LLMs - Information Matters

In the classic 1950s TV sitcom I Love Lucy, when Lucy did something outrageous her husband Ricky would exclaim “Lucy, you’ve got some explainin’ to do!” Typically, Lucy would come up with some sort of implausible response. Hilarity ensued. Well, it’s not the 1950s anymore but 70+ years later Large Language Models (LLMs) and AI chatbots (e.g., ChatGPT, Gemini) are doing outrageous things (hallucinations, fabrications, misinformation, and worse) and the explanations, if there are any, are just as implausible. And it isn’t funny.

October 16, 2025 Ponego Letswalo

MemGPT: Engineering Semantic Memory through Adaptive Retention and Context Summarization

In traditional information systems, preservation is paramount, deletion represents data loss. MemGPT challenges this paradigm by implementing strategic forgetting through two key mechanisms: summarization and targeted deletion.

Featured Opinion

August 22, 2025 Ponego Letswalo

Can AI Have a Conscience? A Look at Ethics in Machine Learning

Can AI have a conscience? Of course, today’s AI isn’t a sentient being with feelings or guilt. It won’t lose sleep over a tough decision. But as artificial intelligence plays a bigger role in our lives, we do expect it to act responsibly. In essence, we want AI to follow ethical principles, a sort of programmed “conscience” so that it helps society without harming it. This is the crux of AI ethics, an increasingly important topic now that machine learning systems are making decisions that matter.

Featured Translation

April 16, 2025 Jaihyun Park

Rethinking Reuse in Data Lifecycle in the Age of Large Language Models

In the world we are living in, a digital world, some data slips past our awareness, but very little data ever truly disappears. As we, information scientists, are concerned with reproducibility and responsibility of research, data lifecycle models have been developed to manage the complexity. To foster open, transparent, and collaborative science, data is often archived in a repository at the end of the project according to such data lifecycle models. This is often followed by the last step of the lifecycle models, data reuse. Traditionally, this model is cyclical, with reused data leading to new questions and fueling subsequent rounds of research.

Featured InfoFire

March 10, 2025 Shalini Urs

LLMs, AI, and the Future of Research Evaluation: A Conversation with Mike Thelwall on Informetrics and Research Impact

In this episode of InfoFire, I sit down with Professor Mike Thelwall, a well accomplished scholar of Informetrics, to explore the intersections of Large Language Models (LLMs) and research evaluation. We delve into how LLMs are reshaping the landscape of research assessment, examining the promises they hold and the challenges they present in ensuring fair, meaningful, and context-aware evaluations.

InfoFire Multimedia

May 10, 2023 Shalini Urs

Is AI the future of everything? A fireside chat with one of the fathers of the Internet, Vint Cerf.

AI (Artificial Intelligence) is coming at humanity fast and furious. Shalini Urs discusses the new epoch of AI with one of the fathers of the Internet, Vint Cerf.

Editorial

December 8, 2022 Chirag Shah

The Rise of AI Chat Agents and the Discourse with Dilettantes

ChatGPT is a simple-to-use conversation agent developed by OpenAI. Of course, this is not the first time we have seen an AI agent that generates information. Google’s LaMDA and Meta’s Galactica are some recent examples for text generation.

Translation

September 23, 2022 Haihua Chen

Why Does Data Quality Matter? How to Evaluate and Improve Data Quality for Machine Learning Systems

With data as the infrastructure, and machine learning and deep learning as the technology foundations of building AI systems combined, they largely determine the performance, fairness, robustness, reliability, and scalability of AI systems.

Editorial

June 29, 2022 Chirag Shah

Sentient AI — Is that What We Really Want?

There is much too hype around AI systems that are acting like they know or feel. But we are not asking enough if we should even want such systems. Blindly trusting systems that make us believe as if they understand us or know everything can be dangerous.

InfoFire Multimedia

May 4, 2022 Shalini Urs

The Power and the Pitfalls of Large Language Models: A fireside chat with Ricardo Baeza-Yates.

Have you ever wondered how Google helps you complete your search query by suggesting the next terms of your query? Large Language Models (LLMs) power this feature. But LLMs go beyond that feature. Today, LLMs are used in building AI systems and applications ranging from recognizing speech to writing poetry. They have become very powerful, but there are also pitfalls.