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README.md

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+# Small Language Models Need Strong Verifiers to Self-Correct Reasoning
+
+## Abstract
+
+> Self-correction has emerged as a promising solution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated critiques that pinpoint the errors. This work explores whether small (≤ 13B) language models (LMs) have the ability of self-correction on reasoning tasks with minimal inputs from stronger LMs. We propose a novel pipeline that prompts smaller LMs to collect self-correction data that supports the training of self-refinement abilities. First, we leverage correct solutions to guide the model in critiquing their incorrect responses. Second, the generated critiques, after filtering, are used for supervised fine-tuning of the self-correcting reasoner through solution refinement. Our experimental results show improved self-correction abilities of two models on five datasets spanning math and commonsense reasoning, with notable performance gains when paired with a strong GPT-4-based verifier, though limitations are identified when using a weak self-verifier for determining when to correct.
+
+Below we introduce the command for reproducing out experimental results. We decompose the task of self-correction into two phases: (SELF-)VERIFY and SELF-REFINE. 
+
+For Verifiers, we implement the following options: 
+
+- Fine-tuning Small LM as self-verifier
+- Prompting GPT-4 as verifier
+- Prompting Small LM as verifier
+
+For Refiners, we implement the following options:
+
+- SCORE-fine-tuned small LM as refiner
+- Prompting gpt-4 as refiner
+
+For the following commands, set the `${TASK}` parameter as follows:
+
+| Dataset Name  | ${TASK}     |
+| ------------- | ----------- |
+| GSM8K         | gsm8k_nl    |
+| MATH Subset   | math        |
+| CommonsenseQA | csqa        |
+|               | riddlesense |
+| QASC          | qasc        |
+
+And set the `${MODEL}` parameter as either `meta-llama/Llama-2-13b-chat-hf` or `google/gemma-7b-it`.
+
+## Refiner Options
+
+### Ours Refiner Training Data Collection Pipeline (SCORE)
+
+#### Step a. Sample and label N=10 solutions per question
+
+Sample diverse reasoning chains for each question.
+
+`python sample_ans.py --task ${TASK} --model ${MODEL} --split train --num_generations 10`
+
+Score each reasoning chains based on final answer.
+`python pal_code_exec.py --task ${TASK} --model ${MODEL} --split train`
+
+#### Step b. Generate critique for incorrect solutions using correct solutions as hints
+
+`python sample_feedback.py --task ${TASK} --model ${MODEL} --method diff --num_generations 1 --greedy
+
+> Appy rule-based filtering to filter out invalid feedback. These criteria include: 1) The number of steps and feedbacks (counted by the appearances of “Step {i}:” and “Feedback:”) should be the same. 2) Each step should be exactly copied from the initial solution. 3) The feedback for the last step should provide the correct answer.
+
+`python prefilter_feedback.py --task ${TASK} --model ${MODEL}  --greedy`
+
+#### Step c. Check whether base LM can recover the correct solution with filtered critique
+
+Generate refinement based on the feedback.
+
+`python apply_feedback.py --task ${TASK} --method diff --model ${MODEL} --num_generations 1 --greedy`
+
+Score refined solution based on final answer correctness.
+
+`python apply_feedback_results.py --task ${TASK} --model ${MODEL}`
+
+#### Step d. Supervised Finetuning on critique-correction data
+
+Prepare fine-tuning data into LLaMA-Factory format.
+
+`python prepare_sft_data.py --task ${TASK} --model ${MODEL} --stage sft`
+
+Use LLaMA-Factory for SFT.
+
+### Baseline: Prompting Small LM as refiner
+
+```bash
+python sample_verification.py \
+        --task ${TASK} \
+        --model ${MODEL} \
+        --verifier ${MODEL} \
+        --split test \
+        --num_generations 1 \
+        --greedy \
+        --self_refine
+```
+
+## Verifier Options
+
+Generate and label one solution for each question to be verified.
+
+```bash
+# Generate solutions
+python sample_ans.py --task ${TASK} --model ${MODEL} --split dev --num_generations 1 --greedy
+python sample_ans.py --task ${TASK} --model ${MODEL} --split test --num_generations 1 --greedy
+# Label solutions
+python pal_code_exec.py --task ${TASK} --model ${MODEL} --split dev --greedy
+python pal_code_exec.py --task ${TASK} --model ${MODEL} --split test --greedy
+```
+
+### Option 1: Fine-tuning Small LM as self-verifier
+
+Prepare verifier fine-tuning data.
+
+```bash
+python --task ${TASK} --model ${MODEL} --split train
+python --task ${TASK} --model ${MODEL} --split dev --greedy
+python --task ${TASK} --model ${MODEL} --split test --greedy
+```
+
+Fine-tuning verifier with a binary classification objective.
+
+```bash
+# Fine-tuning
+python llama_sequence_classification.py \
+		--data_path data/${TASK}/verifier/${MODEL} \
+		--output_path checkpoint/verifier/${TASK}/${MODEL} \
+		--model_name ${MODEL} \
+		--set_pad_id \
+		--lr 1e-5 \
+		--train_batch_size 32 \
+		--eval_batch_size 32 \
+		--num_epochs 3 \
+		--lora_rank 32 \
+		--lora_alpha 64		
+# Inference on dev and test		
+python llama_sequence_classification.py \
+		--data_path data/${TASK}/verifier/${MODEL}/dev.json \
+		--output_path checkpoint/verifier/${TASK}/${MODEL}/dev.json \
+		--model_name ${MODEL} \
+		--set_pad_id \
+		--predict_only
+python llama_sequence_classification.py \
+		--data_path data/${TASK}/verifier/${MODEL}/test.json \
+		--output_path checkpoint/verifier/${TASK}/${MODEL}/test.json \
+		--model_name ${MODEL} \
+		--set_pad_id \
+		--predict_only
+```
+
+### Option 2: Prompting GPT-4 as verifier
+
+```bash
+python sample_verification.py \
+        --task ${TASK} \
+        --model ${MODEL} \
+        --verifier gpt-4 \
+        --split test \
+        --num_generations 1 \
+        --greedy
+```
+
+### Option 3: Prompting Small LM as verifier
+
+```bash
+python sample_verification.py \
+        --task ${TASK} \
+        --model ${MODEL} \
+        --verifier ${MODEL} \
+        --split test \
+        --num_generations 1 \
+        --greedy
+```
+
+## Inference and Evaluation
+
+Prepare inference data into LLaMA-Factory format.
+
+```bash
+python prepare_sft_data.py --task ${TASK} --model ${MODEL} --stage predict_feedback --split dev --sample 1
+python prepare_sft_data.py --task ${TASK} --model ${MODEL} --stage predict_feedback --split test --sample 1
+```
+
+Use LLaMA-Factory to generate one critique-correction for each question-solution.
+
+Then report the final task accuracies after self-correction.
+
+> During inference, the self-verifier model outputs a probability of the initial solution being incorrect, and the refinement is introduced only when the confidence of the verifier’s predictions exceeds a certain threshold, which is automatically chosen in a way that maximizes the accuracy on the dev set ...
+
+`python ft_results.py --task ${TASK} --stage eval_greedy_refine --input prompted --verifier_type ours_ft_on_prompted_solutions --verifier_model ${MODEL} --refiner_type filtered_feedback4_refinement_diff --refiner_model ${MODEL} --split dev --verbose`
+
+ This will print the optimal threshold ${THRESHOLD}.
+
+> and then fixed during test-time predictions.
+
+`python ft_results.py --task ${TASK} --stage eval_greedy_refine --input prompted --verifier_type ours_ft_on_prompted_solutions --verifier_model ${MODEL} --refiner_type filtered_feedback4_refinement_diff --refiner_model ${MODEL} --split test --best_dev_confidence ${THRESHOLD} --verbose`
+
+For other settings:
+
+- change to `--refiner_type prompted --refiner_model ${MODEL}` for prompted small LM as self-refiner
+
+- change to `--verifier_type prompted --verifier_model gpt-4` for prompted gpt-4 as verifier
+- change to `--verifier_type prompted --verifier_model ${MODEL}` for prompted small LM as verifier