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evaluate.py
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from transformers import AutoTokenizer, AutoModelForCausalLM
from tqdm import tqdm
import json
import os
import argparse
from string import Template
from langchain.llms import Anthropic
from dotenv import dotenv_values
from openai import OpenAI
import torch
import time
ANTHROPIC_API_KEY = dotenv_values(".env").get("ANTHROPIC_API_KEY", None)
OPENAI_API_KEY = dotenv_values(".env").get("OPENAI_API_KEY", None)
def read_jsonl(file_path):
data = []
with open(file_path, 'r', encoding='utf-8') as file:
for line in file:
entry = json.loads(line.strip(), strict=False)
data.append(entry)
return data
def write_jsonl(data_list, output_path):
with open(output_path, 'w', encoding='utf-8') as file:
for entry in data_list:
line = json.dumps(entry)
file.write(line + "\n")
parser = argparse.ArgumentParser()
parser.add_argument("--model")
parser.add_argument("--device", default="cpu")
parser.add_argument("--api_model", action="store_true")
parser.add_argument("--api_has_logprobs", action="store_true")
parser.add_argument("--cot", action="store_true")
parser.add_argument("--multi_gpu", action="store_true")
parser.add_argument("--half_precision", action="store_true")
parser.add_argument("--impossible_dataset", action="store_true")
args = parser.parse_args()
if args.impossible_dataset:
evaluation_results_dir = "impossible_evaluation_results"
data_list = read_jsonl("i_am_an_impossible_dataset.jsonl")
else:
evaluation_results_dir = "evaluation_results"
data_list = read_jsonl("i_am_a_strange_dataset.jsonl")
if not args.api_model:
tokenizer = AutoTokenizer.from_pretrained(args.model)
if args.multi_gpu:
if args.half_precision:
model = AutoModelForCausalLM.from_pretrained(args.model, device_map="auto", torch_dtype=torch.float16)
else:
model = AutoModelForCausalLM.from_pretrained(args.model, device_map="auto")
else:
if args.half_precision:
model = AutoModelForCausalLM.from_pretrained(args.model, torch_dtype=torch.float16).to(args.device)
else:
model = AutoModelForCausalLM.from_pretrained(args.model).to(args.device)
model.eval()
else:
if args.model == "claude-2":
if ANTHROPIC_API_KEY is None:
raise ValueError("You are trying to use claude-2, but your ANTHROPIC_API_KEY is not set in a .env file.")
kwargs_for_non_cot = {"max_tokens_to_sample": 2}
model = Anthropic(model="claude-2", anthropic_api_key=ANTHROPIC_API_KEY)
elif args.model == "gpt-4" or args.model == "gpt-4-1106-preview" or args.model == "gpt-3.5-turbo":
if OPENAI_API_KEY is None:
raise ValueError("You are trying to use gpt-4, but your OPENAI_API_KEY is not set in a .env file.")
os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
client = OpenAI()
kwargs_for_non_cot = {"max_tokens": 2, "top_logprobs": 5, "logprobs": True}
def model(message, max_tokens=None, logprobs=None, top_logprobs=None, temperature=0):
completion = client.chat.completions.create(
temperature=temperature,
logprobs=logprobs,
max_tokens=max_tokens,
top_logprobs=top_logprobs,
model=args.model,
messages=[
{"role": "user", "content": message}
],
)
if max_tokens==None:
return completion.choices[0].message.content
return completion
else:
raise ValueError(f"{args.model} is not a supported model for our evaluation yet when using the api_model option.")
# Compute generation results.
if not args.api_model:
generation_losses = []
for datum in tqdm(data_list):
true_statement = datum["beginning"] + datum["true_continuation"]
false_statement = datum["beginning"] + datum["false_continuation"]
true_input_ids = tokenizer(true_statement, return_tensors='pt', truncation=True).input_ids.to(args.device)
true_loss = model(input_ids=true_input_ids, labels=true_input_ids).loss.item()
false_input_ids = tokenizer(false_statement, return_tensors='pt', truncation=True).input_ids.to(args.device)
false_loss = model(input_ids=false_input_ids, labels=false_input_ids).loss.item()
non_self_referential = datum["non_self_referential_beginning"] is not None and not datum.get("used_in_non_self_referential_prompt", False)
if non_self_referential:
non_self_referential_true_statement = datum["non_self_referential_beginning"] + datum["true_continuation"] + "\n\n" + true_statement
non_self_referential_false_statement = datum["non_self_referential_beginning"] + datum["false_continuation"] + "\n\n" + false_statement
non_self_referential_true_input_ids = tokenizer(non_self_referential_true_statement, return_tensors='pt', truncation=True).input_ids.to(args.device)
non_self_referential_true_loss = model(input_ids=non_self_referential_true_input_ids, labels=non_self_referential_true_input_ids).loss.item()
non_self_referential_false_input_ids = tokenizer(non_self_referential_false_statement, return_tensors='pt', truncation=True).input_ids.to(args.device)
non_self_referential_false_loss = model(input_ids=non_self_referential_false_input_ids, labels=non_self_referential_false_input_ids).loss.item()
generation_losses.append({"id": datum["id"], "true": true_loss, "false": false_loss, "non_self_referential_true": non_self_referential_true_loss, "non_self_referential_false": non_self_referential_false_loss})
else:
generation_losses.append({"id": datum["id"], "true": true_loss, "false": false_loss})
write_jsonl(generation_losses, f"{evaluation_results_dir}/{args.model.split('/')[-1]}_generation_losses.jsonl")
print("Computed generation results")
# Compute validation results
prompt_data_list = read_jsonl("prompt_templates/prompt_data.jsonl")
few_shot_prompt_data_dict = {}
cot_prompt_data_dict = {}
for index in range(len(prompt_data_list)):
prompt_datum = prompt_data_list[index]
example_0 = prompt_datum["beginning"] + prompt_datum["true_continuation"]
example_1 = prompt_datum["beginning"] + prompt_datum["false_continuation"]
few_shot_prompt_data_dict[f"few_shot_prompt_example_{index}_0"] = example_0
few_shot_prompt_data_dict[f"few_shot_prompt_example_{index}_1"] = example_1
few_shot_prompt_data_dict[f"few_shot_prompt_example_{index}_0_answer"] = "True"
few_shot_prompt_data_dict[f"few_shot_prompt_example_{index}_1_answer"] = "False"
cot_prompt_data_dict[f"few_shot_prompt_example_{index}_0"] = example_0
cot_prompt_data_dict[f"few_shot_prompt_example_{index}_1"] = example_1
cot_prompt_data_dict[f"few_shot_prompt_example_{index}_0_answer"] = prompt_datum["cot_true_answer"]
cot_prompt_data_dict[f"few_shot_prompt_example_{index}_1_answer"] = prompt_datum["cot_false_answer"]
non_self_referential_prompt_data_list = read_jsonl("prompt_templates/prompt_data_non_self_referential.jsonl")
non_self_referential_few_shot_prompt_data_dict = {}
non_self_referential_cot_prompt_data_dict = {}
for index in range(len(non_self_referential_prompt_data_list)):
prompt_datum = non_self_referential_prompt_data_list[index]
example_0 = prompt_datum["beginning"] + prompt_datum["true_continuation"]
example_1 = prompt_datum["beginning"] + prompt_datum["false_continuation"]
non_self_referential_example_0 = prompt_datum["non_self_referential_beginning"] + prompt_datum["true_continuation"]
non_self_referential_example_1 = prompt_datum["non_self_referential_beginning"] + prompt_datum["false_continuation"]
non_self_referential_few_shot_prompt_data_dict[f"few_shot_prompt_example_{index}_0"] = non_self_referential_example_0 + "\n\n" + example_0
non_self_referential_few_shot_prompt_data_dict[f"few_shot_prompt_example_{index}_1"] = non_self_referential_example_1 + "\n\n" + example_1
non_self_referential_few_shot_prompt_data_dict[f"few_shot_prompt_example_{index}_0_answer"] = "True"
non_self_referential_few_shot_prompt_data_dict[f"few_shot_prompt_example_{index}_1_answer"] = "False"
non_self_referential_cot_prompt_data_dict[f"few_shot_prompt_example_{index}_0"] = non_self_referential_example_0 + "\n\n" + example_0
non_self_referential_cot_prompt_data_dict[f"few_shot_prompt_example_{index}_1"] = non_self_referential_example_1 + "\n\n" + example_1
non_self_referential_cot_prompt_data_dict[f"few_shot_prompt_example_{index}_0_answer"] = prompt_datum["cot_true_answer"]
non_self_referential_cot_prompt_data_dict[f"few_shot_prompt_example_{index}_1_answer"] = prompt_datum["cot_false_answer"]
cot_template = Template(open("prompt_templates/cot_prompt_template.txt").read())
non_self_referential_cot_template = Template(open("prompt_templates/cot_prompt_template_non_self_referential.txt").read())
few_shot_template = Template(open("prompt_templates/few_shot_prompt_template.txt").read())
non_self_referential_few_shot_template = Template(open("prompt_templates/few_shot_prompt_template_non_self_referential.txt").read())
zero_shot_template = Template(open("prompt_templates/zero_shot_prompt_template.txt").read())
non_self_referential_zero_shot_template = Template(open("prompt_templates/zero_shot_prompt_template_non_self_referential.txt").read())
validation_outputs = []
for datum in tqdm(data_list):
true_statement = datum["beginning"] + datum["true_continuation"]
false_statement = datum["beginning"] + datum["false_continuation"]
true_statement_cot = cot_template.substitute(**cot_prompt_data_dict, example=true_statement)
false_statement_cot = cot_template.substitute(**cot_prompt_data_dict, example=false_statement)
non_self_referential = datum["non_self_referential_beginning"] is not None and not datum.get("used_in_non_self_referential_prompt", False)
if non_self_referential:
non_self_referential_true_statement = datum["non_self_referential_beginning"] + datum["true_continuation"] + "\n\n" + true_statement
non_self_referential_false_statement = datum["non_self_referential_beginning"] + datum["false_continuation"] + "\n\n" + false_statement
non_self_referential_true_statement_cot = non_self_referential_cot_template.substitute(**non_self_referential_cot_prompt_data_dict, example=non_self_referential_true_statement)
non_self_referential_false_statement_cot = non_self_referential_cot_template.substitute(**non_self_referential_cot_prompt_data_dict, example=non_self_referential_false_statement)
if args.api_model:
def get_api_loss(text, generated):
loss = None
for obj in generated.choices[0].logprobs.content[0].top_logprobs:
if obj.token.lower() == text:
loss = -obj.logprob
break
return loss
generated_text_true_statement_cot = model(true_statement_cot, temperature=0)
generated_text_false_statement_cot = model(false_statement_cot, temperature=0)
time.sleep(1) # For rate limits.
validation_outputs.append({
"id": datum["id"],
"generated_text_true_statement_cot": generated_text_true_statement_cot,
"generated_text_false_statement_cot": generated_text_false_statement_cot,
})
if args.api_has_logprobs:
# Few shot prompt
true_statement_few_shot = few_shot_template.substitute(**few_shot_prompt_data_dict, example=true_statement, answer="")
false_statement_few_shot = few_shot_template.substitute(**few_shot_prompt_data_dict, example=false_statement, answer="")
# Zero shot prompt
true_statement_zero_shot = zero_shot_template.substitute(example=true_statement, answer="")
false_statement_zero_shot = zero_shot_template.substitute(example=false_statement, answer="")
generated_text_true_statement_zero_shot = model(true_statement_zero_shot, temperature=0, **kwargs_for_non_cot)
generated_text_false_statement_zero_shot = model(false_statement_zero_shot, temperature=0, **kwargs_for_non_cot)
generated_text_true_statement_few_shot = model(true_statement_few_shot, temperature=0, **kwargs_for_non_cot)
generated_text_false_statement_few_shot = model(false_statement_few_shot, temperature=0, **kwargs_for_non_cot)
validation_outputs[-1]["loss_true_statement_true_answer_few_shot"] = get_api_loss("true", generated_text_true_statement_few_shot)
validation_outputs[-1]["loss_true_statement_false_answer_few_shot"] = get_api_loss("false", generated_text_true_statement_few_shot)
validation_outputs[-1]["loss_false_statement_true_answer_few_shot"] = get_api_loss("true", generated_text_false_statement_few_shot)
validation_outputs[-1]["loss_false_statement_false_answer_few_shot"] = get_api_loss("false", generated_text_false_statement_few_shot)
validation_outputs[-1]["loss_true_statement_true_answer_zero_shot"] = get_api_loss("true", generated_text_true_statement_zero_shot)
validation_outputs[-1]["loss_true_statement_false_answer_zero_shot"] = get_api_loss("false", generated_text_true_statement_zero_shot)
validation_outputs[-1]["loss_false_statement_true_answer_zero_shot"] = get_api_loss("true", generated_text_false_statement_zero_shot)
validation_outputs[-1]["loss_false_statement_false_answer_zero_shot"] = get_api_loss("false", generated_text_false_statement_zero_shot)
if non_self_referential:
non_self_referential_generated_text_true_statement_cot = model(non_self_referential_true_statement_cot, temperature=0)
non_self_referential_generated_text_false_statement_cot = model(non_self_referential_false_statement_cot, temperature=0)
validation_outputs[-1]["non_self_referential_generated_text_true_statement_cot"] = non_self_referential_generated_text_true_statement_cot
validation_outputs[-1]["non_self_referential_generated_text_false_statement_cot"] = non_self_referential_generated_text_false_statement_cot
if args.api_has_logprobs:
# Few shot prompt
non_self_referential_true_statement_few_shot = non_self_referential_few_shot_template.substitute(**non_self_referential_few_shot_prompt_data_dict, example=non_self_referential_true_statement, answer="")
non_self_referential_false_statement_few_shot = non_self_referential_few_shot_template.substitute(**non_self_referential_few_shot_prompt_data_dict, example=non_self_referential_false_statement, answer="")
# Zero shot prompt
non_self_referential_true_statement_zero_shot = non_self_referential_zero_shot_template.substitute(example=non_self_referential_true_statement, answer="")
non_self_referential_false_statement_zero_shot = non_self_referential_zero_shot_template.substitute(example=non_self_referential_false_statement, answer="")
non_self_referential_generated_text_true_statement_zero_shot = model(non_self_referential_true_statement_zero_shot, temperature=0, **kwargs_for_non_cot)
non_self_referential_generated_text_false_statement_zero_shot = model(non_self_referential_false_statement_zero_shot, temperature=0, **kwargs_for_non_cot)
non_self_referential_generated_text_true_statement_few_shot = model(non_self_referential_true_statement_few_shot, temperature=0, **kwargs_for_non_cot)
non_self_referential_generated_text_false_statement_few_shot = model(non_self_referential_false_statement_few_shot, temperature=0, **kwargs_for_non_cot)
validation_outputs[-1]["non_self_referential_loss_true_statement_true_answer_few_shot"] = get_api_loss("true", non_self_referential_generated_text_true_statement_few_shot)
validation_outputs[-1]["non_self_referential_loss_true_statement_false_answer_few_shot"] = get_api_loss("false", non_self_referential_generated_text_true_statement_few_shot)
validation_outputs[-1]["non_self_referential_loss_false_statement_true_answer_few_shot"] = get_api_loss("true", non_self_referential_generated_text_false_statement_few_shot)
validation_outputs[-1]["non_self_referential_loss_false_statement_false_answer_few_shot"] = get_api_loss("false", non_self_referential_generated_text_false_statement_few_shot)
validation_outputs[-1]["non_self_referential_loss_true_statement_true_answer_zero_shot"] = get_api_loss("true", non_self_referential_generated_text_true_statement_zero_shot)
validation_outputs[-1]["non_self_referential_loss_true_statement_false_answer_zero_shot"] = get_api_loss("false", non_self_referential_generated_text_true_statement_zero_shot)
validation_outputs[-1]["non_self_referential_loss_false_statement_true_answer_zero_shot"] = get_api_loss("true", non_self_referential_generated_text_false_statement_zero_shot)
validation_outputs[-1]["non_self_referential_loss_false_statement_false_answer_zero_shot"] = get_api_loss("false", non_self_referential_generated_text_false_statement_zero_shot)
# Write the outputs separately here as we go, because APIs are expensive and if they go down, we still want something to show for it.
write_jsonl(validation_outputs, f"{args.model.split('/')[-1]}_validation_outputs.jsonl")
else:
# Few shot prompt
true_statement_true_answer_few_shot = few_shot_template.substitute(**few_shot_prompt_data_dict, example=true_statement, answer="True")
true_statement_false_answer_few_shot = few_shot_template.substitute(**few_shot_prompt_data_dict, example=true_statement, answer="False")
false_statement_true_answer_few_shot = few_shot_template.substitute(**few_shot_prompt_data_dict, example=false_statement, answer="True")
false_statement_false_answer_few_shot = few_shot_template.substitute(**few_shot_prompt_data_dict, example=false_statement, answer="False")
# Zero shot prompt
true_statement_true_answer_zero_shot = zero_shot_template.substitute(example=true_statement, answer="True")
true_statement_false_answer_zero_shot = zero_shot_template.substitute(example=true_statement, answer="False")
false_statement_true_answer_zero_shot = zero_shot_template.substitute(example=false_statement, answer="True")
false_statement_false_answer_zero_shot = zero_shot_template.substitute(example=false_statement, answer="False")
def run_zs_or_fs(true_statement_true_answer, true_statement_false_answer, false_statement_true_answer, false_statement_false_answer):
input_ids_true_statement_true_answer = tokenizer(true_statement_true_answer, return_tensors="pt", truncation=True).input_ids.to(args.device)
input_ids_true_statement_false_answer = tokenizer(true_statement_false_answer, return_tensors="pt", truncation=True).input_ids.to(args.device)
input_ids_false_statement_true_answer = tokenizer(false_statement_true_answer, return_tensors="pt", truncation=True).input_ids.to(args.device)
input_ids_false_statement_false_answer = tokenizer(false_statement_false_answer, return_tensors="pt", truncation=True).input_ids.to(args.device)
loss_true_statement_true_answer = model(input_ids=input_ids_true_statement_true_answer, labels=input_ids_true_statement_true_answer).loss.item()
loss_true_statement_false_answer = model(input_ids=input_ids_true_statement_false_answer, labels=input_ids_true_statement_false_answer).loss.item()
loss_false_statement_true_answer = model(input_ids=input_ids_false_statement_true_answer, labels=input_ids_false_statement_true_answer).loss.item()
loss_false_statement_false_answer = model(input_ids=input_ids_false_statement_false_answer, labels=input_ids_false_statement_false_answer).loss.item()
return loss_true_statement_true_answer, loss_true_statement_false_answer, loss_false_statement_true_answer, loss_false_statement_false_answer
loss_true_statement_true_answer_zero_shot, loss_true_statement_false_answer_zero_shot, loss_false_statement_true_answer_zero_shot, loss_false_statement_false_answer_zero_shot = run_zs_or_fs(true_statement_true_answer_zero_shot, true_statement_false_answer_zero_shot, false_statement_true_answer_zero_shot, false_statement_false_answer_zero_shot)
loss_true_statement_true_answer_few_shot, loss_true_statement_false_answer_few_shot, loss_false_statement_true_answer_few_shot, loss_false_statement_false_answer_few_shot = run_zs_or_fs(true_statement_true_answer_few_shot, true_statement_false_answer_few_shot, false_statement_true_answer_few_shot, false_statement_false_answer_few_shot)
validation_outputs.append({
"id": datum["id"],
"loss_true_statement_true_answer_few_shot": loss_true_statement_true_answer_few_shot,
"loss_true_statement_false_answer_few_shot": loss_true_statement_false_answer_few_shot,
"loss_false_statement_true_answer_few_shot": loss_false_statement_true_answer_few_shot,
"loss_false_statement_false_answer_few_shot": loss_false_statement_false_answer_few_shot,
"loss_true_statement_true_answer_zero_shot": loss_true_statement_true_answer_zero_shot,
"loss_true_statement_false_answer_zero_shot": loss_true_statement_false_answer_zero_shot,
"loss_false_statement_true_answer_zero_shot": loss_false_statement_true_answer_zero_shot,
"loss_false_statement_false_answer_zero_shot": loss_false_statement_false_answer_zero_shot,
})
if non_self_referential:
# Few shot prompt
non_self_referential_true_statement_true_answer_few_shot = non_self_referential_few_shot_template.substitute(**non_self_referential_few_shot_prompt_data_dict, example=non_self_referential_true_statement, answer="True")
non_self_referential_true_statement_false_answer_few_shot = non_self_referential_few_shot_template.substitute(**non_self_referential_few_shot_prompt_data_dict, example=non_self_referential_true_statement, answer="False")
non_self_referential_false_statement_true_answer_few_shot = non_self_referential_few_shot_template.substitute(**non_self_referential_few_shot_prompt_data_dict, example=non_self_referential_false_statement, answer="True")
non_self_referential_false_statement_false_answer_few_shot = non_self_referential_few_shot_template.substitute(**non_self_referential_few_shot_prompt_data_dict, example=non_self_referential_false_statement, answer="False")
# Zero shot prompt
non_self_referential_true_statement_true_answer_zero_shot = non_self_referential_zero_shot_template.substitute(example=non_self_referential_true_statement, answer="True")
non_self_referential_true_statement_false_answer_zero_shot = non_self_referential_zero_shot_template.substitute(example=non_self_referential_true_statement, answer="False")
non_self_referential_false_statement_true_answer_zero_shot = non_self_referential_zero_shot_template.substitute(example=non_self_referential_false_statement, answer="True")
non_self_referential_false_statement_false_answer_zero_shot = non_self_referential_zero_shot_template.substitute(example=non_self_referential_false_statement, answer="False")
non_self_referential_loss_true_statement_true_answer_zero_shot, non_self_referential_loss_true_statement_false_answer_zero_shot, non_self_referential_loss_false_statement_true_answer_zero_shot, non_self_referential_loss_false_statement_false_answer_zero_shot = run_zs_or_fs(non_self_referential_true_statement_true_answer_zero_shot, non_self_referential_true_statement_false_answer_zero_shot, non_self_referential_false_statement_true_answer_zero_shot, non_self_referential_false_statement_false_answer_zero_shot)
non_self_referential_loss_true_statement_true_answer_few_shot, non_self_referential_loss_true_statement_false_answer_few_shot, non_self_referential_loss_false_statement_true_answer_few_shot, non_self_referential_loss_false_statement_false_answer_few_shot = run_zs_or_fs(non_self_referential_true_statement_true_answer_few_shot, non_self_referential_true_statement_false_answer_few_shot, non_self_referential_false_statement_true_answer_few_shot, non_self_referential_false_statement_false_answer_few_shot)
validation_outputs[-1]["non_self_referential_loss_true_statement_true_answer_zero_shot"] = non_self_referential_loss_true_statement_true_answer_zero_shot
validation_outputs[-1]["non_self_referential_loss_true_statement_false_answer_zero_shot"] = non_self_referential_loss_true_statement_false_answer_zero_shot
validation_outputs[-1]["non_self_referential_loss_false_statement_true_answer_zero_shot"] = non_self_referential_loss_false_statement_true_answer_zero_shot
validation_outputs[-1]["non_self_referential_loss_false_statement_false_answer_zero_shot"] = non_self_referential_loss_false_statement_false_answer_zero_shot
validation_outputs[-1]["non_self_referential_loss_true_statement_true_answer_few_shot"] = non_self_referential_loss_true_statement_true_answer_few_shot
validation_outputs[-1]["non_self_referential_loss_true_statement_false_answer_few_shot"] = non_self_referential_loss_true_statement_false_answer_few_shot
validation_outputs[-1]["non_self_referential_loss_false_statement_true_answer_few_shot"] = non_self_referential_loss_false_statement_true_answer_few_shot
validation_outputs[-1]["non_self_referential_loss_false_statement_false_answer_few_shot"] = non_self_referential_loss_false_statement_false_answer_few_shot
# COT prompt
if args.cot:
def run_cot(true_statement_cot, false_statement_cot):
input_ids_true_statement_cot = tokenizer(true_statement_cot, return_tensors="pt", truncation=True).input_ids.to(args.device)
input_ids_false_statement_cot = tokenizer(false_statement_cot, return_tensors="pt", truncation=True).input_ids.to(args.device)
max_new_tokens = min(200, max(0, model.config.max_position_embeddings - input_ids_true_statement_cot.shape[-1]), max(0, model.config.max_position_embeddings - input_ids_false_statement_cot.shape[-1])) # Maximum length of the generated tokens
if max_new_tokens == 0:
return "", ""
prompt_length_true_statement_cot = len(input_ids_true_statement_cot[0])
generated_ids_true_statement_cot = model.generate(input_ids_true_statement_cot, max_new_tokens=max_new_tokens, num_return_sequences=1, num_beams=1, do_sample=False, pad_token_id=tokenizer.eos_token_id, temperature=0.0)
generated_text_true_statement_cot = tokenizer.decode(generated_ids_true_statement_cot[0][prompt_length_true_statement_cot:], skip_special_tokens=True)
prompt_length_false_statement_cot = len(input_ids_false_statement_cot[0])
generated_ids_false_statement_cot = model.generate(input_ids_false_statement_cot, max_new_tokens=max_new_tokens, num_return_sequences=1, num_beams=1, do_sample=False, pad_token_id=tokenizer.eos_token_id, temperature=0.0)
generated_text_false_statement_cot = tokenizer.decode(generated_ids_false_statement_cot[0][prompt_length_false_statement_cot:], skip_special_tokens=True)
return generated_text_true_statement_cot, generated_text_false_statement_cot
generated_text_true_statement_cot, generated_text_false_statement_cot = run_cot(true_statement_cot, false_statement_cot)
validation_outputs[-1]["generated_text_true_statement_cot"] = generated_text_true_statement_cot
validation_outputs[-1]["generated_text_false_statement_cot"] = generated_text_false_statement_cot
if non_self_referential:
non_self_referential_generated_text_true_statement_cot, non_self_referential_generated_text_false_statement_cot = run_cot(non_self_referential_true_statement_cot, non_self_referential_false_statement_cot)
validation_outputs[-1]["non_self_referential_generated_text_true_statement_cot"] = non_self_referential_generated_text_true_statement_cot
validation_outputs[-1]["non_self_referential_generated_text_false_statement_cot"] = non_self_referential_generated_text_false_statement_cot
write_jsonl(validation_outputs, f"{evaluation_results_dir}/{args.model.split('/')[-1]}_validation_outputs.jsonl")
print("Computed validation results")