"""Perturbation step. The first ingredient in our approach is to randomly perturb prompts passed as input to

the LLM. Given an alphabet A, we consider three kinds of perturbations:

• Insert: Randomly sample q% of the characters in P, and after each of these characters, insert a new

character sampled uniformly from A.

• Swap: Randomly sample q% of the characters in P, and then swap the characters at those locations by

sampling new characters uniformly from A.

• Patch: Randomly sample d consecutive characters in P, where d equals q% of the characters in P, and

then replace these characters with new characters sampled uniformly from A"""

import random import string

def random_insert_updated(text, insert_pct): """Randomly insert new chars into text after selected characters.""" num_inserts = int(len(text) * insert_pct) indices = random.sample(range(len(text)), num_inserts) for idx in sorted(indices, reverse=True): new_char = random.choice(string.printable) text = text[:idx + 1] + new_char + text[idx + 1:] return text

def random_swap_updated(text, swap_pct): """Randomly swap chars within the text with new characters.""" num_swaps = int(len(text) * swap_pct) indices = random.sample(range(len(text)), num_swaps) for i in indices: new_char = random.choice(string.printable) text = text[:i] + new_char + text[i+1:] return text

def random_patch(text, patch_pct): """Replace a random contiguous patch.""" patch_len = int(len(text) * patch_pct) start_idx = random.randint(0, len(text)-patch_len) patch_str = ''.join(random.choice(string.printable) for _ in range(patch_len)) text = text[:start_idx] + patch_str + text[start_idx+patch_len:] return text

def adaptive_perturb_pct(text, base_pct, min_len=10, max_len=100): """Adapt perturbation percentage based on text length.""" text_len = len(text) if text_len <= min_len: return base_pct / 2 elif text_len >= max_len: return base_pct * 2 else: return base_pct

def smooth(prompts, perturb_pct=0.1, n=10): smoothed = [] for prompt in prompts: perturbed = [prompt] for _ in range(n - 1): func = random.choice([random_insert_updated, random_swap_updated, random_patch]) adaptive_pct = adaptive_perturb_pct(prompt, perturb_pct) perturbed.append(func(prompt, adaptive_pct)) smoothed.append(perturbed) return smoothed

better examples should be used here

sample_prompts = [ "Hi!",
"How's it going?", "It's a pleasure to make your acquaintance.", "Lorem ipsum dolor sit amet, consectetur adipiscing elit." ]

result = smooth(sample_prompts, base_perturb_pct=0.1, n=5) print(result)