Deep Papers

Explore the fascinating role of the KV cache in enhancing chat experiences with AI models like GPT. Discover how this component accelerates interactions and optimizes context management. Harrison Chu simplifies complex concepts, including attention heads and KQV matrices, making them accessible. Learn how top AI products leverage this technology for fast, high-quality user experiences. Dive into the mechanics behind the scenes and understand the computational intricacies that power modern AI systems.

In this byte-sized podcast, Harrison Chu, Director of Engineering at Arize, breaks down the Shrek Sampler. This innovative Entropy-Based Sampling technique--nicknamed the 'Shrek Sampler--is transforming LLMs. Harrison talks about how this method improves upon traditional sampling strategies by leveraging entropy and varentropy to produce more dynamic and intelligent responses. Explore its potential to enhance open-source AI models and enable human-like reasoning in smaller language models. Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

This week, Aman Khan and Harrison Chu explore NotebookLM’s unique features, including its ability to generate realistic-sounding podcast episodes from text (but this podcast is very real!). They dive into some technical underpinnings of the product, specifically the SoundStorm model used for generating high-quality audio, and how it leverages a hierarchical vector quantization approach (RVQ) to maintain consistency in speaker voice and tone throughout long audio durations. The discussion also touches on ethical implications of such technology, particularly the potential for hallucinations and the need to balance creative freedom with factual accuracy. We close out with a few hot takes, and speculate on the future of AI-generated audio. Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

OpenAI recently released its o1-preview, which they claim outperforms GPT-4o on a number of benchmarks. These models are designed to think more before answering and handle complex tasks better than their other models, especially science and math questions. We take a closer look at their latest crop of o1 models, and we also highlight some research our team did to see how they stack up against Claude Sonnet 3.5--using a real world use case. Read it on our blog:  https://arize.com/blog/exploring-openai-o1-preview-and-o1-miniLearn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

A recent announcement on X boasted a tuned model with pretty outstanding performance, and claimed these results were achieved through Reflection Tuning. However, people were unable to reproduce the results. We dive into some recent drama in the AI community as a jumping off point for a discussion about Reflection 70B.In 2023, there was a paper written about Reflection Tuning that this new model (Reflection 70B) draws concepts from. Reflection tuning is an optimization technique where models learn to improve their decision-making processes by “reflecting” on past actions or predictions. This method enables models to iteratively refine their performance by analyzing mistakes and successes, thus improving both accuracy and adaptability over time. By incorporating a feedback loop, reflection tuning can address model weaknesses more dynamically, helping AI systems become more robust in real-world applications where uncertainty or changing environments are prevalent.Dat Ngo (AI Solutions Architect at Arize), talks to Rohan Pandey (Founding Engineer at Reworkd) about Reflection 70B, Reflection Tuning, the recent drama, and the importance of double checking your research.Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

This week, we're excited to be joined by Kyle O'Brien, Applied Scientist at Microsoft, to discuss his most recent paper, Composable Interventions for Language Models. Kyle and his team present a new framework, composable interventions, that allows for the study of multiple interventions applied sequentially to the same language model. The discussion will cover their key findings from extensive experiments, revealing how different interventions—such as knowledge editing, model compression, and machine unlearning—interact with each other.Read it on the blog: https://arize.com/blog/composable-interventions-for-language-models/Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

This week’s paper presents a comprehensive study of the performance of various LLMs acting as judges. The researchers leverage TriviaQA as a benchmark for assessing objective knowledge reasoning of LLMs and evaluate them alongside human annotations which they find to have a high inter-annotator agreement. The study includes nine judge models and nine exam-taker models – both base and instruction-tuned. They assess the judge models’ alignment across different model sizes, families, and judge prompts to answer questions about the strengths and weaknesses of this paradigm, and what potential biases it may hold.Read it on the blog: https://arize.com/blog/judging-the-judges-llm-as-a-judge/Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

Meta just released Llama 3.1 405B–according to them, it’s “the first openly available model that rivals the top AI models when it comes to state-of-the-art capabilities in general knowledge, steerability, math, tool use, and multilingual translation.” Will the latest Llama herd ignite new applications and modeling paradigms like synthetic data generation? Will it enable the improvement and training of smaller models, as well as model distillation? Meta thinks so. We’ll take a look at what they did here, talk about open source, and decide if we want to believe the hype.Read it on the blog: https://arize.com/blog/breaking-down-meta-llama-3/Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

Chaining language model (LM) calls as composable modules is fueling a new way of programming, but ensuring LMs adhere to important constraints requires heuristic “prompt engineering.” The paper this week introduces LM Assertions, a programming construct for expressing computational constraints that LMs should satisfy. The researchers integrated their constructs into the recent DSPy programming model for LMs and present new strategies that allow DSPy to compile programs with LM Assertions into more reliable and accurate systems. They also propose strategies to use assertions at inference time for automatic self-refinement with LMs. They reported on four diverse case studies for text generation and found that LM Assertions improve not only compliance with imposed rules but also downstream task performance, passing constraints up to 164% more often and generating up to 37% more higher-quality responses.We discuss this paper with Cyrus Nouroozi, DSPY key contributor. Read it on the blog: https://arize.com/blog/dspy-assertions-computational-constraints/Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

Sai Kolasani, a researcher at UC Berkeley’s RISE Lab and Arize AI Intern, discusses RAFT, a method to adapt language models for domain-specific question-answering. RAFT improves models' reasoning by training them to ignore distractor documents, enhancing performance in specialized domains like PubMed and HotpotQA. The podcast explores RAFT's chain-of-thought-style response, data curation, and optimizing performance in domain-specific tasks.