect information."
]
# Prepare the data for the cross-encoder
# The model expects pairs of (query, document)
pairs = [[query, doc] for doc in documents]
# Tokenize the pairs
tokenized_pairs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt')
# Predict scores
# The model outputs a single score for each pair, indicating relevance
scores = model.predict(tokenized_pairs)
# Sort documents by relevance score (descending)
# The scores are raw outputs; for ranking, we just need their order.
# If you want probabilities, you might need to add a sigmoid or softmax depending on the model's output layer.
# For reranking, the raw scores are sufficient.
sorted_scores = torch.sort(scores, dim=0, descending=True)
print("Reranked results:")
for rank, (score, doc) in enumerate(zip(sorted_scores.values.tolist(), [documents[i] for i in sorted_scores.indices.tolist()])):
print(f"{rank + 1}. Score: {score:.4f}, Document: {doc}")
Running this code would output something like:
Reranked results:
1. Score: 2.5678, Document: The benefits of using LLMs include improved efficiency in tasks like summarization and translation. They can also enhance creativity and aid in research.
2. Score: 1.8901, Document: Large language models (LLMs) are a type of AI that can understand and generate human-like text. They are trained on vast amounts of data.
3. Score: -0.5432, Document: LLMs require significant computational resources for training and inference, and can sometimes generate biased or factually incorrect information.
Notice how snippet 2, which directly answers the question about "benefits," gets the highest score. Snippet 1, a definition, gets a moderate score. Snippet 3, discussing drawbacks, gets a negative score, correctly identifying it as less relevant to the benefits query.
The core problem this solves is that standard embedding retrieval (Bi-Encoders) operates on a "bag-of-words" or "semantic space" approximation. It tells you if the query and document are generally about similar topics. Cross-Encoders, by processing query and document together, can understand how specific phrases or nuances in the query relate to specific parts of the document. For example, if the query asks "What are the advantages of X?" and a document says "X has many disadvantages", a Bi-Encoder might still find some similarity if "X" and "advantages/disadvantages" share semantic space. A Cross-Encoder, seeing "advantages" juxtaposed with "disadvantages" in the context of the same query, can penalize that document much more effectively.
The levers you control are primarily:
- The Cross-Encoder Model: Different models are fine-tuned on different datasets (e.g., MS MARCO for search relevance, NLI tasks for natural language inference). Choosing a model appropriate for your domain (e.g., a medical cross-encoder for medical queries) is crucial.
- The Set of Documents to Rerank: You typically don’t rerank your entire corpus. Instead, you use a faster Bi-Encoder or keyword search to retrieve an initial candidate set (e.g., top 50 or 100 documents) and then apply the more computationally expensive Cross-Encoder to this smaller set.
- The Threshold for Final Selection: After reranking, you might decide to only return documents with a score above a certain threshold.
What most people don’t realize is that the order of the input pairs to the tokenizer matters for some models, although for standard cross-encoders designed for similarity, it’s usually [query, document]. The model’s internal attention mechanism dynamically learns which words in the query are most important for understanding the relevance of words in the document, and vice-versa, allowing for a much richer comparison than independent encodings.
The next step in optimizing RAG with advanced reranking might involve exploring custom fine-tuning of cross-encoders on your specific dataset or investigating multi-stage reranking architectures.