What is Chain-Of-Density? (CoD)

[AaronWard]

Chain of Density (CoD) for Text Summarization:

Introduction:

• The art of crafting a perfect summary lies in striking a balance between details and core ideas. Too detailed, and it becomes cumbersome; too vague, and it lacks information. Researchers from Salesforce AI, MIT, and Columbia University have unveiled a new summarization method using GPT-4, called the "Chain of Density" (CoD). The CoD method is designed to create summaries that maintain a delicate balance between density (information-richness) and readability.

The CoD Approach:

• The CoD method involves generating summaries that begin with a sparse representation and gradually incorporate more entities without increasing the summary length.
• The process involves two main steps iterated five times:
• Identify 1-3 informative entities from the article that haven't been included in the previous summary.
• Create a new summary of the same length that covers every detail from the previous summary and the newly identified entities.

Guidelines for the CoD Technique:

• The first summary should be longer (~80 words) but not very specific.
• As new entities are added, the summary must be rephrased to maintain the same length. This requires enhancing the flow, compressing details, and eliminating redundant phrases.
• Despite becoming denser with each iteration, the summaries should remain coherent and comprehensible without needing to refer back to the original article.

Evaluation & Results:

• A human preference study was conducted using 100 CNN DailyMail articles. The study aimed to compare the CoD summaries against those generated by the vanilla GPT-4 prompt.
• The results indicated that humans generally prefer GPT-4 summaries that are denser than those made by the vanilla prompt. In fact, the preferred density of these summaries was close to that of human-written summaries.
• The researchers also utilized GPT-4 itself to evaluate the summaries based on various dimensions such as informativeness, coherence, and overall quality. The results suggest that as summaries become denser, they also tend to be more informative, but after a certain point, readability starts to decline.
• Implications & Contributions:

The CoD technique offers a way to control the density of summaries, ensuring they are information-rich yet readable.
The method sheds light on the tradeoff between informativeness and clarity in summaries. For the broader research community, the team has made available 500 annotated CoD summaries and an additional 5,000 unannotated summaries on HuggingFace.

Limitations:

• The study was conducted only in the domain of news summarization.
• The human evaluation did not always show high agreement between annotators, underscoring the subjective nature of summarization quality.
• GPT-4 is a closed-source model, so only evaluation data and summaries have been made public.

---

Example

• Demo example
• I pasted the test from fastchat post and this is what it did.

Denser Summary 1
Missing Entities: LLM ecosystem
FastChat is a versatile platform that serves as a training, serving, and evaluation hub for large language model (LLM) based chatbots. It offers state-of-the-art model training and evaluation, multi-model serving system for deployment, and real-time chatbot competitions. FastChat supports a wide range of models, allows flexible deployment on various hardware configurations, and provides rich datasets. Its APIs are compatible with existing platforms like OpenAI, facilitating integration for developers. FastChat plays a crucial role in LLM research, deployment, evaluation, and community engagement in the LLM ecosystem.

Denser Summary 2
Missing Entities: FastChat's position
FastChat, a comprehensive platform for training and evaluating language models, occupies a pivotal position in the LLM ecosystem. It offers the infrastructure to train and evaluate models, a serving system for deployment, and real-time chatbot competitions. With support for various models and flexible deployment options, FastChat enables researchers and developers to fine-tune and experiment with models. It fosters community engagement through its open-source nature and the Chatbot Arena. FastChat serves as an all-in-one toolkit, combining a research lab, deployment platform, and chatbot competition stage to advance the field of LLMs.

Denser Summary 3
Missing Entities: FastChat's features
FastChat is a comprehensive platform that provides training, serving, and evaluation functionalities for large language model (LLM) based chatbots. It supports a variety of models, offers flexible deployment options, and provides rich datasets. FastChat's APIs are compatible with existing platforms like OpenAI, ensuring smooth integration for developers. The platform also includes the Chatbot Arena, allowing real-time chatbot competitions for benchmarking LLM effectiveness. FastChat's features encompass training and evaluation tools, model serving infrastructure, datasets, and community engagement, making it a valuable resource in the LLM ecosystem.

Denser Summary 4
Missing Entities: Research, deployment, evaluation
FastChat, an inclusive platform for LLMs, addresses the needs of the LLM community in research, deployment, and evaluation. It offers training tools, datasets, and benchmarks, facilitating LLM research and experimentation. FastChat's serving infrastructure enables the deployment of models for end-users or integration into applications. It also provides evaluation tools like the Chatbot Arena and MT-bench datasets for assessing model performance. Furthermore, FastChat fosters community engagement by encouraging user interaction, feedback, and participation in model evaluation. With its wide range of functionalities, FastChat plays a crucial role in advancing the LLM field.

Denser Summary 5
Missing Entities: none
FastChat is a consolidated platform for the LLM community, offering tools and infrastructure for training, evaluation, and deployment of chatbot models. It supports various models, hardware configurations, and integrates with existing platforms like OpenAI. FastChat provides datasets for training and evaluating models and enables real-time competitions through the Chatbot Arena. This platform empowers researchers and developers to fine-tune models, deploy them for user interactions, and gather feedback from the community. With its comprehensive capabilities, FastChat plays a pivotal role in accelerating advancements and fostering engagement in the LLM domain.

Tell me about the deployment process in fastchat

The deployment process in FastChat involves making trained chatbot models available for user interactions. FastChat offers a flexible deployment system that allows users to deploy models on various hardware configurations. This includes setups with single GPUs, multi-GPU systems, CPUs, and even specific processors like Apple's Metal backend or Intel's XPU. By supporting different hardware setups, FastChat ensures compatibility across a range of devices and environments.

Additionally, FastChat provides a serving infrastructure that enables the deployment of models and makes them accessible to end-users or integratable into applications. This infrastructure ensures that the deployed models can handle user interactions efficiently and effectively.

Overall, FastChat simplifies the deployment process by offering flexibility in hardware configurations and providing a serving infrastructure that streamlines the availability of trained chatbot models for users.

[AaronWard]

thought: could potentially add an additional step to check all the entities that were chosen and all the entities it could find in the document and suggest to run again after reaching the max iterations condition

[AaronWard]

Advantages:
Efficient Storage and Reduced Dimensionality:

• CoD summarization can help reduce the size of the original documents while retaining essential information. This reduction can lead to more efficient storage of vector embeddings, especially if the original dataset contains verbose or redundant information.
  Potential for Faster Retrieval:
• If embeddings are primarily generated from densified information, the search space during retrieval could be reduced. This might lead to quicker searches, as there could be fewer vectors to compare against during a query.
  Improved Precision:
• Densified summaries aim to capture the essence of a text. Embeddings generated from such summaries might be more focused, which could enhance the precision of retrievals. The noise introduced by peripheral details in full texts might be mitigated.
  Enhanced Context in RAG:
• For Retrieval Augmented Generation tasks, using embeddings from densified texts might provide more concise and relevant context to LLMs, potentially enhancing the quality of the generated responses.