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"""Profile-based baseline.
Uses the LLM to generate a user writing style profile from K support examples,
then conditions generation on that profile summary.
"""
def build_profile_prompt(support_items, task):
"""Build prompt to generate a user writing style profile from support examples."""
parts = ["Analyze the following writing samples and describe the author's writing style "
"in 2-3 sentences. Focus on tone, vocabulary, sentence structure, and any "
"distinctive patterns.\n"]
for i, item in enumerate(support_items, 1):
parts.append(f"--- Sample {i} ---")
parts.append(item['support_output'][:500]) # truncate long samples
parts.append("")
parts.append("Writing style description:")
return "\n".join(parts)
def build_profile_conditioned_prompt(query_input, profile_summary, task):
"""Build generation prompt conditioned on the user profile."""
from data.templates import build_query_prompt
base_prompt = build_query_prompt(query_input, task)
return (
f"The following describes this user's writing style:\n"
f"{profile_summary}\n\n"
f"Write in this style.\n\n"
f"{base_prompt}"
)
def generate_profile(wrapper, support_items, task, max_profile_tokens=150):
"""Generate a user writing style profile using the LLM."""
import torch
prompt = build_profile_prompt(support_items, task)
chat_messages = [
{"role": "system", "content": "You are a writing style analyst."},
{"role": "user", "content": prompt},
]
prompt_text = wrapper.tokenizer.apply_chat_template(
chat_messages, tokenize=False, add_generation_prompt=True
)
input_ids = wrapper.tokenizer.encode(prompt_text, return_tensors="pt").to(wrapper.device)
with torch.no_grad():
outputs = wrapper.model.generate(
input_ids,
max_new_tokens=max_profile_tokens,
temperature=None, top_p=None, do_sample=False,
pad_token_id=wrapper.tokenizer.pad_token_id,
)
generated_ids = outputs[0, input_ids.shape[1]:]
return wrapper.tokenizer.decode(generated_ids, skip_special_tokens=True)
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