Tyler Neylon’s Post

WORKING WITH NON-TRADITIONAL TEXTS: Pattern Recognition, Reconstructed: The Unlikely Return of Fornés’s ‘Evelyn Brown’ https://rebrand.ly/rjr9vcn

Decompose a question into a set of sub-problems / questions, which can either be solved sequentially (use the answer from first + retrieval to answer the second) or in parallel (consolidate each answer into final answer). Various works such as Least-to-Most prompting

Quick and dirty summary of an interesting talk I watched yesterday, by applied math professor David Sumpter on what he calls the "four ways of thinking" -- things we can learn from the mindsets of scientists and how they approach problems: - 🧢 Statistical thinking: Deriving correlations from significant data points while being aware of the limitations of this approach. - 👒 Interactive thinking: Looking for causation between events by directly manipulating the factors in play. - 🤠 Chaotic thinking: Taking into account the importance of initial conditions and how very slight input differences can lead to radically divergent outputs, a.k.a. the butterfly effect. - 🎩 Complex thinking: He didn't say much about this one because of time constraints, but I suppose it's the ability the combine all of the above knowing that real-world phenomena are often non-linear (in the sense that they can be non-deterministic, subject to circular causality, or produce counter-intuitive results, among other oddities). The way I understand it, these aren't mutually exclusive, but rather incremental and can be combined depending on the problem at hand. Like programming paradigms, they represent different approaches to solve problems, and it's useful for a knowledge worker like myself to know these things to be able to choose the right tool for the job. Anyway, I can only recommend you watch the talk; it's very interesting (entertaining even), with a ton of football (soccer) examples, historical figures (mostly scientists and engineers), and even advice for couples 😅 If you're interested, the talk is based on Sumpter's book, "Four Ways of Thinking: Statistical, Interactive, Chaotic and Complex" (ISBN: 9781250806260). One more in my long reading list! #Knowledge #Science #Thinking

useful summary and a nice article for pleasant reading...I wish good reading

"The goal of this post is that you can walk away from this post with a general sense of which performance metric you assessing for your specific use case." A High Level Guide to LLM Evaluation Metrics by David Hundley

I think this is the best source for learning SVD by visual interpretation. What a powerful concept indeed! https://lnkd.in/dzHWYM6T

Here's what you need to know about Chain-of-Verficiation by Meta: Whenever you use a LLM for text generation you run into the risk of hallucination, where the LLM outputs claims that are not grounded in reality. The risk is event higher when you're dealing with long text generation... To tackle this challenge, the researchers device a method that resembles Chain-of-Thought: 1️⃣ Generate an initial response conditioned on the user's instruction. 2️⃣ Generate verification questions conditioned on the instruction and the response. 3️⃣ Generate answers conditioned on the questions. It's important to not condition on the response, or otherwise the LLM might repeat its hallucinations. 4️⃣ Rewrite the draft to fix any inconsistencies / mistakes found in the verification phase.

....one word is not enough to capture the real intentions of any given strategy. But as a way of summarising the motivation and predicting what it could achieve, de-risking constitutes a considerable improvement to both notions of decoupling and strategic autonomy. My latest. https://lnkd.in/eEa_Z29R

In this episode, we discuss Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? by Zorik Gekhman, Gal Yona, Roee Aharoni, Matan Eyal, Amir Feder, Roi Reichart, Jonathan Herzig. The paper explores the effects of integrating new factual information into large language models (LLMs) during the fine-tuning phase, particularly focusing on how this affects their ability to retain and utilize pre-existing knowledge. It was found that LLMs struggle to learn new facts during fine-tuning, indicating a slower learning curve for new information compared to familiar content from their training data. Additionally, the study reveals that as LLMs incorporate new facts, they are more prone to generating factually incorrect or "hallucinated" responses, suggesting a trade-off between knowledge integration and accuracy.

The Geometry of Truth Pretty neat interactive blog to explore how LLMs represent truth. It's based on this recent paper that presents evidence that language models linearly represent the truth or falsehood of factual statements. blog: https://lnkd.in/eGA4ERQb paper: https://lnkd.in/eR_HgEq2