André Meyer-Vitali’s Post

Today we remember Niklaus Wirth, who recently passed away. Born on this day in 1934, Wirth received the 1984 #ACMTuringAward for developing a sequence of innovative #computerlanguages, EULER, ALGOL-W, MODULA and #PASCAL. PASCAL has become pedagogically significant and has provided a foundation for future computer language, systems, and architectural research. Watch Wirth discuss the importance of abstraction to language design: https://bit.ly/40X6djN

Your ability to inculcate the desire to gain insights from AI Research papers is paramount to your success.

In this episode, we discuss Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention by Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal. The paper presents a novel method for enabling Transformer-based Large Language Models to process extremely long inputs while keeping memory and computational requirements fixed. The technique introduced, called Infini-attention, blends a new form of memory-augmented attention with local and linear long-term attention within a single Transformer layer. The effectiveness of this method is demonstrated through impressive performance on long-context challenges, including a one million length sequence task and a half-million word book summarization, while maintaining efficient streaming capabilities and a minimal increase in memory parameters.

In this episode, we discuss To Believe or Not to Believe Your LLM by Yasin Abbasi Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári. The study investigates uncertainty quantification in large language models (LLMs), focusing on distinguishing large epistemic uncertainty to identify unreliable outputs and potential hallucinations. By employing an information-theoretic metric and a method of iterative prompting based on prior responses, the approach effectively detects high uncertainty scenarios, particularly in distinguishing between cases with single and multiple possible answers. The proposed method outperforms standard strategies and highlights how iterative prompting influences the probability assignments of LLM outputs.

On YouTube🎙️ Dive deep into the Large Language Model Evaluation with Gauthier Guinet, on our latest LLM Daily episode. 🌐 Elevate your understanding of LLM Evaluation by tuning in for insightful, scientific discussions. #LLMEvaluation #AIResearch #ScientificInsights 🚀 Youtube: https://lnkd.in/gQa5funr

In this episode, we discuss System 2 Attention (is something you might need too) by Jason Weston, Sainbayar Sukhbaatar. The paper introduces System 2 Attention (S2A), an approach that improves Transformer-based Large Language Models by regenerating input contexts to focus on relevant information before processing, thereby enhancing the generation of the next token. S2A was created to address the problem of standard soft attention mechanisms that often integrate distracting information into outputs. In testing, S2A demonstrated superior performance by producing more factual, objective, and less biased responses on tasks such as question answering, math word problems, and longform content generation.

Day 49 Cracking the Code: Roman to Integer Conversion!  Enhance your algorithmic skills by deciphering the symbolic values and transforming Roman numeral strings into numerical representations. #100daysofcode #drgvishwanathanchallenge #codingchallenge #geeksforgeeks #datastructuresandalgorithms #codingjourney #100daysofcodechallenge

In this episode, we discuss Transformers need glasses! Information over-squashing in language tasks by Federico Barbero, Andrea Banino, Steven Kapturowski, Dharshan Kumaran, João G. M. Araújo, Alex Vitvitskyi, Razvan Pascanu, Petar Veličković. The paper explores how information propagates in decoder-only Transformers, revealing a phenomenon where different input sequences can result in nearly identical final token representations. This issue, worsened by low-precision floating-point formats, impairs the model’s ability to distinguish between these sequences, leading to errors in specific tasks. The authors provide theoretical and empirical evidence of this problem and suggest simple solutions to mitigate it.

👉 Chess skills as an argument against parrots: ----- "This research is not only relevant to chess but also offers insights into the potential of Transformer architecture in other domains. The team explicitly refers to the narrative of large language models as "stochastic parrots": "Our work thus adds to a rapidly growing body of literature showing that complex and sophisticated algorithms can be distilled into feed-forward transformers, implying a paradigm-shift away from viewing large transformers as “mere” statistical pattern recognizers to viewing them as a powerful technique for general algorithm approximation." https://lnkd.in/d87i7x4B

A new lecture in the Course: "Theory of Computation", has been published: https://lnkd.in/dD-GKNfB Lecture 23: Turing Decidable Languages This lecture introduces a set of abstract decidable problems from automata theory such as the problem of deciding whether a given finite state automata accepts a certain string, whether the language generated by a context-free grammar is empty, etc. The proofs of decidability is simply based on constructing a Turing machine (an algorithm) that decides the given language. The instruction language is Arabic, however, all material, slides, definitions, results, concepts, examples, etc., are all given in English.