SymbolicAI: A neuro-symbolic perspective on LLMs

Anyone interested in this from a history / semiotics / language-theory perspective should look into the triad concepts of:

Sign (Signum) - The thing which points Locus - The thing being pointed to Sense (Sensus) - The effect/sense in the interpreter

Also known by: Representation/Object/Interpretation, Symbol/Referent/Thought, Signal/Data/User, Symbol/State/Update. Same pattern has been independently identified many many times through history, always ending up with the triplet, renamed many many times.

What you're describing above is the "Locus" essential object being pointed to, fulfilled by different contracts/LLMs/systems but the same essential thing always being eluded to. There's an elegant stability to it from a systems design pov. It makes strong sense to build around those as the indexes/keys being pointed towards, and then various implementations (Signs) attempting to achieve them. I'm building a similar system atm.

I'd appreciate it! It's cool and I wish you success. Just hope that when someone says "We're using Symbolic AI" a year from now, it won't be even more ambiguous than today :D

Oh, definitely. I recommend you go for contracts. I've used something similar for a contract that iteratively "stitched together" a broken ontology graph. Here's some of the data models for inspiration -- you could have something similar for your ops, and write the contract to solve for one op, then apply the op, etc.

---

    class Merge(LLMDataModel):
        indexes: list[int] = Field(description="The indices of the clusters that are being merged.")
        relations: list[SubClassRelation] = Field(
            description="A list of superclass-subclass relations chosen from the existing two clusters in such a way that they merge."
        )
    
        @field_validator("indexes")
        @classmethod
        def is_binary(cls, v):
            if len(v) != 2:
                raise ValueError(
                    f"Binary op error: Invalid number of clusters: {len(v)}. The merge operation requires exactly two clusters."
                )
            return v
    
    
    class Bridge(LLMDataModel):
        indexes: list[int] = Field(description="The indices of the clusters that are being bridged.")
        relations: list[SubClassRelation] = Field(
            description="A list of new superclass-subclass relations used to bridge the two clusters from the ontology."
        )
    
        @field_validator("indexes")
        @classmethod
        def is_binary(cls, v):
            if len(v) != 2:
                raise ValueError(
                    f"Binary op error: Invalid number of clusters: {len(v)}. The merge operation requires exactly two clusters."
                )
            return v
    
    
    class Prune(LLMDataModel):
        indexes: list[int] = Field(description="The indices of the clusters that are being pruned.")
        classes: list[str] = Field(description="A list of classes that are being pruned from the ontology.")
    
        @field_validator("indexes")
        @classmethod
        def is_unary(cls, v):
            if len(v) > 1:
                raise ValueError(
                    f"Unary op error: Invalid number of clusters: {len(v)}. The prune operation requires exactly one cluster."
                )
            return v
    
    
    class Operation(LLMDataModel):
        type: Merge | Bridge | Prune = Field(description="The type of operation to perform.")

---

Since these seem like short prompts, you can send as context data that was correct on past prompts

You can create a test suite for your code that will compile correct results according to another prompt or dictionary verification

  t.test(
     Symbol(['apple', 'banana', 'cherry', 'cat', 'dog']).map('convert all fruits to vegetables'),
     "list only has vegetable and cat,dog"
  )

Thank you to everyone for participating in the discussion and for your overall support! As I said, I didn't expect this. I'm always just an email or tweet away, so you know how to reach me. It was great talking to you all!