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I spent some time toying around with LLM-guided "symbolic regression", basically having an LLM review documents in order to come up with primitives (aka operato

I 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.

futurisold 1y ago
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.") ---

futurisold 1y ago

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.") ---