ICYMI: LangChain version 0.0.17 Refactored and improved documentation around prompts
PROMPT ENGINEERING
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LangChain 0.0.16: Chain Input/Output Comparison and Model Evaluation
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version 0.0.16: compare inputs/outputs on a whole chain Useful for evaluation when you're doing more than just a simple call to an LLM Github: https://
github.com/hwchase17/lang
chain
… How do different models do in @OfirPress
's self-ask w/ search example? https://
colab.research.google.com/drive/1atz4xfZ
LpIHJKD2kf38WnxHv61XU-dwb
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GPT as a General-Purpose Computer Reconfigurable via Natural Language Programs
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If previous neural nets are special-purpose computers designed for a specific task, GPT is a general-purpose computer, reconfigurable at run-time to run natural language programs. Programs are given in prompts (a kind of inception). GPT runs the program by completing the document
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Language Models Continue Sequences from Prompts, Not Maximize Rewards
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they don't maximize rewards, they are given a prompt (a kind of inception) and continue the sequence
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LLMs as Cognitive Engines Orchestrating Compute Infrastructure via Text
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Good post. A lot of interest atm in wiring up LLMs to a wider compute infrastructure via text I/O (e.g. calculator, python interpreter, google search, scratchpads, databases, …). The LLM becomes the "cognitive engine" orchestrating resources, its thought stack trace in raw text
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Galactica LLM Performance with XGBoost Model Implementation
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Amazed by the performance of Galactica by @paperswithcode
— SRK (@sudalairajkumar) 16 novembre 2022
Tried with prompt "jupyter notebook on how to use xgboost model" @tunguz might like it 🙂 #LLM #NLP #AI pic.twitter.com/7ohrREfiltAmazed by the performance of Galactica by @paperswithcode Tried with prompt "jupyter notebook on how to use xgboost model" @tunguz might like it 🙂 #LLM #NLP #AI
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Large Language Models Are Not Zero-Shot Communicators
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Large language models are not zero-shot communicators Ruis et al.: https://
arxiv.org/abs/2210.14986 #Artificialintelligence #DeepLearning #MachineLearning -
GPT Training Framework with Dataset and Sampling Tools
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Prompt: "You are a GPT and you're in charge of training an even better GPT, congrats! You have a dataset here . You can train it on document chunks like this: and sample its current understanding like this: . And here's a calculator and a scratchpad . Begin:"
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Understanding Code Generated by AI Assistants Matters
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“Be careful with copilot code generation assistance. We need people to understand their codes.” – Don Knuth, Turing Award winner (1974), luminary of @stanford CS dept, and a dear friend, at @StanfordHAI Fall Conf