Python Function for a Basic Gen Model | Dataford Interview Questions

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JPMorganChase is evaluating lightweight text-generation components for internal AI assistants. Implement a token-level generative model without external machine learning libraries.

Given a training corpus, build an interpolated n-gram model that predicts the next token from the longest available context. Apply additive smoothing so every vocabulary token remains possible, back off to shorter contexts when a context was not observed, and generate a reproducible continuation using a seeded random number generator.

Formal Specification

Implement generate_text(corpus, prompt, order, max_tokens, temperature, seed).

corpus and prompt are non-empty whitespace-separated strings.
order is the maximum context length.
max_tokens is the number of tokens to generate, excluding the prompt.
temperature controls randomness. A value of 0 selects the highest-probability token; positive values sample from temperature-adjusted probabilities.
seed initializes Python's pseudorandom generator.
Return a string containing exactly the generated tokens separated by single spaces. Return an empty string when max_tokens is zero.

Use additive smoothing with a fixed alpha of 0.1. If several tokens have the same highest probability at temperature 0, choose the lexicographically smallest token.

Constraints

1 <= len(corpus.split()) <= 10^4
0 <= max_tokens <= 100
1 <= order <= 5
0 <= temperature <= 2
seed is an integer
The vocabulary contains at most 2,000 distinct tokens

Function Signature

def generate_text(corpus, prompt, order, max_tokens, temperature, seed):