Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/liyucheng09/contamination_detector

Lightweight tool to identify Data Contamination in LLMs evaluation
https://github.com/liyucheng09/contamination_detector

Last synced: 2 months ago
JSON representation

Lightweight tool to identify Data Contamination in LLMs evaluation

Awesome Lists containing this project

README 

        


    Logo of Contamination Detector




# Contamination Detector for LLMs Evaluation



Data Contamination is a pervasive and critical issue in the evaluation of Large Language Models (LLMs). Our **Contamination Detector** is designed to identify and analyze potential contamination issues without needing access to the LLMs' training data, enabling the community to audit LLMs evaluation results and conduct robust evaluation.



**News!!**



- Our new preprint: [An open source data contamination report for large language models](https://arxiv.org/abs/2310.17589)!



# Our Methods: check potential contamination via search engine



Contamination Detector checks whether test examples appear on the internet via **Bing search** and **Common Crawl index**. We categorize test samples into three subsets:

1. **Clean** set: the question and reference answer do not appear online.

2. **Input-only contaminated** set: the question appears online, but not its answer.

3. **Input-and-label contaminated** set: both question and answer appear online.



If either the "question" or "answer" of a test example is found online, this sample may have been included in the LLM's training data. As a result, LLMs might gain an **unfair advantage by 'remembering' these samples**, rather than genuinely **understanding or solving them**.



We now support the following popular LLMs benchmarks:



- MMLU

- CEval

- Winogrande

- ARC

- Hellaswag

- CommonsenseQA



# Get start: Test LLMs' degree of contamination



1. Clone the repository and install the required packages:



```

git clone https://github.com/liyucheng09/Contamination_Detector.git

cd Contamination_Detector/

pip install -r requirements.txt

```



2. We need model predictions to further analyze their data contamination issue. We have prepared model predictions for the following LLMs:



- LLaMA 7,13,30,65B

- Llama-2 7,13,70B

- Qwen-7b

- Baichuan2-7B

- Mistral-7B

- Mistral Instruct 7B

- Yi 6B



That you can download directly without going through the inference:



```

wget https://github.com/liyucheng09/Contamination_Detector/releases/download/v0.1.1rc2/model_predictions.zip

unzip model_predictions.zip

```



If you hope to conduct the analysis on your own prediction data, format your model prediction as following and put under `model_predictions/`:



```

{

  "mmlu": {

    "business_ethics 0": {

      "gold": "C",

      "pred": "A"

    },

    "business_ethics 1": {

      "gold": "B",

      "pred": "A"

    },

    "business_ethics 2": {

      "gold": "D",

      "pred": "A"

    },

    "business_ethics 3": {

      "gold": "D",

      "pred": "D"

    },

    "business_ethics 4": {

      "gold": "B",

      "pred": "B"

    },

    .....

```



3. Generate contamination analysis table:



```

python clean_dirty_comparison.py

```



This will use the contamination annotation under `reports/` to generate models' performance on the clean, input-only contaminated, and input-and-label contaminated subsets.



See how the performance of Llama-2 70B differs on the three subsets.



| Dataset   | Condition          | Llama-2 70B |

|-----------|--------------------|--------------|

| MMLU      | Clean              | .6763       |

| MMLU      | All Dirty          | .6667 ↓      |

| MMLU      | Input-label Dirty  | .7093 ↑      |

| Hellaswag | Clean              | .7726       |

| Hellaswag | All Dirty          | .8348 ↑      |

| Hellaswag | Input-label Dirty  | .8455 ↑      |

| ARC       | Clean              | .4555       |

| ARC       | All Dirty          | .5632 ↑      |

| ARC       | Input-label Dirty  | .5667 ↑      |

| Average   | Clean              | .6348       |

| Average   | All Dirty          | .6882 ↑      |

| Average   | Input-label Dirty  | .7072 ↑      |



Other than this table, `clean_dirty_comparison.py` also produces a figure illustrating how the performance change with the recall score (the extent of contamination for a sample).



# Audit your own evaluation data



To check potential contamination in your benchmark, we have a script to identify potential contaminated test samples in your data:



Set up your benchmark in `utils.py`, this requires you to specify how to load your benchmark and verbalization methods, etc.



Then run the following to produce contamination reports for your benchmark:



```

python search.py

```



To run this script, you will need a free access token for Bing search API. You could obtain one via [this](https://www.microsoft.com/en-us/bing/apis/bing-web-search-api). A free access key allow 1000 calls monthly. Student will receive $100 funding if you're creating a new account.



Set the key via `export Bing_Key = [YOUR API KEY]` in terminal.



`search.py` will generate a report under `reports/` such as `reports/mmlu_report.json` that highlight all matches online, for example:

```

[

  {

    "input": "The economy is in a deep recession. Given this economic situation which of the following statements about monetary policy is accurate?",

    "match_string": "The economy is in a deep recession. Given this economic situation, which of the following statements about monetary policy is accurate policy recession policy",

    "score": 0.900540825748582,

    "name": "AP Macroeconomics Question 445: Answer and Explanation - CrackAP.com",

    "contaminated_url": "https://www.crackap.com/ap/macroeconomics/question-445-answer-and-explanation.html",

  },

...

```



Reports for six popular multi-choice QA benchmarks are ready to access under `/reports`.



To visualize the results, please move to [visualize](https://github.com/liyucheng09/Contamination_Detector/tree/master/visualize).



**Check contamination examples: MMLU at [here](https://htmlpreview.github.io/?https://github.com/liyucheng09/Contamination_Detector/blob/master/reports/mmlu.html), and C-Eval at [here](https://htmlpreview.github.io/?https://github.com/liyucheng09/Contamination_Detector/blob/master/reports/ceval.html)**



If you cannot accessing Huggingface Hub for the benchmark datasets, download them as json files [here](https://github.com/liyucheng09/Contamination_Detector/releases/tag/v0.1.1).



## Citation:



Consider cite our project if you find it helpful:

```

@article{Li2023AnOS,

  title={An Open Source Data Contamination Report for Large Language Models},

  author={Yucheng Li},

  journal={ArXiv},

  year={2023},

  volume={abs/2310.17589},

}

```



## Issues



Open an issue or contact me via email if you encounter any problems in your use.