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RGER

This is the official implementation of "Reasoning graph enhanced exemplars retrieval for In-Context Learning"(RGER)

The code framework has been modified based on CEIL and DQ-LORE, and we are very grateful for their previous work.

Method Framework

The retrieval framework includes a reasoning graph generation process and graph similarity reranking shown as below: Main Framework

to graph

Setup

All required packages can be found in requirements.txt. You can install them in a new environment with

conda create -n RGER python=3.10
conda activate RGER

git clone https://github.com/Yukang-Lin/RGER.git

# The following line to be replaced depending on your cuda version.
pip install torch==2.3.0
pip install transformers=4.41.1
cd RGER
pip install -r requirements.txt
# if you don't want to use API from openai, just comment out the `openai` package in `requirements.txt`.

Testing on manual designed prompt methods

Manual cot prompt is placed at directory ./cot-prompt/, and result is save at output/svamp/vanilla/

sh scripts/concat_with_prompt.sh svamp cot
## this include cot and complex-cot
sh scripts/prompt_inference.sh 0 svamp cot
## generate response

For auto-cot method,

sh scripts/autocot_retriever.sh svamp 8
## generate auto-cot prompt in directory ./cot-prompt
sh scripts/concat_with_prompt.sh svamp autocot
sh scripts/prompt_inference.sh 0 svamp autocot
## generate response

Reproducing our results

  1. Train cot-aware embedding model. The model is save at output/svamp/svamp/vicuna-7b/bert-fix_ctx-shared-bs64/
sh scripts/retriever_trainer.sh 0 svamp vicuna-7b
## you should config your model_path in the script

  1. Generate graph representation

  2. Retrieve exemplars and inference. The retrieve example is save at output/svamp/svamp/vicuna-7b/RGER/prompt-RGER_rerank64.json where candidate ids is in 'ctxs' field. The response is save at output/svamp/svamp/vicuna-7b/RGER/pred_RGER_rerank64.json

sh scripts/retrieve_and_inference.sh 0 svamp RGER vicuna-7b

Testing on other retrieved-based methods

## Retrieve exemplars and inference
sh scripts/retrieve_and_inference.sh 0 svamp DQ-LoRe vicuna-7b
## method can select from EPR, CEIL, DQ-LoRe

evaluate the response

The script calcuates accuracy for response, then output results in results/svamp

  • For manual cot evaluation
sh scripts/evaluate_results_manual.sh svamp
  • For retrieve-based evaluation
sh scripts/evaluate_results_retrieved.sh svamp

use in your own dataset

Your can process your data in the manner as the paper, and save your graph in the directory save_graph/your_dataset with the function networkx_to_json in networkx package.