ChatGPT And InstructGPT: Aligning Language Models To Human Intention Deep Papers podcast

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Justin Manly, BCG’s lead on growth and innovation, explains how businesses can choose the right growth strategy given their aspirations and position in the market. Whether a company is gaining or losing share, in a booming or sluggish industry, profitable growth depends on knowing your starting point. Learn More: Justin Manly: https://on.bcg.com/4kGYyPH BCG’s Latest Thinking on Growth and Innovation Analytics: https://on.bcg.com/4kjTEIB BCG’s Latest Thinking on Corporate Finance and Strategy: https://on.bcg.com/3ZeAMlX Your Growth Strategy Depends on Your Starting Point: https://on.bcg.com/43YpzZm The Vitality Code: How Growth Leaders Master Strategy, Technology, People, and Culture: https://on.bcg.com/4dMKjH1 This podcast uses the following third-party services for analysis: Podtrac - https://analytics.podtrac.com/privacy-policy-gdrp…

Deep Papers «
ChatGPT and InstructGPT: Aligning Language Models to Human Intention

۲ سال پیش 47:39

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Deep Papers is a podcast series featuring deep dives on today’s seminal AI papers and research. Hosted by AI Pub creator Brian Burns and Arize AI founders Jason Lopatecki and Aparna Dhinakaran, each episode profiles the people and techniques behind cutting-edge breakthroughs in machine learning.
In this first episode, we’re joined by Long Ouyang and Ryan Lowe, research scientists at OpenAI and creators of InstructGPT. InstructGPT was one of the first major applications of Reinforcement Learning with Human Feedback to train large language models, and is the precursor to the now-famous ChatGPT. Listen to learn about the major ideas behind InstructGPT and the future of aligning language models to human intention.

Read OpenAI's InstructGPT paper here: https://openai.com/blog/instruction-following/

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

51 قسمت

#Science #Tech #Math #Business #Arize AI

ChatGPT and InstructGPT: Aligning Language Models to Human Intention

Deep Papers

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published ۲ سال پیش

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Read OpenAI's InstructGPT paper here: https://openai.com/blog/instruction-following/

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

51 قسمت

#Science #Tech #Math #Business #Arize AI

همه قسمت ها

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The Illusion of Thinking: What the Apple AI Paper Says About LLM Reasoning 30:35

۲ روز پیش30:35

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This week we discuss The Illusion of Thinking, a new paper from researchers at Apple that challenges today’s evaluation methods and introduces a new benchmark: synthetic puzzles with controllable complexity and clean logic. Their findings? Large Reasoning Models (LRMs) show surprising failure modes, including a complete collapse on high-complexity tasks and a decline in reasoning effort as problems get harder. Dylan and Parth dive into the paper's findings as well as the debate around it, including a response paper aptly titled "The Illusion of the Illusion of Thinking." Read the paper: The Illusion of Thinking Read the response: The Illusion of the Illusion of Thinking Explore more AI research and sign up for future 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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Accurate KV Cache Quantization with Outlier Tokens Tracing 25:11

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Scalable Chain of Thoughts via Elastic Reasoning 28:54

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

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

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

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

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

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

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

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

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Introduction to OpenAI's Realtime API 29:56

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

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KV Cache Explained 4:19

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

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

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Deep Papers

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

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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

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Deep Papers

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

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Deep Papers

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

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Deep Papers

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

48 weeks پیش33:57

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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

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

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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

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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

1 year 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

1
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

1
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 .…

Deep Papers

1
Reinforcement Learning in the Era of LLMs 44:49

1 year پیش44:49

44:49

We’re exploring Reinforcement Learning in the Era of LLMs this week with Claire Longo, Arize’s Head of Customer Success. Recent advancements in Large Language Models (LLMs) have garnered wide attention and led to successful products such as ChatGPT and GPT-4. Their proficiency in adhering to instructions and delivering harmless, helpful, and honest (3H) responses can largely be attributed to the technique of Reinforcement Learning from Human Feedback (RLHF). This week’s paper, aims to link the research in conventional RL to RL techniques used in LLM research and demystify this technique by discussing why, when, and how RL excels. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Sora: OpenAI’s Text-to-Video Generation Model 45:08

1 year پیش45:08

45:08

This week, we discuss the implications of Text-to-Video Generation and speculate as to the possibilities (and limitations) of this incredible technology with some hot takes. Dat Ngo, ML Solutions Engineer at Arize, is joined by community member and AI Engineer Vibhu Sapra to review OpenAI’s technical report on their Text-To-Video Generation Model: Sora. According to OpenAI, “Sora can generate videos up to a minute long while maintaining visual quality and adherence to the user’s prompt.” At the time of this recording, the model had not been widely released yet, but was becoming available to red teamers to assess risk, and also to artists to receive feedback on how Sora could be helpful for creatives. At the end of our discussion, we also explore EvalCrafter: Benchmarking and Evaluating Large Video Generation Models. This recent paper proposed a new framework and pipeline to exhaustively evaluate the performance of the generated videos, which we look at in light of Sora. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
RAG vs Fine-Tuning 39:49

1 year پیش39:49

39:49

This week, we’re discussing "RAG vs Fine-Tuning: Pipelines, Tradeoff, and a Case Study on Agriculture." This paper explores a pipeline for fine-tuning and RAG, and presents the tradeoffs of both for multiple popular LLMs, including Llama2-13B, GPT-3.5, and GPT-4. The authors propose a pipeline that consists of multiple stages, including extracting information from PDFs, generating questions and answers, using them for fine-tuning, and leveraging GPT-4 for evaluating the results. Overall, the results point to how systems built using LLMs can be adapted to respond and incorporate knowledge across a dimension that is critical for a specific industry, paving the way for further applications of LLMs in other industrial domains. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels 36:22

1 year پیش36:22

36:22

We discuss HyDE: a thrilling zero-shot learning technique that combines GPT-3’s language understanding with contrastive text encoders. HyDE revolutionizes information retrieval and grounding in real-world data by generating hypothetical documents from queries and retrieving similar real-world documents. It outperforms traditional unsupervised retrievers, rivaling fine-tuned retrievers across diverse tasks and languages. This leap in zero-shot learning efficiently retrieves relevant real-world information without task-specific fine-tuning, broadening AI model applicability and effectiveness. Link to transcript and live recording: https://arize.com/blog/hyde-paper-reading-and-discussion/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Phi-2 Model 44:29

1 year پیش44:29

44:29

We dive into Phi-2 and some of the major differences and use cases for a small language model (SLM) versus an LLM. With only 2.7 billion parameters, Phi-2 surpasses the performance of Mistral and Llama-2 models at 7B and 13B parameters on various aggregated benchmarks. Notably, it achieves better performance compared to 25x larger Llama-2-70B model on multi-step reasoning tasks, i.e., coding and math. Furthermore, Phi-2 matches or outperforms the recently-announced Google Gemini Nano 2, despite being smaller in size. Find the transcript and live recording: https://arize.com/blog/phi-2-model Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
A Deep Dive Into Generative's Newest Models: Gemini vs Mistral (Mixtral-8x7B)–Part I 47:50

1 year پیش47:50

47:50

For the last paper read of the year, Arize CPO & Co-Founder, Aparna Dhinakaran, is joined by a Dat Ngo (ML Solutions Architect) and Aman Khan (Product Manager) for an exploration of the new kids on the block: Gemini and Mixtral-8x7B. There's a lot to cover, so this week's paper read is Part I in a series about Mixtral and Gemini. In Part I, we provide some background and context for Mixtral 8x7B from Mistral AI, a high-quality sparse mixture of experts model (SMoE) that outperforms Llama 2 70B on most benchmarks with 6x faster inference Mixtral also matches or outperforms GPT3.5 on most benchmarks. This open-source model was optimized through supervised fine-tuning and direct preference optimization. Stay tuned for Part II in January, where we'll build on this conversation in and discuss Gemini-developed by teams at DeepMind and Google Research. Link to transcript and live recording: https://arize.com/blog/a-deep-dive-into-generatives-newest-models-mistral-mixtral-8x7b/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings 44:59

۲ years پیش44:59

44:59

We’re thrilled to be joined by Shuaichen Chang, LLM researcher and the author of this week’s paper to discuss his findings. Shuaichen’s research investigates the impact of prompt constructions on the performance of large language models (LLMs) in the text-to-SQL task, particularly focusing on zero-shot, single-domain, and cross-domain settings. Shuaichen and his team explore various strategies for prompt construction, evaluating the influence of database schema, content representation, and prompt length on LLMs’ effectiveness. The findings emphasize the importance of careful consideration in constructing prompts, highlighting the crucial role of table relationships and content, the effectiveness of in-domain demonstration examples, and the significance of prompt length in cross-domain scenarios. Read the blog and watch the discussion: https://arize.com/blog/how-to-prompt-llms-for-text-to-sql-paper-reading/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
The Geometry of Truth: Emergent Linear Structure in LLM Representation of True/False Datasets 41:02

۲ years پیش41:02

41:02

For this paper read, we’re joined by Samuel Marks, Postdoctoral Research Associate at Northeastern University, to discuss his paper, “The Geometry of Truth: Emergent Linear Structure in LLM Representation of True/False Datasets.” Samuel and his team curated high-quality datasets of true/false statements and used them to study in detail the structure of LLM representations of truth. Overall, they present evidence that language models linearly represent the truth or falsehood of factual statements and also introduce a novel technique, mass-mean probing, which generalizes better and is more causally implicated in model outputs than other probing techniques. Find the transcript and read more here: https://arize.com/blog/the-geometry-of-truth-emergent-linear-structure-in-llm-representation-of-true-false-datasets-paper-reading/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Towards Monosemanticity: Decomposing Language Models With Dictionary Learning 44:50

۲ years پیش44:50

44:50

In this paper read, we discuss “Towards Monosemanticity: Decomposing Language Models Into Understandable Components,” a paper from Anthropic that addresses the challenge of understanding the inner workings of neural networks, drawing parallels with the complexity of human brain function. It explores the concept of “features,” (patterns of neuron activations) providing a more interpretable way to dissect neural networks. By decomposing a layer of neurons into thousands of features, this approach uncovers hidden model properties that are not evident when examining individual neurons. These features are demonstrated to be more interpretable and consistent, offering the potential to steer model behavior and improve AI safety. Find the transcript and more here: https://arize.com/blog/decomposing-language-models-with-dictionary-learning-paper-reading/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models 43:49

۲ years پیش43:49

43:49

We discuss RankVicuna, the first fully open-source LLM capable of performing high-quality listwise reranking in a zero-shot setting. While researchers have successfully applied LLMs such as ChatGPT to reranking in an information retrieval context, such work has mostly been built on proprietary models hidden behind opaque API endpoints. This approach yields experimental results that are not reproducible and non-deterministic, threatening the veracity of outcomes that build on such shaky foundations. RankVicuna provides access to a fully open-source LLM and associated code infrastructure capable of performing high-quality reranking. Find the transcript and more here: https://arize.com/blog/rankvicuna-paper-reading/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Explaining Grokking Through Circuit Efficiency 36:12

۲ years پیش36:12

36:12

Join Arize Co-Founder & CEO Jason Lopatecki, and ML Solutions Engineer, Sally-Ann DeLucia, as they discuss “Explaining Grokking Through Circuit Efficiency." This paper explores novel predictions about grokking, providing significant evidence in favor of its explanation. Most strikingly, the research conducted in this paper demonstrates two novel and surprising behaviors: ungrokking, in which a network regresses from perfect to low test accuracy, and semi-grokking, in which a network shows delayed generalization to partial rather than perfect test accuracy. Find the transcript and more here: https://arize.com/blog/explaining-grokking-through-circuit-efficiency-paper-reading/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior 42:14

۲ years پیش42:14

42:14

Deep Papers is a podcast series featuring deep dives on today’s seminal AI papers and research. Each episode profiles the people and techniques behind cutting-edge breakthroughs in machine learning. In this episode, we discuss the paper, “Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior.” This episode is led by SallyAnn Delucia (ML Solutions Engineer, Arize AI), and Amber Roberts (ML Solutions Engineer, Arize AI). The research they discuss highlights that while LLMs have great generalization capabilities, they struggle to effectively predict and optimize communication to get the desired receiver behavior. We’ll explore whether this might be because of a lack of “behavior tokens” in LLM training corpora and how Large Content Behavior Models (LCBMs) might help to solve this issue. Find the transcript and more here: https://arize.com/blog/large-content-and-behavior-models-paper-reading/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Skeleton of Thought: LLMs Can Do Parallel Decoding 43:39

۲ years پیش43:39

43:39

Deep Papers is a podcast series featuring deep dives on today’s seminal AI papers and research. Each episode profiles the people and techniques behind cutting-edge breakthroughs in machine learning. In this paper reading, we explore the paper ‘Skeleton-of-Thought’ (SoT) approach, aimed at reducing large language model latency while enhancing answer quality. This episode is led by Aparna Dhinakaran ( Chief Product Officer, Arize AI) and Sally-Ann Delucia (ML Solutions Engineer, Arize AI), with two of the paper authors: Xuefei Ning , Postdoctoral Researcher at Tsinghua University and Zinan Lin , Senior Researcher, Microsoft Research. SoT’s innovative methodology guides LLMs to construct answer skeletons before parallel content elaboration, achieving impressive speed-ups of up to 2.39x across 11 models. Don’t miss the opportunity to delve into this human-inspired optimization strategy and its profound implications for efficient and high-quality language generation. Full transcript and more here: https://arize.com/blog/skeleton-of-thought-llms-can-do-parallel-decoding-paper-reading/ 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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Deep Papers « ChatGPT and InstructGPT: Aligning Language Models to Human Intention

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ChatGPT and InstructGPT: Aligning Language Models to Human Intention