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| def get_question_text(problem): | |
| question = problem['question'] | |
| return question | |
| def get_context_text(problem, use_caption): | |
| txt_context = problem['hint'] | |
| img_context = problem['caption'] if use_caption else "" | |
| context = " ".join([txt_context, img_context]).strip() | |
| if context == "": | |
| context = "N/A" | |
| return context | |
| def get_choice_text(probelm, options): | |
| choices = probelm['choices'] | |
| choice_list = [] | |
| for i, c in enumerate(choices): | |
| choice_list.append("({}) {}".format(options[i], c)) | |
| choice_txt = " ".join(choice_list) | |
| #print(choice_txt) | |
| return choice_txt | |
| def get_answer(problem, options): | |
| return options[problem['answer']] | |
| def get_lecture_text(problem): | |
| # \\n: GPT-3 can generate the lecture with more tokens. | |
| lecture = problem['lecture'].replace("\n", "\\n") | |
| return lecture | |
| def get_solution_text(problem): | |
| # \\n: GPT-3 can generate the solution with more tokens | |
| solution = problem['solution'].replace("\n", "\\n") | |
| return solution | |
| def create_one_example_chatbot(format, question, context, choice, answer, lecture, solution, test_example=True): | |
| input_format, output_format = format.split("-") | |
| ## Inputs | |
| if input_format == "CQM": | |
| input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n" | |
| elif input_format == "QCM": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n" | |
| # upper bound experiment | |
| elif input_format == "QCML": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n" | |
| elif input_format == "QCME": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n" | |
| elif input_format == "QCMLE": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n" | |
| elif input_format == "QCLM": | |
| input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n" | |
| elif input_format == "QCEM": | |
| input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n" | |
| elif input_format == "QCLEM": | |
| input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n" | |
| # Outputs | |
| if test_example: | |
| output = "Answer:" | |
| elif output_format == 'A': | |
| output = f"Answer: The answer is {answer}." | |
| elif output_format == 'AL': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {solution}" | |
| elif output_format == 'AE': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {lecture}" | |
| elif output_format == 'ALE': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}" | |
| elif output_format == 'AEL': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}" | |
| elif output_format == 'LA': | |
| output = f"Answer: {lecture} The answer is {answer}." | |
| elif output_format == 'EA': | |
| output = f"Answer: {solution} The answer is {answer}." | |
| elif output_format == 'LEA': | |
| output = f"Answer: {lecture} {solution} The answer is {answer}." | |
| elif output_format == 'ELA': | |
| output = f"Answer: {solution} {lecture} The answer is {answer}." | |
| elif output_format == 'LEPA': | |
| output = '' | |
| if len(lecture.strip()) > 0: | |
| output += f"LECTURE: {lecture}\n" | |
| if len(solution.strip()) > 0: | |
| output += f"SOLUTION: {solution}\n" | |
| output += '###\n' | |
| output += f"ANSWER: {answer}." | |
| input = input.replace(" ", " ").strip() | |
| output = output.replace(" ", " ").strip() | |
| if input.endswith("BECAUSE:"): | |
| input = input.replace("BECAUSE:", "").strip() | |
| if output.endswith("BECAUSE:"): | |
| output = output.replace("BECAUSE:", "").strip() | |
| return input, output | |
| def create_one_example(format, question, context, choice, answer, lecture, solution, test_example=True): | |
| input_format, output_format = format.split("-") | |
| ## Inputs | |
| if input_format == "CQM": | |
| input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n" | |
| elif input_format == "QCM": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n" | |
| # upper bound experiment | |
| elif input_format == "QCML": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n" | |
| elif input_format == "QCME": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n" | |
| elif input_format == "QCMLE": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n" | |
| elif input_format == "QCLM": | |
| input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n" | |
| elif input_format == "QCEM": | |
| input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n" | |
| elif input_format == "QCLEM": | |
| input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n" | |
| # Outputs | |
| if test_example: | |
| output = "Answer:" | |
| elif output_format == 'A': | |
| output = f"Answer: The answer is {answer}." | |
| elif output_format == 'AL': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {solution}" | |
| elif output_format == 'AE': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {lecture}" | |
| elif output_format == 'ALE': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}" | |
| elif output_format == 'AEL': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}" | |
| elif output_format == 'LA': | |
| output = f"Answer: {lecture} The answer is {answer}." | |
| elif output_format == 'EA': | |
| output = f"Answer: {solution} The answer is {answer}." | |
| elif output_format == 'LEA': | |
| output = f"Answer: {lecture} {solution} The answer is {answer}." | |
| elif output_format == 'ELA': | |
| output = f"Answer: {solution} {lecture} The answer is {answer}." | |
| text = input + output | |
| text = text.replace(" ", " ").strip() | |
| if text.endswith("BECAUSE:"): | |
| text = text.replace("BECAUSE:", "").strip() | |
| return text | |
| def create_one_example_gpt4(format, question, context, choice, answer, lecture, solution, test_example=True): | |
| input_format, output_format = format.split("-") | |
| ## Inputs | |
| if input_format == "CQM": | |
| input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n" | |
| elif input_format == "QCM": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n" | |
| # upper bound experiment | |
| elif input_format == "QCML": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n" | |
| elif input_format == "QCME": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n" | |
| elif input_format == "QCMLE": | |
| input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n" | |
| elif input_format == "QCLM": | |
| input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n" | |
| elif input_format == "QCEM": | |
| input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n" | |
| elif input_format == "QCLEM": | |
| input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n" | |
| # Outputs | |
| if test_example: | |
| output = "Answer:" | |
| elif output_format == 'A': | |
| output = f"Answer: The answer is {answer}." | |
| elif output_format == 'AL': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {solution}" | |
| elif output_format == 'AE': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {lecture}" | |
| elif output_format == 'ALE': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}" | |
| elif output_format == 'AEL': | |
| output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}" | |
| elif output_format == 'LA': | |
| output = f"Answer: {lecture} The answer is {answer}." | |
| elif output_format == 'EA': | |
| output = f"Answer: {solution} The answer is {answer}." | |
| elif output_format == 'LEA': | |
| output = f"Answer: {lecture} {solution} The answer is {answer}." | |
| elif output_format == 'ELA': | |
| output = f"Answer: {solution} {lecture} The answer is {answer}." | |
| input = input.replace(" ", " ").strip() | |
| output = output.replace(" ", " ").strip() | |
| if output.endswith("BECAUSE:"): | |
| output = output.replace("BECAUSE:", "").strip() | |
| user_prompt = {"role": "user", "content": f"Can you explain {input}?"} | |
| assistant_prompt = {"role": "assistant", "content": f"{output}"} | |
| return user_prompt, assistant_prompt | |
| def build_prompt_chatbot(problems, shot_qids, prompt_format, use_caption=False, options=["A", "B", "C", "D", "E"], is_test=False): | |
| examples = {} | |
| for qid in shot_qids: | |
| question = get_question_text(problems[qid]) | |
| context = get_context_text(problems[qid], use_caption) | |
| choice = get_choice_text(problems[qid], options) | |
| answer = get_answer(problems[qid], options) | |
| lecture = get_lecture_text(problems[qid]).replace('\\n', '\n') | |
| solution = get_solution_text(problems[qid]).replace('\\n', '\n') | |
| train_example = create_one_example_chatbot(prompt_format, | |
| question, | |
| context, | |
| choice, | |
| answer, | |
| lecture, | |
| solution, | |
| test_example=is_test) | |
| examples[qid] = train_example | |
| return examples | |
| def build_prompt(problems, shot_qids, test_qid, args): | |
| examples = [] | |
| # n-shot training examples | |
| for qid in shot_qids: | |
| question = get_question_text(problems[qid]) | |
| context = get_context_text(problems[qid], args.use_caption) | |
| choice = get_choice_text(problems[qid], args.options) | |
| answer = get_answer(problems[qid], args.options) | |
| lecture = get_lecture_text(problems[qid]) | |
| solution = get_solution_text(problems[qid]) | |
| train_example = create_one_example(args.prompt_format, | |
| question, | |
| context, | |
| choice, | |
| answer, | |
| lecture, | |
| solution, | |
| test_example=False) | |
| examples.append(train_example) | |
| # test example | |
| question = get_question_text(problems[test_qid]) | |
| context = get_context_text(problems[test_qid], args.use_caption) | |
| choice = get_choice_text(problems[test_qid], args.options) | |
| answer = get_answer(problems[test_qid], args.options) | |
| lecture = get_lecture_text(problems[test_qid]) | |
| solution = get_solution_text(problems[test_qid]) | |
| test_example = create_one_example(args.prompt_format, | |
| question, | |
| context, | |
| choice, | |
| answer, | |
| lecture, | |
| solution, | |
| test_example=True) | |
| examples.append(test_example) | |
| # create the prompt input | |
| prompt_input = '\n\n'.join(examples) | |
| return prompt_input | |
| def build_prompt_gpt4(problems, shot_qids, test_qid, args): | |
| prompt_array = [{"role": "system", "content": "You are a helpful assistant."}] | |
| # n-shot training examples | |
| for qid in shot_qids: | |
| question = get_question_text(problems[qid]) | |
| context = get_context_text(problems[qid], args.use_caption) | |
| choice = get_choice_text(problems[qid], args.options) | |
| answer = get_answer(problems[qid], args.options) | |
| lecture = get_lecture_text(problems[qid]) | |
| solution = get_solution_text(problems[qid]) | |
| user_prompt, assistant_prompt = create_one_example_gpt4(args.prompt_format, | |
| question, | |
| context, | |
| choice, | |
| answer, | |
| lecture, | |
| solution, | |
| test_example=False) | |
| prompt_array.append(user_prompt) | |
| prompt_array.append(assistant_prompt) | |
| # test example | |
| question = get_question_text(problems[test_qid]) | |
| context = get_context_text(problems[test_qid], args.use_caption) | |
| choice = get_choice_text(problems[test_qid], args.options) | |
| answer = get_answer(problems[test_qid], args.options) | |
| lecture = get_lecture_text(problems[test_qid]) | |
| solution = get_solution_text(problems[test_qid]) | |
| user_prompt, assistant_prompt = create_one_example_gpt4(args.prompt_format, | |
| question, | |
| context, | |
| choice, | |
| answer, | |
| lecture, | |
| solution, | |
| test_example=True) | |
| prompt_array.append(user_prompt) | |
| prompt_array.append(assistant_prompt) | |
| return prompt_array |