@0xjasper came up with a very cool example of LLMs fact checking themselves with prompt chaining. See the original tweet thread below
LLMS
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LangChain version 0.0.34: OpenAI moderation and fact checking
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LangChain version 0.0.34 Doc cleanup from `moozilla` (if on twitter lmk!) Support for @OpenAI's moderation endpoint LLMFactChecker chain from @thepromptking, original idea by @0xjasper Some more info on the LLM fact checker chain
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ChatGPT generates plausible but incorrect answers without online feedback
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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.
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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.