GenAI

In this context, “interaction” refers to the process of assessing how well a document matches a given search query by comparing their representations.

A dense retrieval model is a model that uses some type of neural network architecture to retrieve relevant documents for a search query.

Traditional methods for retrieval commonly use “no-interaction” retrieval models. In this case, the search query and documents are processed separately

Advantages of no-interaction retrieval models are primarily that they are fast and computationally efficient

These characteristics make full interaction models great for second-stage retrieval, like reranking a curated set of candidate documents

extremely computationally expensive

contextually rich

scalable and contextually rich

storage requirements - they require an embedding for each token, which requires a lot more storage for a complete set of vectors

Disadvantages of no-interaction retrieval models lie in the lack of interaction between the search query and the documents.

multimodal late interaction retrieval models

vision language models (VLMs) instead of text-only models

The primary evaluation metric used in τ-bench is pass^k, which measures the probability that all k independent task trials are successful for a given task, averaged across all tasks. Unlike the pass@k metric that is common for other LLM evaluations (which measures if at least one of k trials succeeds), pass^k evaluates consistency and reliability—critical qualities for customer service applications where consistent adherence to policies is essential.