I spoke to @latimes on #ChatGPT on how it can confidently give wrong but plausible answers to puzzles, and it is not getting online feedback on if the text it is generating is true.
LLMS
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Simple conversation buffer memory with dynamic N adjustment
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it is pretty simple at the moment – just keeps a buffer of the past N conversations in memory. a pretty simple improvement would be dynamically adjusting N based on the length of the conversations
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Improving ChatGPT’s Math Abilities with LangChain
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An awesome example improving ChatGPT's much maligned math abilities!
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Build ChatGPT-like Applications with Official APIs Now
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Tired of using an "unofficial" ChatGPT api? Just use this instead! More seriously: applications like ChatGPT are very possible for anyone to develop NOW. What will you build?
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LangChain Already Supports Conversation Summary Memory
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we actually support that already! https://
langchain.readthedocs.io/en/latest/gett
ing_started/memory.html#ConversationChain-with-ConversationSummaryMemory
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Dynamic conversation context length determination in LangChain
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excited to hear your thoughts/ideas! right now its pretty simple: just take the last N lines of conversation. but a very natural extension to determine N dynamically based on their length
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Guidance-Annealing Approach Combined with Normalization Techniques
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Have you tried the guidance-annealing approach too? It works well combined with this normalization.
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Discovering Latent Knowledge in Language Models Without Supervision
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Discovering Latent Knowledge in Language Models Without Supervision Burns et al.: https://
arxiv.org/abs/2212.03827 #ArtificialIntelligence #DeepLearning #MachineLearning -
Elixir Now Supports AI Models from GPT2 to Stable Diffusion
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welcomes @elixirlang ! Elixir now supports models on the Hub, from GPT2 to Stable Diffusion We're excited to see what the community builds with this! @josevalim
, the creator of Elixir, wrote this nice blog post