Long Context vs. RAG for LLMs

A retrieval method that breaks down documents into smaller pieces and retrieves the most relevant ones based on their content.

A technique for retrieving information by creating specialized data structures called 'indices' that organize and quickly access relevant content.

A retrieval method that uses hierarchical summaries to capture the essential details of a document at different levels of abstraction.

Retrieval methods that use term frequency to represent text and rank chunks based on similarity.

Retrieval methods that use dense vector representations of both queries and document chunks to calculate relevance based on similarity.

A classic sparse retrieval technique that uses term frequency and inverse document frequency (IDF) to rank documents based on relevance.

A hierarchical tree structure used for indexing, where nodes represent data points and relationships connect them.

A knowledge representation model that uses labeled nodes and edges to represent entities and relationships.

The ability of a language model to perform well on a task without requiring extensive training data. This means that the model can understand and respond to new inputs that it has not been explicitly trained on.

Text generated by a language model that is not supported by the actual information it was trained on.

A process where a language model is augmented with external knowledge sources, such as databases or documents, to improve its accuracy and provide access to real-time information.

A limitation of LLMs where they can only process a certain amount of text at a time. This can restrict the amount of information they can access and use to answer questions.

An approach to enhance LLMs by retrieving relevant information from external sources and feeding it back to the model.

An approach to enhancing LLMs by directly incorporating the external knowledge directly into the model's training data.

The process of evaluating the performance of different approaches, such as RAG and LC, by comparing their results on specific tasks using benchmark datasets.

Identifying and analyzing the contributing factors that lead to differing conclusions and disagreements between studies researching LLMs and their augmentation techniques.

A type of AI model that focuses on extracting information from large datasets. One example includes "LongBench" which aims to improve large language model retrieval capabilities.

A process where the model learns how to access and retrieve information from a knowledge base or database, often using algorithms like BM25 which ranks the relevance of search results.

These are the building blocks of modern AI systems, trained on massive datasets, they can understand and generate human-like text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

This approach involves using LLMs to improve retrieval processes, effectively combining the strengths of both techniques.

This process uses a combination of both knowledge base retrieval (KBR) and retrieval augmented language model (RAG) technologies, effectively utilizing both structured and unstructured data sources.

A specific example of a KBR+RAG system aiming to improve large language model retrieval capabilities, it was published in August 2024.

An example of a RAG system that emphasizes long-form text understanding, developed by a team at Microsoft in late 2024.

A progression of key large language models released between 2023 and 2024. This timeline highlights the advancement of LLMs and their capabilities.

Retriever's performance depends more on the retrieval method used than the generation model, meaning both GPT-4 and GPT-4-Turbo can generate high-quality responses.

The consistency of results across retrieval methods (like Chunk-based, Index-based, etc.) suggests their impact on overall performance is greater than the specific generation model used.

A case study analyzing the most common errors from both LC (language-based retrieval) and RAG (retrieval-augmented generation) methods. RAG is more prone to mistakes than LC.

Analyzing the errors in the case study helps understand the strengths and weaknesses of each retrieval method.

Errors made by only RAG (retrieval-augmented generation) are identified and categorized for a deeper understanding.

Errors made by only LC (language-based retrieval) are identified and categorized for better analysis.

A table that highlights specific examples of incorrect answers made by the RAG method.

A table that highlights specific examples of incorrect answers made by the LC method.

A type of question answering (QA) system that focuses on interpreting and understanding the meaning of a passage of text to find the answer. It is often used for multiple-choice questions, where the answer is already present in the text.

This refers to the type of question answering (QA) task being evaluated. Knowledge-based tasks focus on factual information, reasoning tasks require deeper understanding of relationships and inferences, and reading comprehension tasks assess the ability to understand and interpret text.

This refers to the number of documents relevant to answering a question. Multi-document tasks involve retrieving multiple documents, single-document tasks involve retrieving a single document, and no-document tasks involve retrieving a list of relevant documents.

This refers to the type of answer expected by a QA system. Open-ended questions require a textual answer, multiple-choice questions require selecting an option from a predefined set, and other modes may involve specific formats like tables, lists, or code.

A measure of how complex or challenging a question is, usually reflecting the length and complexity of the required reasoning or text understanding.

Refers to datasets used to evaluate and train question answering (QA) systems. They typically contain a set of questions, associated documents, and corresponding answers.

This refers to the source material from which the questions and answers are derived. Some popular sources include Wikipedia, textbooks, and scientific papers. The source often influences the type of questions and the complexity of the answers.

A crucial topic in NLP, 'long context' refers to the ability of a model to understand and reason across large amounts of text, often exceeding the typical limitations of previous models.

Datasets like NovelQA, which use realistic, long-form narratives like novels, research papers, or other extensive texts, present challenges for models to comprehend and synthesize complex information spread across a coherent, extended text.

Datasets like LongBench, which combine smaller, relevant text segments (often from Wikipedia), present a different challenge by requiring models to process information woven together from multiple sources. These datasets often resemble classic reading comprehension tasks.

Realistic long texts closely mimic real-world reading comprehension tasks where models focus on absorbing and logically interpreting information from a single, continuous body of text.

Synthetic long texts often present challenges similar to factual reasoning tasks, where models need to retrieve and verify information drawn from diverse sources.

Long Context models can be challenging to evaluate. Studies often define 'long' using token counts (over 8k or 32k tokens). However, this is a simplification, as other factors like text coherence, complexity, and the type of reasoning required come into play.

Retrieval Augmented Generation (RAG) is a vital technique for building Long Context models. It involves retrieving relevant text snippets from a large corpus and incorporating them into the model's generation process.

In the context of Long Context, noise refers to irrelevant or distracting information present in the text. This noise can hinder the model's ability to accurately extract and reason about the relevant information needed to answer a question.