Direct Evaluation of CoT in Multi-hop Reasoning with Knowledge Graphs

Official Implementation of "Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs".

Has been accepted at ACL2024 Findings.

Aiming evaluate not only final answers but also intermediate steps in the CoT reasoning capabilities of LLMs in multi-hop question answering, the paper proposed 2 evaluation modules:

1. Discriminative: assess LLMs' knowledge of reasoning
2. Generative: assess the accuracy of the generated CoT by utilizing knowledge graphs (KGs).

In addition, we do ablation studies to evaluate the fine-grain CoT generation to calculate edit-distance & reasoning errors.

Requirements

conda create --name llm-reasoning-cert python=3.8
conda activate llm-reasoning-cert

pip install -r requirements.txt

Datasets

The paper uses 2 datasets: CWQ and GrailQA as initiate datasets for experiments.

Then, extract subgraph and ground-truth reasoning path based on SPARQL.

Final datasets used for the paper are uploaded into HuggingFace: (Note: update later)

1. CWQ-Subgraph-Eval
2. GrailQA-Subgraph-Eval

Preprocess for each dataset:

Aim: create subgraphs for querying ground-truth reasoning path & creating VectorDB

Create subgraphs

Code at ./preprocess_data

1. Create subgraph from the raw-subgraph via the detail implementation in preprocess's readme
2. Get groundtruth reasoning path via the subgraph, answer entities and topic entities

python ./preprocess_data/ground_truth_paths.py

4. Rearrange questions according to the number of edge of groundtruth reasoning path

python ./preprocess_data/splitted_ground_truth_paths.py

We only use questions >=2 hops in the corresponding reasoning path.

Create VectorDB

FAISS & sentence-transformers/all-mpnet-base-v2 are used to create VectorDB before retrieving

DATASET='cwq' # 'grail_qa
sbatch scripts/gen-cert/extract_triplet.sh $DATASET

you can setup addition arguments:

• embed_model_name. Default is sentence-transformers/all-mpnet-base-v2
• top_k. Default is 10
• device. Default is cpu

!Note: remember re-setup them in ./generative-cert.py#L228

Data for Discriminative

Download data at here

Generate negative reasoning paths

• Negative generation model:
  1. replace: replace the entities in reasoning paths.
  2. reorder: reorder the reasoning paths.
  3. mislead: generate the reasoning paths leading to incorrect answers.
• Code:

# 1. Generate supgraph for misguide paths
python preprocess_data/subgraph_discriminative_cert.py
# 2. Generate negative paths:
## - CWQ dataset
python gen_negative.py --data_path data/cwq_test_res.csv --kg_path data/cwq_test.jsonl_cwq_test.jsonl --mode {'mislead', 'reorder', 'replace'}
## - GrailQA dataset
python gen_negative.py --data_path data/multi_hop_grailqa.csv --kg_path data/grail_w_kg.jsonl --mode {'mislead', 'reorder', 'replace'}

Framework

Set your OpenAI api key & Huggingface key (if needed) in .env (check file .env.example as the example).

Discriminative Mode

• Evaluation for ground-truth reasoning paths

    sh scripts/disc-cert/submit_discriminative_cert.sh

• Evaluation for generated negative reasoning paths

    sh scripts/disc-cert/submit_discriminative_cert_neg.sh

• Get results

python scripts/disc-cert/summary_results.py

Generative Mode

Stage1: LLM prompting for structured answer

1. ChatGPT

sh scripts/gen-cert/llm_prompting.sh

2. HF models: Llama2 7B/13B/70B chat-hf, Mistral-7B-Instruct-v0.1, Qwen-14B-Chat, Vicuna-33b-v1.3

sh generative_cert/scripts/fitcluster/script.sh

Stage 2 & 3: Retrieval & Evaluation

1. Main result

sh scripts/gen-cert/job_eval_llm.sh

2. The fine-grained generative evaluation: edit-distance score

sh scripts/gen-cert/job_eval_llm_finegrained.sh
python finegrained_analysis.py

3. Run the analysis for reasoning errors

python finegrained_analysis.py

Results

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Citation

If you find this paper or the repo useful for your work, please consider citing the paper

@misc{nguyen2024direct,
    title={Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs},
    author={Minh-Vuong Nguyen and Linhao Luo and Fatemeh Shiri and Dinh Phung and Yuan-Fang Li and Thuy-Trang Vu and Gholamreza Haffari},
    year={2024},
    eprint={2402.11199},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}