retrievalloss

Retrieval loss, sometimes written as retrievalloss or retrievalloss, refers to a family of objective functions used to train models for information retrieval tasks. It measures how well a model can retrieve relevant items in response to a query by encouraging higher similarity or lower distance between query representations and their correct matches, while pushing non-relevant items apart. In neural information retrieval and retrieval-augmented systems, retrieval loss is commonly applied to learned representations, such as dense embeddings, and optimized with gradient-based methods.

Common forms of retrieval loss include contrastive losses (such as InfoNCE), which maximize the similarity between a query and its true match while minimizing similarity to negative examples. A typical softmax-based contrastive loss is L = -log exp(sim(q, p_pos)) / sum_j exp(sim(q, p_j)), where sim is a similarity measure like dot product or cosine similarity. Other variants include hinge or margin-based losses and pointwise cross-entropy over a relevance score. These losses can be used with different architectures, such as bi-encoder or cross-encoder models.

Training setups often rely on in-batch negatives, negative mining strategies (hard or semi-hard negatives), and efficient batching to scale to large corpora. Dense passage retrieval (DPR) and similar systems use retrieval loss to jointly train query and document encoders to align matching pairs. Retrieval loss is applicable to text, multimodal, and other domains where ranking and matching are central goals.

Variants and challenges include selecting informative negatives, avoiding representation collapse, and the computational demands of large candidate sets. Model evaluation typically uses retrieval metrics such as recall@k and nDCG, measured on held-out query-document pairs.