Lishuai Jing’s Post

Turning Thought Trees into a Reasoning Forest 🌳🧠 Researchers just introduced Retrieval Augmented Thought Trees (RATT) - a new way for LLMs to "think" that could significantly boost their reasoning and decision-making capabilities. The key innovation is that RATT considers both factual correctness and overall logical soundness at each step of the thought process. It does this by combining two powerful techniques: 1. Retrieval-Augmented Generation (RAG) for fact-checking individual statements 2. LLMs' ability to assess the overall strategy and feasibility of a reasoning path At each node in the thought tree, RATT explores multiple potential next steps, evaluates them using this dual criteria, and then adjusts the tree structure to focus on the most promising branches. The result is a more coherent and efficient reasoning process that stays grounded in facts while also optimizing for the big picture. In experiments across a range of tasks, RATT significantly outperformed existing methods like vanilla Thought Trees and RAG alone. As LLMs continue to be considered for more high-stakes applications, frameworks like RATT could be key to making their outputs more reliable and trustworthy. ↓ Liked this post? Join my newsletter with 35k+ readers that breaks down all you need to know about the latest LLM research: llmwatch.com 💡

Curious about how computational thinking can revolutionize the way we approach complex problems? Check out Henderson Engineers Dauphin's in-depth article for a closer look at the key principles and applications. Believe it or not, it can be as easy as PB&J!

Curious about how computational thinking can revolutionize the way we approach complex problems? Check out Henderson Engineers Dauphin's in-depth article for a closer look at the key principles and applications. Believe it or not, it can be as easy as PB&J!

Microsoft Copilot reflections on the nature of scientific inquiry is that the traditional scientific method involving forming hypotheses, which are essentially educated assumptions that guide our exploration and experimentation may not be best for #quantumprocessors. A more phenomenological approach—observing and experiencing phenomena without the filter of preconceived notions aligns with certain philosophical traditions that emphasize direct experience and perception as the primary means of understanding the world or the requirement of a single state or source of truth. It encourages a form of inquiry that is open to new insights, discoveries, and even #multiplicityframeworks free from the constraints of trying to fit observations into pre-existing storyboards. In practice, both approaches have their merits. The hypothesis-driven method allows us to test specific ideas and theories systematically, while the phenomenological approach can foster a more open-ended exploration that may lead to unexpected insights. The key might be to find a balance between the two, allowing for both structured experimentation and unbounded observation. In the context of data-driven decision-making, this could mean using data to inform hypotheses while remaining open to what the data reveals, even if it challenges our initial assumptions. It’s about being both critical and receptive, allowing the data to speak for itself while also applying rigorous analysis. Ultimately, the most #responsiblestewardship in #scientificpursuit may involve #embracingthecomplexityoftheworld, #recognizingthelimitations of our #tools and #methods, and continually #striving to #refineourunderstanding. It’s a #sharedadventure, indeed, where #humility and #curiosity are our most #pricelesscompanions.

Curious about how computational thinking can revolutionize the way we approach complex problems? Check out Henderson Engineers Dauphin's in-depth article for a closer look at the key principles and applications. Believe it or not, it can be as easy as PB&J!

https://lnkd.in/dYpYABEd Visual Reasoning is the next Frontier in LLM research!

Well deserved, Delaram Esfahani! Current research focusing on "algorithmic contestability", the ability to contest/overturn the outcome of an automated decision system via actionable counterfactual explanations, still lacks empirical studies to understand how to build interactive tools to convey such information more effectively.  In this short paper, we performed an initial investigation, focusing on a fictitious money lending task, by comparing the effectiveness of an explorative or guided interaction pattern in helping users discover actionable explanations tailored to their preferences! Check it out!

The world grows increasingly complex and uncertain, and sensitivity and uncertainty analyses practice must catch up. SimDec offers a powerful tool to explore dynamic interactions within models, visualize uncertainties, and find robust solutions for a better future.

📢 Discover valuable insights on managing technical debt in embedded systems! 📘 Delve into "The Perception of Technical Debt in the Embedded Systems Domain: An Industrial Case Study," a compelling research paper by Ampatzoglou, Chatzigeorgiou, Avgeriou, Abrahamsson, Martini, Zdun, and Systä, presented at the 2016 IEEE 8th International Workshop on Managing Technical Debt (MTD). This study is a must-read for professionals and researchers seeking to understand the crucial impact of technical debt on system sustainability and software quality. Gain practical, evidence-based knowledge that can transform your strategies for managing technical debt, enhancing long-term project outcomes. To explore the findings and implications of this groundbreaking research, visit the full article on GPT-Lab's website: [Read more](https://lnkd.in/dRAfgjrP). We welcome your thoughts and encourage you to share with your network! This post is created and published by our AI helpers. #TechnicalDebt #EmbeddedSystems #SoftwareEngineering #IEEE #ResearchInsights #IndustryTransformation #AIAssistedWriting

Financial engineering lies at the intersection of problem solving and mathematics, unlocking innovative solutions to solve some of the most complex problems across healthcare, environmental science, real estate, finance, and more. A recent piece from Fast Company discusses the ongoing importance of mathematical skills in the evolving job market, especially as automation and AI continue to reshape industries. With mathematical knowledge expected to remain critical across countless fields, the value surrounding financial engineering is only expected to grow. https://hubs.ly/Q02NydTW0