emerging-trajectories
LookupChatGPT
emerging-trajectories | LookupChatGPT | |
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6 | 6 | |
57 | 21 | |
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9.1 | 8.1 | |
14 days ago | 3 months ago | |
Python | JavaScript | |
MIT License | GNU General Public License v3.0 only |
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emerging-trajectories
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Large language models (e.g., ChatGPT) as research assistants
I think LLMs can do a lot more than people assume, but they need to be given the proper frameworks.
When was the last time a researcher, economist, etc. was given 10,000 papers and simply told "do some original work"? That's not how it works. Daniel (the author) provides some good examples where _streamlined_ work can happen, but again, this is pretty basic stuff.
To push this further, though, imagine LLMs that fill in frameworks... A few steps here: (1) do a lit review, (2) fill in the framework, (3) discuss what might be missing, and maybe even try and fill in the missing information.
I'm doing something like this with politics and economics (see: https://emergingtrajectories.com/) and it works generally well. I think with a ton more engineering, curating of knowledge bases, etc., one can get these LLMs to actually find some new "nuggets" of information.
Admittedly, it's very hard, but I think there's something there.
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Ask HN: Is RAG the Future of LLMs?
RAG will have a place in the LLM world, since it's a way to obtain data/facts/info for relevant queries.
Since you asked about alternatives...
(a) "World models" where LLMs structure information into code, structured data, etc. and query those models will likely be a thing. AlphaGeometry uses this[1], and people have tried to abstract this in different ways[2].
(b) Depending on how you define RAG, knowledge graphs could be a form of RAG or alternatively an alternative to them. Companies like Elemental Cognition[3] are building distinct alternatives to RAG that use such graphs and give LLMs the ability to run queries on said graphs. Another approach here is to build "fact databases" where, you structure observations about the world into standalone concepts/ideas/observations and reference those[4]. Again, similar to RAG but not quite RAG as we know it today.
[1] https://deepmind.google/discover/blog/alphageometry-an-olymp...
[2] https://arxiv.org/abs/2306.12672
[3] https://ec.ai/
[4] https://emergingtrajectories.com/
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Long-form factuality in large language models
For those interested in using search-augmented "reasoning", I implemented something similar in Emerging Trajectories[1], an open source package that forecasts geopolitical and economic events. We extract facts[2] from various websites (Google searches, news articles, RSS feeds) and have the LLM generate a hypothesis on a metric.
We're tracking the info forecasts to see how well this does for future events. For example, we're pitting the LLMs against each other to predict March 2024 CPI[3].
[1] https://emergingtrajectories.com/
[2] Sample code: https://github.com/wgryc/emerging-trajectories/blob/main/eme...
[3] https://emergingtrajectories.com/a/statement/28
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Ask HN: What are some actual use cases of AI Agents?
I'm working on research agents to help with economic, financial, and political research. These agents are open source (see: https://github.com/wgryc/emerging-trajectories).
The use cases are pretty straight forward and low risk:
1. Run a Google web search.
2. Query a news API.
3. Write a document based on the above, while citing sources.
Here's an example of something written yesterday, where I'm forecasting whether July 2024 will be the hottest on record: https://emergingtrajectories.com/a/forecast/74
This is working well in that the writeups are great and there are some "aha" moments, like the agent finding and referencing the The National Snow and Ice Data Center (NSIDC)... Very cool! I wouldn't have thought of it.
Then there's the part where the agent also tells me that the Oregon Department of Transportation has holidays during the summer, which doesn't matter at all.
So, YMMV, as they say... But I am more productive with these agents. I wouldn't publish anything formally without confirming and reviewing the content, though.
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Ask HN: What have you built with LLMs?
LLM agents to forecast geopolitical and economic events.
- Site: https://emergingtrajectories.com/
- GitHub repo: https://github.com/wgryc/emerging-trajectories
I've helped a number of companies build various sorts of LLM-powered apps (chatbots mainly) and found it interesting but not incredibly inspiring. The above is my attempt to build something no one else is working on.
It's been a lot of fun. Not sure if it'll be a "thing" ever, but I enjoy it.
LookupChatGPT
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Ask HN: What have you built with LLMs?
A chrome extension to ask about selected text with a right click. https://github.com/SMUsamaShah/LookupChatGPT
A chrome extension to show processed video overlay on YouTube to highlight motion.
A script to show stories going up and down on HN front page. This one just took 1 prompt.
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Chrome Experimental AI Features
I have a chrome extension (https://github.com/SMUsamaShah/LookupChatGPT) where i just added in-place text replacement option. Write something, select it, right click and select your own prompt to refine the text your way.
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Ask HN: What apps have you created for your own use?
https://github.com/SMUsamaShah/LookupChatGPT/ a chrome plugin to ask ChatGPT about selected text (fully customizable). Came out of need to understand random jargon.
- Quick Look up selected text via ChatGPT using your own prompt
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Ask HN: What are your most used Chat GPT prompts
I often use this on HN itself when I come across something I have no clue about. It gives me back enough info to understand the discussion.
[1]: https://github.com/SMUsamaShah/LookupChatGPT/
- Show HN: Chrome addon to lookup selected text via ChatGPT using custom prompts
What are some alternatives?
ChatGPT-AutoExpert - 🚀🧠💬 Supercharged Custom Instructions for ChatGPT (non-coding) and ChatGPT Advanced Data Analysis (coding).
gpt_jailbreak_status - This is a repository that aims to provide updates on the status of jailbreaking the OpenAI GPT language model.
RSS-Link-Database - Bookmarked archived links
pq - a command-line Protobuf parser with Kafka support and JSON output
aider - aider is AI pair programming in your terminal
data-analytics - Welcome to the Data-Analytics repository
CX_DB8 - a contextual, biasable, word-or-sentence-or-paragraph extractive summarizer powered by the latest in text embeddings (Bert, Universal Sentence Encoder, Flair)