GenerativeAI
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Knowlege Management, Graphs, and Retrieval Augmented Generation
Knowledge Management, Graphs, and Retrieval Augmented Generation
There is an odd sentiment in some quarters that GenAI renders knowledge management obsolete. In certain respects, GenAI can make KM less labor intensive. But, overall, GenAI only makes good KM more acutely valuable—i.e., the drag on productivity from poor KM practices becomes more severe and more glaring.
Of particular importance in legal, many applications* currently being built rely on KM methods like knowledge graphs and retrieval augmented generation (or “RAG”) in order to be performant and fit for purpose.
Retrieval Augmented Generation (RAG) has become a popular technique in AI, allowing language models to incorporate external knowledge for more accurate responses. Best practices for RAG are still evolving, but thorough evaluation, data quality, chunking strategies, embedding models, and information retrieval methods are key considerations. Experimentation and optimization are necessary to deploy an effective RAG system. The choice of metrics, data preparation, metadata, and additional context also impact RAG performance. The evaluation of RAG systems requires a reference dataset and human evaluators. The quality of the knowledge corpus and the chunking strategy are crucial for RAG performance. The choice of embedding models significantly affects retrieval and quality scores. Data management and information retrieval systems, such as vector databases and knowledge graphs, play a vital role in RAG applications. The selection of the right language model is essential, considering factors like accuracy, latency, and cost. Systematic exploration and hyperparameter optimization tools can further enhance RAG systems.
Retrieval Augmented Generation (RAG) has become a popular technique in AI, allowing language models to incorporate external knowledge for more accurate responses. Best practices for RAG are still evolving, but thorough evaluation, data quality, chunking strategies, embedding models, and information retrieval methods are key considerations. Experimentation and optimization are necessary to deploy an effective RAG system. The choice of metrics, data preparation, metadata, and additional context also impact RAG performance. The evaluation of RAG systems requires a reference dataset and human evaluators. The quality of the knowledge corpus and the chunking strategy are crucial for RAG performance. The choice of embedding models significantly affects retrieval and quality scores. Data management and information retrieval systems, such as vector databases and knowledge graphs, play a vital role in RAG applications. The selection of the right language model is essential, considering factors like accuracy, latency, and cost. Systematic exploration and hyperparameter optimization tools can further enhance RAG systems.
OpenAI's text-to-image model, DALL-E, has gained significant attention and popularity. It has been followed by other similar models, and the technology has sparked creativity and controversy. While the models have limitations, they are seen as a step towards artificial general intelligence and have the potential to revolutionize creative industries.
OpenAI's text-to-image model, DALL-E, has gained significant attention and popularity. It has been followed by other similar models, and the technology has sparked creativity and controversy. While the models have limitations, they are seen as a step towards artificial general intelligence and have the potential to revolutionize creative industries.
The article discusses the importance of clean legal data and how it can benefit legal departments. Dirty data, which is inaccurate or not well-understood, can hinder decision-making. Legal departments often lack clean data due to reliance on other systems and lack of ownership over processes. To clean up data, legal teams should prioritize contracts, intellectual property assets, and third-party expenditures. Collaboration with other departments and appointing a professional to lead data cleanup efforts are recommended. CLOs should define data standards, identify tools, develop a repository strategy, and create a data curation strategy.
The article discusses the importance of clean legal data and how it can benefit legal departments. Dirty data, which is inaccurate or not well-understood, can hinder decision-making. Legal departments often lack clean data due to reliance on other systems and lack of ownership over processes. To clean up data, legal teams should prioritize contracts, intellectual property assets, and third-party expenditures. Collaboration with other departments and appointing a professional to lead data cleanup efforts are recommended. CLOs should define data standards, identify tools, develop a repository strategy, and create a data curation strategy.
Retrieval systems aim to identify and return the most relevant information for a given query from a large corpus of data. This involves searching a database, finding pertinent passages, or retrieving useful knowledge. Traditional single-step retrieval has limitations, so multi-step frameworks break down the process into modular steps for better results.
Retrieval systems aim to identify and return the most relevant information for a given query from a large corpus of data. This involves searching a database, finding pertinent passages, or retrieving useful knowledge. Traditional single-step retrieval has limitations, so multi-step frameworks break down the process into modular steps for better results.
Over four months, the author created a retrieval-augmented generation (RAG) product prototype using a Large-Language Model (LLM). However, due to cost issues, the project is being shut down. The author explores the challenges faced and alternative routes for the future of the project.
Over four months, the author created a retrieval-augmented generation (RAG) product prototype using a Large-Language Model (LLM). However, due to cost issues, the project is being shut down. The author explores the challenges faced and alternative routes for the future of the project.
Cisco is acquiring cybersecurity firm Splunk in a $28 billion deal to enhance its defenses against security threats potentially intensified by AI's widespread use. The deal aims to boost AI-enabled security and observability, helping organizations improve security. The acquisition is expected to close by Q3 2024, subject to approval from Splunk shareholders.
Cisco is acquiring cybersecurity firm Splunk in a $28 billion deal to enhance its defenses against security threats potentially intensified by AI's widespread use. The deal aims to boost AI-enabled security and observability, helping organizations improve security. The acquisition is expected to close by Q3 2024, subject to approval from Splunk shareholders.
Hollywood Labor Day strikes due to AI role in film industry. Writers and actors demand AI regulation amid shutdown.
Hollywood Labor Day strikes due to AI role in film industry. Writers and actors demand AI regulation amid shutdown.
Google's Bard chatbot introduces a "double-check" feature to verify AI-generated responses. After providing an answer, users can click the "Google It" button, and Bard evaluates the response by checking web content. It highlights sentences in green if they have supporting citations or brown if not. This feature aims to address the issue of chatbots providing inaccurate information. While it's a step toward accountability, users may still need to verify citations for complete accuracy.
Google's Bard chatbot introduces a "double-check" feature to verify AI-generated responses. After providing an answer, users can click the "Google It" button, and Bard evaluates the response by checking web content. It highlights sentences in green if they have supporting citations or brown if not. This feature aims to address the issue of chatbots providing inaccurate information. While it's a step toward accountability, users may still need to verify citations for complete accuracy.
A recent paper by Berglund et al. reveals a fundamental limitation in large language models (LLMs). They struggle with basic inferential tasks, echoing concerns raised in previous work like Fodor and Pylyshyn's 1988 critique. Despite advancements, LLMs still lack systematic reasoning abilities. The challenge lies in their inability to generalize relationships between entities in a symmetrical manner, even after extensive training. This limitation highlights the need for new approaches in AI, potentially involving neurosymbolic methods.
A recent paper by Berglund et al. reveals a fundamental limitation in large language models (LLMs). They struggle with basic inferential tasks, echoing concerns raised in previous work like Fodor and Pylyshyn's 1988 critique. Despite advancements, LLMs still lack systematic reasoning abilities. The challenge lies in their inability to generalize relationships between entities in a symmetrical manner, even after extensive training. This limitation highlights the need for new approaches in AI, potentially involving neurosymbolic methods.
