| from ...smp import * |
| from ...utils import can_infer |
|
|
|
|
| FAIL_MSG = 'Failed to obtain answer via API.' |
|
|
|
|
| def get_gpt4_extract_ICE(): |
| example_1 = """ |
| 1. |
| Model response: 'Rounded to two decimal places, the perimeter of the sector is approximately:\n\n(-2, 1)' |
| Extracted Answer: (-2, 1) |
| """ |
|
|
| example_2 = """ |
| 2. |
| Model response: 'at those points.\n\nTherefore, the correct option that represents the meaning of the intersection points of the graphs is:\n\nD. They give the solutions to the equation $f(t)=g(t)$.",' |
| Extracted Answer: D |
| """ |
|
|
| example_3 = """ |
| 3. |
| Model response: ' at 1 (there's a closed circle at y = 1), the range in interval notation is \\((-4, 1]\\).\n\nFinal values:\nDomain: \\((-3, 3]\\)\nRange: \\((-4, 1]\\)' |
| Extracted Answer: Domain: \\((-3, 3]\\)\nRange: \\((-4, 1]\\) |
| """ |
|
|
| example_4 = """ |
| 4. |
| Model response: 'As it stands, I cannot provide the correct option letter because there isn't enough information to solve for 'y'.' |
| Extracted Answer: null |
| """ |
|
|
| example_5 = """ |
| 5. |
| Model response: 'Given that AB = 17.6 meters, we can now substitute into the equation:\n\nd = 17.6 / cos(38\u00b0)\n\nTherefore, to one decimal place, the distance d between Ned and Bart is approximately 22.3 meters.' |
| Extracted answer: 22.3 |
| """ |
|
|
| example_6 = """ |
| 6. |
| Model response: have all the coefficients for the quadratic function:\n\\( f(x) = ax^2 + bx + c \\)\n\\( f(x) = -1x^2 - 2x + 1 \\)\n\nTherefore, the equation for the graphed function \\( f \\) is:\n\\( f(x) = -x^2 - 2x + 1 \\)"' |
| Extracted answer: f(x) = -x^2 - 2x + 1 |
| """ |
|
|
| return [example_1, example_2, example_3, example_4, example_5, example_6] |
|
|
|
|
| def get_gpt4_score_ICE(): |
| example_1 = """ |
| [Question]: Write the set of numbers represented on the number line in interval notation. |
| [Standard Answer]: (-2,1] |
| [Model_answer] : Extracted Answer: \\((-2, 1)\\) |
| Judgement: 0 |
| """ |
|
|
| example_2 = """ |
| [Question]: As shown in the figure, circle O has a radius 1.0, if angle BAC = 60.0, then the length of BC is ()\nChoices:\nA:2\nB:2\u221a{{3}}\nC:\u221a{{3}}\nD:2\u221a{{2}} |
| [Standard Answer]: C |
| [Model_answer] : B:2\u221a{{3}} |
| Judgement: 0 |
| """ |
|
|
| example_3 = """ |
| [Question]: Find the domain and range of the function f using interval notation. |
| [Standard Answer]: domain: [-4, 0) and range: (-3, 1] |
| [Model_answer] : Range: \\((-4, 1]\\) |
| Judgement: 0 |
| """ |
|
|
| example_4 = """ |
| [Question]: As shown in the figure, circle O has a radius 1.0, if angle BAC = 60.0, then the length of BC is ()\nChoices:\nA:2\nB:2\u221a{{3}}\nC:\u221a{{3}}\nD:2\u221a{{2}} |
| [Standard Answer]: C |
| [Model_answer] : null |
| Judgement: 0 |
| """ |
|
|
| return [example_1, example_2, example_3, example_4] |
|
|
|
|
| def build_mathverse_gpt4_extract_prompt(line): |
| task_description = """ |
| I am providing you a response from a model to a math problem, termed 'Model Response'. You should extract the answer from the response as 'Extracted Answer'. Directly output the extracted answer with no explanation.\n\n |
| """ |
| prediction = str(line['prediction']) |
| demo_prompt = task_description |
| examples = get_gpt4_extract_ICE() |
| for example in examples: |
| demo_prompt += example + '\n\n' |
| test_prompt = f"Model response: '{prediction}'\nExtracted Answer: " |
| full_prompt = f'{demo_prompt}7.\n{test_prompt}' |
|
|
| return full_prompt |
|
|
|
|
| def build_mathverse_gpt4_score_prompt(line): |
| task_description = """ |
| Below are two answers to a math question. Question is [Question], [Standard Answer] is the standard answer to the question, and [Model_answer] is the answer extracted from a model's output to this question. Determine whether these two answers are consistent. |
| Please note that only when the [Model_answer] completely matches the [Standard Answer] means they are consistent. For non-multiple-choice questions, if the meaning is expressed in the same way, it is also considered consistent, for example, 0.5m and 50cm. |
| If they are consistent, Judement is 1; if they are different, Judement is 0.\n\n |
| """ |
| question_for_eval = line['question_for_eval'] |
| extract = line['extract'] |
| answer = line['answer'] |
| demo_prompt = task_description |
| examples = get_gpt4_score_ICE() |
| for example in examples: |
| demo_prompt += example + '\n\n' |
| test_prompt = f""" |
| [Question]: {question_for_eval} |
| [Standard Answer]: {answer} |
| [Model_answer] : {extract} |
| Judgement:""" |
| full_prompt = f'{demo_prompt}{test_prompt}' |
|
|
| return full_prompt |
|
|
|
|
| def post_check_score(line, prefetch=False): |
| ans = str(line['answer']).strip() |
| response = str(line['extract']).strip() |
|
|
| if response == ans: |
| return response if prefetch else True |
| else: |
| return False |
|
|
|
|
| def MathVerse_auxeval_extract(model, line): |
| prompt = build_mathverse_gpt4_extract_prompt(line) |
| log = '' |
| retry = 5 |
| for i in range(retry): |
| prediction = line['prediction'] |
| res = model.generate(prompt, temperature=i * 0.5) |
|
|
| if FAIL_MSG in res: |
| log += f'Try {i}: output is {prediction}, failed to parse.\n' |
| else: |
| log += 'Succeed' |
| return dict(log_extract=log, extract=res) |
| log += 'All 5 retries failed.\n' |
| return dict(log_extract=log, extract='') |
|
|
|
|
| def MathVerse_auxeval_score(model, line): |
| prompt = build_mathverse_gpt4_score_prompt(line) |
| log = '' |
| retry = 5 |
| if post_check_score(line, prefetch=True): |
| res = post_check_score(line, prefetch=True) |
| return dict(log_score='Prefetch succeed', score=True) |
| for i in range(retry): |
| prediction = line['prediction'] |
| res = model.generate(prompt, temperature=i * 0.5) |
|
|
| if FAIL_MSG in res or res.strip() not in ['0', '1']: |
| log += f'Try {i}: output is {prediction}, res is {res}, failed to parse.\n' |
| else: |
| log += 'Succeed' |
| return dict(log_score=log, score=int(res) == 1) |
| log += 'All 5 retries failed.\n' |
| return dict(log_score=log, score=False) |
|
|
|
|
| def MathVerse_acc(result_file): |
| df = load(result_file) |
|
|
| df['metadata'] = df['metadata'].apply(lambda x: x.replace("'", '"')) |
| df['metadata'] = df['metadata'].apply(json.loads) |
| df_metadata = pd.json_normalize(df['metadata']) |
| df = pd.concat([df.drop('metadata', axis=1), df_metadata], axis=1) |
|
|
| subset = ["Overall"] |
| subset.extend(list(set(df['problem_version']))) |
| |
| res = defaultdict(list) |
| for p in subset: |
| if p != 'Overall': |
| sub = df[df['problem_version'] == p] |
| else: |
| sub = cp.deepcopy(df) |
| res['split'].append(p) |
| |
| res['Overall'].append(np.mean(sub['score']) * 100) |
| |
| subjects = set(df['subject']) |
| for k in subjects: |
| res[k].append(np.mean(sub[sub['subject'] == k]['score']) * 100) |
| |
| subfields = set(df['subfield']) |
| for k in subfields: |
| res[k].append(np.mean(sub[sub['subfield'] == k]['score']) * 100) |
|
|
| return pd.DataFrame(res) |
|
|
