Performance Comparison of Retrieval Methods

A technique that increases the context window size to allow the model to process and understand larger amounts of text information.

A method that uses retrievers to select relevant information from a vast pool of text and feeds it to the model.

The ability of language models (LLMs) to incorporate external contexts and integrate them into their responses.

A trend where language models are being developed to handle progressively longer contexts.

Combining both Long Context and Retrieval-Augmented Generation techniques to leverage the strengths of both approaches.

A visual representation that shows the evolution of language models in terms of their ability to handle different context lengths.

A central challenge in the field of natural language processing, aiming to enable language models to effectively incorporate long external contexts.

The process of evaluating language models with respect to their ability to leverage long context and retrieval mechanisms.

The type of task a dataset focuses on, categorized as "Knowledge" (K) for factual questions, "Reasoning" (R) for tasks requiring logical inferences, or "Reading Comprehension" (C) for comprehension-based questions.

Refers to whether a dataset uses single or multiple documents to answer a question. 'Single' implies a single document contains the necessary information, while 'Multi' indicates using information from multiple documents.

The average length of documents used in the datasets, measured in words.

This refers to the different research papers that use the dataset to evaluate Long Context (LC) or Retrieval-Augmented Generation (RAG) models.

The total number of questions in the dataset.

The number of questions in the dataset that are retained after removing those that don't require context, such as questions about simple facts.

The percentage of questions kept after removing those that don't require context.

Specifies the format of a questions, including "Open" for free-response questions and "MCQ" for multiple-choice questions.

A way to measure a retriever's performance by comparing its answers to a gold standard, where the retriever's answer is marked "Better", "Same", or "Worse" based on the similarity to the gold standard.

A method that divides text into chunks and retrieves information based on these specific chunks.

A method that uses a tree-like structure to store and retrieve information. It's designed for efficient search and retrieval in large datasets.

A specific type of retrieval model known for its performance in answering questions that require understanding the entire context of a document, such as a research paper.

A method of retrieval that involves dividing text into windows and processing information within those windows.

A retrieval model using text embeddings to represent documents and queries, enabling the model to find relevant information based on semantic similarity.

A method for evaluating language models, where the model's performance is measured by its ability to answer questions correctly using both long context and retrieval-augmented generation techniques.

A measure of how well a model's answer aligns with the reference answer, taking into account both precision and recall.

A technique that analyzes a large amount of text by clustering similar chunks together and summarizing each cluster, forming a hierarchical structure, which is further flattened to facilitate efficient query comparison.

A type of evaluation setting where a model is considered better if it outperforms another model even slightly, taking into account both exact matches and overall meaning.

A method for analyzing and comparing the effectiveness of different approaches, particularly Long Context and Retrieval-Augmented Generation, in answering complex questions.

A process of building a hierarchical structure by grouping similar text chunks together, and then summarizing each group creating a parent node, which continues until no further grouping is possible.

The process of comparing the performance of language models, particularly their ability to leverage long contexts and external information.

A problem in Natural Language Processing (NLP) where models struggle to effectively use and integrate large amounts of external information.

A type of dataset that focuses on providing factual information, often used to evaluate a model's ability to understand and answer questions about real-world knowledge.

A type of dataset that tests a model's ability to reason logically and draw inferences from given information.

A type of dataset designed to evaluate a model's ability to understand and answer questions based on reading and comprehension.

Long Context (LC) models excel at understanding dense information like Wikipedia articles, providing accurate answers to specific questions, and performing well on factual knowledge tasks. They are ideal for tasks requiring detailed understanding and complex information retrieval.

Retrieval-Augmented Generation (RAG) models excel at handling fragmented information, especially in dialogue-based scenarios and for answering more general questions. They shine when dealing with loose, conversational-style information.

OP-RAG preserves the original order of chunks from the context, placing them in the same sequence they appear in the original document.

LC LLM-RAG puts the highest-scoring chunks at the beginning and end of the context, aiming to surround the model with the most relevant information first.

Relevance feedback is a technique where the user provides feedback on the retrieved results, allowing the system to refine its search and provide more relevant information in subsequent iterations.

Query expansion aims to improve the search process by adding additional keywords or phrases to the original query, thereby broadening the scope of the search and retrieving more relevant information.

Evaluating RAG on a wider range of datasets and models is expensive, especially with the rapid emergence of new LLMs. This limitation can hinder the generalization of findings and make it challenging to assess the true capabilities of RAG.

Research focusing on RAG has mainly covered textual contexts. Neglecting other modalities like audio, video, and multi-modal contexts limits the applicability of current findings to real-world scenarios involving diverse forms of information.