Earlier quoted context omitted.
Why is carrot the vegetablefication of apple?
Also if you run it twice, is it gonna be a carrot again?
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SymbolicAI: A neuro-symbolic perspective on LLMs
31–40 of 66 posts
Re: SymbolicAI: A neuro-symbolic perspective on LLMs
#32This is the voodoo that excites me. Examples I found interesting: Semantic map lambdas S = Symbol(['apple', 'banana', 'cherry', 'cat', 'dog']) print(S.map('convert all fruits to vegetables')) # => ['carrot', 'broccoli', 'spinach', 'cat', 'dog'] comparison parameterized by context # Contextual greeting comparison greeting = Symbol('Hello, good morning!') similar_greeting = 'Hi there, good day!' # Compare with specific…
Why is carrot the vegetablefication of apple?
Are you asking for the root cause?
Re: SymbolicAI: A neuro-symbolic perspective on LLMs
#33FYI, there’s a correctness issue in the part about correctness contracts: valid_opts = ['A', 'B', 'C']
if v not in valid_sizes:
valid_sizes is undefined
Re: SymbolicAI: A neuro-symbolic perspective on LLMs
#34This is the voodoo that excites me. Examples I found interesting: Semantic map lambdas S = Symbol(['apple', 'banana', 'cherry', 'cat', 'dog']) print(S.map('convert all fruits to vegetables')) # => ['carrot', 'broccoli', 'spinach', 'cat', 'dog'] comparison parameterized by context # Contextual greeting comparison greeting = Symbol('Hello, good morning!') similar_greeting = 'Hi there, good day!' # Compare with specific…
Why is carrot the vegetablefication of apple?
I think it's interpreting the command as "replace each fruit with a vegetable", and it might intuit "make the resulting vegetables unique from one another" but otherwise it's not trying to find the "most similar" vegetable to every fruit or anything like that.
Re: SymbolicAI: A neuro-symbolic perspective on LLMs
#35I spent some time toying around with LLM-guided "symbolic regression", basically having an LLM review documents in order to come up with primitives (aka operators) that could be fed into github.com/MilesCranmer/PySR
I didn't get very far because I had difficulty piping it all together, but with something like this I might give it another go. Cool stuff.
Re: SymbolicAI: A neuro-symbolic perspective on LLMs
#36what are the implicaitons and actual real world application of this? better agents? more accurate, debuggable LLM answers?
Re: SymbolicAI: A neuro-symbolic perspective on LLMs
#37One question, OP, how does cost for this work? Do you pay the LLM inference cost (quite literally if using an external API) every time you run a line that involves natural language computation? E.g. what happens if you call a "symbolic" function in a loop.
Re: SymbolicAI: A neuro-symbolic perspective on LLMs
#38This is the voodoo that excites me. Examples I found interesting: Semantic map lambdas S = Symbol(['apple', 'banana', 'cherry', 'cat', 'dog']) print(S.map('convert all fruits to vegetables')) # => ['carrot', 'broccoli', 'spinach', 'cat', 'dog'] comparison parameterized by context # Contextual greeting comparison greeting = Symbol('Hello, good morning!') similar_greeting = 'Hi there, good day!' # Compare with specific…
You might enjoy Lotus: https://github.com/lotus-data/lotus
It takes all the core relational operators and makes an easy semantic version of each as a python dataframe library extension . Each call ends up being a 'model' point in case you also want to do fancier things later like more learning based approaches. Afaict, snowflake and friends are moving in this direction for their cloud SQLs as well.
We ended up doing something similar for louie.ai , where you use AI notebooks/dashboards/APIs (ex: MCP) to talk to your data (splunk, databricks, graph db, whatever), and it'll figure out symbolic + semantic operators based on the context. Super helpful in practice.
My 80% case here is:
- semantic map: "get all the alerts from splunk index xyz, add a column flagging anything suspicious and another explaining why" - semantic map => semantic reduce: "... then summarize what you found" <--- then tells you about it in natural text
Re: SymbolicAI: A neuro-symbolic perspective on LLMs
#39Since code is generated by LLM these days, how does specific syntactic constructs like a Symbol which essentially carries the context and can be manipulated with python operators help when compared to a normal python code generated by LLM with all the checks and balances instructed by a human?
For example, I can write in this syntax to convert all fruits to vegetables or I can simply prompt an LLM to construct a program that takes a list of fruits and calls a LLM in the background to return the vegetables equivalent.
I am trying to understand the difference.
Re: SymbolicAI: A neuro-symbolic perspective on LLMs
#40Since code is generated by LLM these days, how does specific syntactic constructs like a Symbol which essentially carries the context and can be manipulated with python operators help when compared to a normal python code generated by LLM with all the checks and balances instructed by a human? For example, I can write in this syntax to convert all fruits to vegetables or I can simply prompt an LLM to construct a prog…
Hallucination obstruction, I'd imagine. When you have an LLM create a formal system, it can be verified way easier than a general purpose one