What is Negative Sampling in RAG?

Negative sampling is a crucial technique in retriever training, helping models learn to distinguish between relevant and irrelevant documents.

There are two key components of RAG:

Retriever: Fetches relevant documents or data.

Generator: Generates the output using the retrieved information.

Negative sampling is a critical technique in training retrievers, particularly dense retrievers like DPR (Dense Passage Retrieval). It involves generating “negative examples”—instances where the document or passage is not relevant to the given query. These negatives are used alongside positive examples (relevant query-document pairs) to train the model to distinguish between relevant and irrelevant passages.

This process helps the retriever learn fine-grained semantic distinctions, improving its ability to rank relevant documents higher during inference.

1. Why is Negative Sampling Needed?

In retrieval tasks, the goal is to identify the most relevant documents from a large corpus based on a query.

A positive example: A document that is highly relevant to the query.
A negative example: A document that is irrelevant or less relevant to the query.

Without training the model on negatives, it might struggle to understand what “irrelevant” means, leading to poor ranking performance.

2. How Does Negative Sampling Work?

Negative sampling involves selecting irrelevant or partially relevant passages to contrast against the positive examples. These negatives are included in the training data, and the model is trained to assign a low similarity score to them.

Loss Function with Negative Sampling

Contrastive Loss: Negative sampling is commonly used with a contrastive loss function.
For a given query:
- The similarity score with the positive passage is maximized.
- The similarity scores with negative passages are minimized.
A popular variant is the triplet loss: $L=max⁡(0,margin+sim(q,dnegative)−sim(q,dpositive))\mathcal{L} = \max(0, \text{margin} + \text{sim}(q, d_{\text{negative}}) – \text{sim}(q, d_{\text{positive}}))$
- $q$ : Query embedding.
- $dpositived_{\text{positive}}$ : Positive passage embedding.
- $dnegatived_{\text{negative}}$ : Negative passage embedding.

3. Types of Negative Samples

a. Random Negatives

Passages randomly sampled from the corpus that are not related to the query.
Advantages: Easy to generate, widely used in initial stages of training.
Limitations: Often too easy for the model to distinguish, leading to limited learning.

b. Hard Negatives

Passages that are semantically close to the query but not truly relevant.

Generated using:

- Pre-trained retrieval models (e.g., BM25 or an earlier retriever version).
- Neighboring passages with lexical or semantic similarity to the positive example.
Advantages: Forces the model to make fine-grained distinctions.
Limitations: Computationally expensive to generate and may introduce noise if incorrectly labeled.

c. In-Batch Negatives

Treats all positive examples in the current training batch as negatives for other queries in the batch.
Example:
- For query $q_1$ , the positive passage is $p_1$ . The passages $p2,p3,…p_2, p_3, \dots$ (positives for other queries in the batch) are used as negatives for $q_1$ .
Advantages: Efficient, no extra data needed.
Limitations: Quality depends on batch composition (may not always provide meaningful negatives).

4. Benefits of Negative Sampling

Improves Ranking Performance:
Encourages the retriever to rank positive passages higher than negatives.
Reduces Overfitting:
This prevents the model from focusing solely on memorized patterns by forcing it to generalize across a variety of negatives.
Enhances Semantic Understanding:
Particularly with hard negatives, the model learns subtle differences between closely related passages.

5. Example in Action

Let’s say you are training a retriever to handle a query:

Query: “What is the capital of France?”
Positive Passage: “The capital of France is Paris.”

Negative Sampling Variants:

1. Random Negative:

Passage: “The Eiffel Tower is a famous landmark in France.”

(Irrelevant but loosely related to the query).

2. Hard Negative:

Passage: “Paris is a well-known tourist destination in Europe.”

(Close to the query but doesn’t explicitly answer it).

3. In-Batch Negative:

Passage: “Berlin is the capital of Germany.”

(Another query’s positive example is treated as negative).

6. Challenges in Negative Sampling

1. Choosing the Right Negatives:

Random negatives may be too easy, while overly difficult negatives might confuse the model.

2. Computational Overhead:

Generating hard negatives using existing retrievers or models adds computational cost.

3. Label Noise:

Incorrectly labeled negatives (e.g., passages that are actually relevant) can degrade model performance.

7. Tools and Libraries Supporting Negative Sampling

Hugging Face Datasets: Simplifies handling positive and negative examples in retrieval datasets.

FAISS: Useful for generating hard negatives by identifying nearest neighbors in the embedding space.

BM25: Often used to generate initial hard negatives for dense retriever training.

8. Conclusion

Negative sampling is a crucial technique in retriever training, helping models learn to distinguish between relevant and irrelevant documents. By incorporating random, hard, and in-batch negatives, retrievers achieve higher accuracy and semantic precision, making them more effective for real-world retrieval tasks like those in RAG systems.