Composable Interventions for Language Models

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حدود یک سال پیش 42:35

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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.

49 قسمت

#Science #Tech #Math #Business #Arize AI

Composable Interventions for Language Models

Deep Papers

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published حدود یک سال پیش

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MP3•خانه قسمت

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

49 قسمت

#Science #Tech #Math #Business #Arize AI

همه قسمت ها

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Scalable Chain of Thoughts via Elastic Reasoning 28:54

۷ روز پیش28:54

28:54

In this week's episode, we talk about Elastic Reasoning, a novel framework designed to enhance the efficiency and scalability of large reasoning models by explicitly separating the reasoning process into two distinct phases: thinking and solution . This separation allows for independent allocation of computational budgets, addressing challenges related to uncontrolled output lengths in real-world deployments with strict resource constraints. Our discussion explores how Elastic Reasoning contributes to more concise and efficient reasoning, even in unconstrained settings, and its implications for deploying LRMs in resource-limited environments. Read the paper here: https://arxiv.org/pdf/2505.05315 Sign up for the next discussion & see more AI research: arize.com/ai-research-papers Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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Sleep-time Compute: Beyond Inference Scaling at Test-time 30:24

21 روز پیش30:24

30:24

What if your LLM could think ahead —preparing answers before questions are even asked? In this week's paper read, we dive into a groundbreaking new paper from researchers at Letta, introducing sleep-time compute: a novel technique that lets models do their heavy lifting offline , well before the user query arrives. By predicting likely questions and precomputing key reasoning steps, sleep-time compute dramatically reduces test-time latency and cost—without sacrificing performance. We explore new benchmarks—Stateful GSM-Symbolic, Stateful AIME, and the multi-query extension of GSM—that show up to 5x lower compute at inference, 2.5x lower cost per query, and up to 18% higher accuracy when scaled. You’ll also see how this method applies to realistic agent use cases and what makes it most effective.If you care about LLM efficiency, scalability, or cutting-edge research. Explore more AI research, or sign up to hear the next session live: arize.com/ai-research-papers Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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LibreEval: The Largest Open Source Benchmark for RAG Hallucination Detection 27:19

۵ weeks پیش27:19

27:19

For this week's paper read, we actually dive into our own research. We wanted to create a replicable, evolving dataset that can keep pace with model training so that you always know you're testing with data your model has never seen before. We also saw the prohibitively high cost of running LLM evals at scale, and have used our data to fine-tune a series of SLMs that perform just as well as their base LLM counterparts, but at 1/10 the cost. So, over the past few weeks, the Arize team generated the largest public dataset of hallucinations, as well as a series of fine-tuned evaluation models. We talk about what we built, the process we took, and the bottom line results. 📃 Read the paper: https://arize.com/llm-hallucination-dataset/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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AI Benchmark Deep Dive: Gemini 2.5 and Humanity's Last Exam 26:11

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Model Context Protocol (MCP) 15:03

۸ weeks پیش15:03

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We cover Anthropic’s groundbreaking Model Context Protocol (MCP) . Though it was released in November 2024, we've been seeing a lot of hype around it lately, and thought it was well worth digging into. Learn how this open standard is revolutionizing AI by enabling seamless integration between LLMs and external data sources, fundamentally transforming them into capable, context-aware agents. We explore the key benefits of MCP, including enhanced context retention across interactions, improved interoperability for agentic workflows, and the development of more capable AI agents that can execute complex tasks in real-world environments. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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AI Roundup: DeepSeek’s Big Moves, Claude 3.7, and the Latest Breakthroughs 30:23

12 weeks پیش30:23

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This week, we're mixing things up a little bit. Instead of diving deep into a single research paper, we cover the biggest AI developments from the past few weeks. We break down key announcements, including: DeepSeek’s Big Launch Week: A look at FlashMLA (DeepSeek’s new approach to efficient inference) and DeepEP (their enhanced pretraining method). Claude 3.7 & Claude Code: What’s new with Anthropic’s latest model, and what Claude Code brings to the AI coding assistant space. Stay ahead of the curve with this fast-paced recap of the most important AI updates. We'll be back next time with our regularly scheduled programming. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

1
How DeepSeek is Pushing the Boundaries of AI Development 29:54

13 weeks پیش29:54

29:54

This week, we dive into DeepSeek. SallyAnn DeLucia, Product Manager at Arize, and Nick Luzio, a Solutions Engineer, break down key insights on a model that have dominating headlines for its significant breakthrough in inference speed over other models. What’s next for AI (and open source)? From training strategies to real-world performance, here’s what you need to know. Read a summary: https://arize.com/blog/how-deepseek-is-pushing-the-boundaries-of-ai-development/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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Multiagent Finetuning: A Conversation with Researcher Yilun Du 30:03

16 weeks پیش30:03

30:03

We talk to Google DeepMind Senior Research Scientist (and incoming Assistant Professor at Harvard), Yilun Du, about his latest paper "Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains." This paper introduces a multiagent finetuning framework that enhances the performance and diversity of language models by employing a society of agents with distinct roles, improving feedback mechanisms and overall output quality. The method enables autonomous self-improvement through iterative finetuning, achieving significant performance gains across various reasoning tasks. It's versatile, applicable to both open-source and proprietary LLMs, and can integrate with human-feedback-based methods like RLHF or DPO, paving the way for future advancements in language model development. Read an overview on the blog Watch the full discussion Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

1
Training Large Language Models to Reason in Continuous Latent Space 24:58

18 weeks پیش24:58

24:58

LLMs have typically been restricted to reason in the "language space," where chain-of-thought (CoT) is used to solve complex reasoning problems. But a new paper argues that language space may not always be the best for reasoning. In this paper read, we cover an exciting new technique from a team at Meta called Chain of Continuous Thought—also known as "Coconut." In the paper, "Training Large Language Models to Reason in a Continuous Latent Space" explores the potential of allowing LLMs to reason in an unrestricted latent space instead of being constrained by natural language tokens. Read a full breakdown of Coconut on our blog Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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LLMs as Judges: A Comprehensive Survey on LLM-Based Evaluation Methods 28:57

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We discuss a major survey of work and research on LLM-as-Judge from the last few years. "LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods" systematically examines the LLMs-as-Judge framework across five dimensions: functionality, methodology, applications, meta-evaluation, and limitations. This survey gives us a birds eye view of the advantages, limitations and methods for evaluating its effectiveness. Read a breakdown on our blog: https://arize.com/blog/llm-as-judge-survey-paper/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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Merge, Ensemble, and Cooperate! A Survey on Collaborative LLM Strategies 28:47

24 weeks پیش28:47

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LLMs have revolutionized natural language processing, showcasing remarkable versatility and capabilities. But individual LLMs often exhibit distinct strengths and weaknesses, influenced by differences in their training corpora. This diversity poses a challenge: how can we maximize the efficiency and utility of LLMs? A new paper, "Merge, Ensemble, and Cooperate: A Survey on Collaborative Strategies in the Era of Large Language Models," highlights collaborative strategies to address this challenge. In this week's episode, we summarize key insights from this paper and discuss practical implications of LLM collaboration strategies across three main approaches: merging, ensemble, and cooperation. We also review some new open source models we're excited about. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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Agent-as-a-Judge: Evaluate Agents with Agents 24:54

26 weeks پیش24:54

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This week, we break down the “Agent-as-a-Judge” framework—a new agent evaluation paradigm that’s kind of like getting robots to grade each other’s homework. Where typical evaluation methods focus solely on outcomes or demand extensive manual work, this approach uses agent systems to evaluate agent systems, offering intermediate feedback throughout the task-solving process. With the power to unlock scalable self-improvement, Agent-as-a-Judge could redefine how we measure and enhance agent performance. Let's get into it! Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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Introduction to OpenAI's Realtime API 29:56

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We break down OpenAI’s realtime API. Learn how to seamlessly integrate powerful language models into your applications for instant, context-aware responses that drive user engagement. Whether you’re building chatbots, dynamic content tools, or enhancing real-time collaboration, we walk through the API’s capabilities, potential use cases, and best practices for implementation. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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Swarm: OpenAI's Experimental Approach to Multi-Agent Systems 46:46

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As multi-agent systems grow in importance for fields ranging from customer support to autonomous decision-making, OpenAI has introduced Swarm, an experimental framework that simplifies the process of building and managing these systems. Swarm, a lightweight Python library, is designed for educational purposes, stripping away complex abstractions to reveal the foundational concepts of multi-agent architectures. In this podcast, we explore Swarm’s design, its practical applications, and how it stacks up against other frameworks. Whether you’re new to multi-agent systems or looking to deepen your understanding, Swarm offers a straightforward, hands-on way to get started. Read a Summary on the Blog Watch on YouTube Sign up for Upcoming Paper Readings Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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KV Cache Explained 4:19

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In this episode, we dive into the intriguing mechanics behind why chat experiences with models like GPT often start slow but then rapidly pick up speed. The key? The KV cache. This essential but under-discussed component enables the seamless and snappy interactions we expect from modern AI systems. Harrison Chu breaks down how the KV cache works, how it relates to the transformer architecture, and why it's crucial for efficient AI responses. By the end of the episode, you'll have a clearer understanding of how top AI products leverage this technology to deliver fast, high-quality user experiences. Tune in for a simplified explanation of attention heads, KQV matrices, and the computational complexities they present. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

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The Shrek Sampler: How Entropy-Based Sampling is Revolutionizing LLMs 3:31

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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 .…

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Google's NotebookLM and the Future of AI-Generated Audio 43:28

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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 .…

Deep Papers

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Exploring OpenAI's o1-preview and o1-mini 42:02

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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-mini Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

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Breaking Down Reflection Tuning: Enhancing LLM Performance with Self-Learning 26:54

35 weeks پیش26:54

26:54

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 .…

Deep Papers

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Composable Interventions for Language Models 42:35

36 weeks پیش42:35

42:35

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 .…

Deep Papers

1
Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges 39:05

40 weeks پیش39:05

39:05

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 .…

Deep Papers

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Breaking Down Meta's Llama 3 Herd of Models 44:40

42 weeks پیش44:40

44:40

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 .…

Deep Papers

1
DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines 33:57

43 weeks پیش33:57

33:57

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 .…

Deep Papers

1
RAFT: Adapting Language Model to Domain Specific RAG 44:01

47 weeks پیش44:01

44:01

Where adapting LLMs to specialized domains is essential (e.g., recent news, enterprise private documents), we discuss a paper that asks how we adapt pre-trained LLMs for RAG in specialized domains. SallyAnn DeLucia is joined by Sai Kolasani, researcher at UC Berkeley’s RISE Lab (and Arize AI Intern), to talk about his work on RAFT: Adapting Language Model to Domain Specific RAG. RAFT (Retrieval-Augmented FineTuning) is a training recipe that improves an LLM’s ability to answer questions in a “open-book” in-domain settings. Given a question, and a set of retrieved documents, the model is trained to ignore documents that don’t help in answering the question (aka distractor documents). This coupled with RAFT’s chain-of-thought-style response, helps improve the model’s ability to reason. In domain-specific RAG, RAFT consistently improves the model’s performance across PubMed, HotpotQA, and Gorilla datasets, presenting a post-training recipe to improve pre-trained LLMs to in-domain RAG. Read it on the blog: https://arize.com/blog/raft-adapting-language-model-to-domain-specific-rag/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

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LLM Interpretability and Sparse Autoencoders: Research from OpenAI and Anthropic 44:00

49 weeks پیش44:00

44:00

It’s been an exciting couple weeks for GenAI! Join us as we discuss the latest research from OpenAI and Anthropic. We’re excited to chat about this significant step forward in understanding how LLMs work and the implications it has for deeper understanding of the neural activity of language models. We take a closer look at some recent research from both OpenAI and Anthropic. These two recent papers both focus on the sparse autoencoder--an unsupervised approach for extracting interpretable features from an LLM. In "Extracting Concepts from GPT-4," OpenAI researchers propose using k-sparse autoencoders to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. In "Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet," researchers at Anthropic show that scaling laws can be used to guide the training of sparse autoencoders, among other findings. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

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Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models' Alignment 48:07

51 weeks پیش48:07

48:07

We break down the paper--Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models' Alignment. Ensuring alignment (aka: making models behave in accordance with human intentions) has become a critical task before deploying LLMs in real-world applications. However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations. To address this issue, this paper presents a comprehensive survey of key dimensions that are crucial to consider when assessing LLM trustworthiness. The survey covers seven major categories of LLM trustworthiness: reliability, safety, fairness, resistance to misuse, explainability and reasoning, adherence to social norms, and robustness. The measurement results indicate that, in general, more aligned models tend to perform better in terms of overall trustworthiness. However, the effectiveness of alignment varies across the different trustworthiness categories considered. By shedding light on these key dimensions of LLM trustworthiness, this paper aims to provide valuable insights and guidance to practitioners in the field. Understanding and addressing these concerns will be crucial in achieving reliable and ethically sound deployment of LLMs in various applications. Read more about Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models' Alignment Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

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Breaking Down EvalGen: Who Validates the Validators? 44:47

1 year پیش44:47

44:47

Due to the cumbersome nature of human evaluation and limitations of code-based evaluation, Large Language Models (LLMs) are increasingly being used to assist humans in evaluating LLM outputs. Yet LLM-generated evaluators often inherit the problems of the LLMs they evaluate, requiring further human validation. This week’s paper explores EvalGen, a mixed-initative approach to aligning LLM-generated evaluation functions with human preferences. EvalGen assists users in developing both criteria acceptable LLM outputs and developing functions to check these standards, ensuring evaluations reflect the users’ own grading standards. Read it on the blog: https://arize.com/blog/breaking-down-evalgen-who-validates-the-validators/ Paper: https://arxiv.org/abs/2404.12272 Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

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Keys To Understanding ReAct: Synergizing Reasoning and Acting in Language Models 45:07

1 year پیش45:07

45:07

This week we explore ReAct, an approach that enhances the reasoning and decision-making capabilities of LLMs by combining step-by-step reasoning with the ability to take actions and gather information from external sources in a unified framework. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

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Demystifying Chronos: Learning the Language of Time Series 44:40

1 year پیش44:40

44:40

This week, we’ve covering Amazon’s time series model: Chronos. Developing accurate machine-learning-based forecasting models has traditionally required substantial dataset-specific tuning and model customization. Chronos however, is built on a language model architecture and trained with billions of tokenized time series observations, enabling it to provide accurate zero-shot forecasts matching or exceeding purpose-built models. We dive into time series forecasting, some recent research our team has done, and take a community pulse on what people think of Chronos. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

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Anthropic Claude 3 43:01

1 year پیش43:01

43:01

This week we dive into the latest buzz in the AI world – the arrival of Claude 3. Claude 3 is the newest family of models in the LLM space, and Opus Claude 3 ( Anthropic's "most intelligent" Claude model ) challenges the likes of GPT-4. The Claude 3 family of models, according to Anthropic "sets new industry benchmarks," and includes "three state-of-the-art models in ascending order of capability: Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus." Each of these models "allows users to select the optimal balance of intelligence, speed, and cost." We explore Anthropic’s recent paper , and walk through Arize’s latest research comparing Claude 3 to GPT-4. This discussion is relevant to researchers, practitioners, or anyone who's curious about the future of AI. Find the full transcript and more here: https://arize.com/blog/anthropic-claude-3/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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Composable Interventions for Language Models