EvolComplexity¶

Evolve instructions to make them more complex using an LLM.

EvolComplexity is a task that evolves instructions to make them more complex, and it is based in the EvolInstruct task, but using slight different prompts, but the exact same evolutionary approach.

Attributes¶

num_instructions: The number of instructions to be generated.
generate_answers: Whether to generate answers for the instructions or not. Defaults to False.
mutation_templates: The mutation templates to be used for the generation of the instructions.
min_length: Defines the length (in bytes) that the generated instruction needs to be higher than, to be considered valid. Defaults to 512.
max_length: Defines the length (in bytes) that the generated instruction needs to be lower than, to be considered valid. Defaults to 1024.
seed: The seed to be set for numpy in order to randomly pick a mutation method. Defaults to 42.

Runtime Parameters¶

min_length: Defines the length (in bytes) that the generated instruction needs to be higher than, to be considered valid.
max_length: Defines the length (in bytes) that the generated instruction needs to be lower than, to be considered valid.
seed: The number of evolutions to be run.

Input & Output Columns¶

graph TD
    subgraph Dataset
        subgraph Columns
            ICOL0[instruction]
        end
        subgraph New columns
            OCOL0[evolved_instruction]
            OCOL1[answer]
            OCOL2[model_name]
        end
    end

    subgraph EvolComplexity
        StepInput[Input Columns: instruction]
        StepOutput[Output Columns: evolved_instruction, answer, model_name]
    end

    ICOL0 --> StepInput
    StepOutput --> OCOL0
    StepOutput --> OCOL1
    StepOutput --> OCOL2
    StepInput --> StepOutput

Inputs¶

instruction (str): The instruction to evolve.

Outputs¶

evolved_instruction (str): The evolved instruction.
answer (str, optional): The answer to the instruction if generate_answers=True.
model_name (str): The name of the LLM used to evolve the instructions.