README_en.md

# Arena-Lite (former Arena-Lite)
Arena-Lite conducts tournaments between models to be compared for each test set command, ranking models accurately at an affordable price. This is more accurate and cost-effective than rating win rates by comparing against reference outputs.

For more information, the followings may help understanding how it works.
* [Paper](https://arxiv.org/abs/2411.01281)
* [Blog Post (KR)](https://ncsoft.github.io/ncresearch/12cc62c1ea0d981971a8923401e8fe6a0f18563d)


## Quickstart
### Running Web Demo locally (streamlit, Recommended!)
```bash
git clone [THIS_REPO]
# install requirements below. we recommend miniforge to manage environment
cd streamlit_app_local
bash run.sh
```
For more details, see `[THIS_REPO]/streamlit_app_local/README.md`

### CLI use
* located at
  * `varco_arena/`
* debug configurations for vscode at
  * `varco_arena/.vscode`
```bash
## gpt-4o-mini as a judge
python main.py -i "./some/dirpath/to/jsonl/files" -o SOME_REL_PATH_TO_CREATE -m tournament -e "gpt-4o-mini"
## vllm-openai served LLM as a judge
python main.py -i "./some/dirpath/to/jsonl/files" -o SOME_REL_PATH_TO_CREATE -e SOME_MODEL_NAME_SERVED -m tournament -u "http://url_to/your/vllm_openai_server:someport"

# dbg lines
## openai api judge dbg
python main.py -i "rsc/inputs_for_dbg/dbg_400_error_inputs/" -o SOME_WANTED_TARGET_DIR -e gpt-4o-mini
## other testing lines
python main.py -i "rsc/inputs_for_dbg/[SOME_DIRECTORY]/" -o SOME_WANTED_TARGET_DIR -e gpt-4o-mini
## dummy judge dbg (checking errors without api requests)
python main.py -i "rsc/inputs_for_dbg/dbg_400_error_inputs/" -o SOME_WANTED_TARGET_DIR -e debug
```

## Requirements
```
pip install -r requirements.txt # python 3.11

# Linux
uvloop
# Windows
winloop
```


#### Argument
- -i, --input : directory path which contains input jsonlines files (llm outputs)
- -o, --output_dir : directory where results to be put
- -e, --evaluation : judge model specification (e.g. "gpt-4o-2024-05-13", "gpt-4o-mini", \[vllm-served-model-name\])
- -k, --openai_api_key : OpenAI API Key
- -u, --openai_url: URL to openai_styled_llm_server (requested by openai sdk)

#### advanced
- -j, --n_jobs : n jobs to be put to `asyncio.semaphore(n=)`
- -p, --evalprompt : [see the directory](./varco_arena/prompts/*.yaml)
- -lr, --limit_requests : vLLM OpenAI server request limit (default: 7,680)
- -lt, --limit_tokens : vLLM OpenAI server token limit (default: 15,728,640)

#### Input Data Format
[input jsonl guides](./streamlit_app_local/guide_mds/input_jsonls_en.md)


## Contributing & Customizing
#### Do this after git clone and installation
```bash
pip install pre-commit
pre-commit install
```
#### before commit
```bash
bash precommit.sh # black formatter will reformat the codes
```

### 📝 Adding a Custom Prompt

Here’s how to add a new evaluation prompt. The process has been simplified recently, as the Judge logic now only relies on the `parsed_output` method.

The easiest way is to copy `llmbar_brief.py` and `llmbar_brief.yaml` to create your own prompt.

#### 1. Create Prompt `.py` and `.yaml` Files

-   Create files like `my_prompt.py` and `my_prompt.yaml` in the `varco_arena/varco_arena_core/prompts/` directory.
-   **`my_prompt.py`**:
    -   Define a class that inherits from `ComparisonPromptBase`.
    -   You **must** implement the `parsed_output(self, response)` method. This function should take the LLM Judge's `response` and return a decision token (e.g., `'a'`, `'b'`) indicating the winner.
-   **`my_prompt.yaml`**:
    -   Define necessary elements for your prompt, such as `sampling_parameters`, `decision_tokens`, and `prompt_template`.
    -   The strings in prompt_template are processed by string.Template and finalized in eval_utils.py via the BasePrompt.complete_prompt() function.
    -   Do not use ${task} in prompt_template. It is a reserved keyword due to the llmbar prompt.

#### 2. Register the Prompt in `prompts/__init__.py`

-   Import your new prompt class:
    ```python
    from .my_prompt import MyPrompt
    ```
-   Add your new prompt's name and class instance to the `NAME2PROMPT_CLS` dictionary:
    ```python
    NAME2PROMPT_CLS = dict(
        # ... other prompts
        my_prompt=MyPrompt(),
    )
    ```
-   Add the new prompt name to the `Literal` type hint for the `promptname` argument in the `load_prompt` function:
    ```python
    def load_prompt(
        promptname: Literal[
            # ... other prompt names
            "my_prompt",
        ],
        # ...
    ):
    ```

#### 3. Add the Prompt to `eval_prompt_list.txt`

-   Open the `eval_prompt_list.txt` file in the project root and add the name of your new prompt (`my_prompt`) on a new line.

#### 4. (Recommended) Test and Debug

-   It is highly recommended to debug your prompt to ensure it works as expected.
-   In the `.vscode/launch.json` file, modify the `"VA"` configuration's `args`:
    -   Change `"-p", "translation_fortunecookie"` to `"-p", "my_prompt"`.
    -   If necessary, update the `"-i", "..."` argument to the path of your test data suitable for the new prompt.
-   Go to the `Run and Debug` tab in VS Code (Ctrl+Shift+D), select the "VA" configuration, and press F5 to run the debugger.
-   Find `result.json` inside the output directory you specified after `-o`. It will show every judge prompt used for each match.


## FAQ
* I want to apply my custom judge prompt to run Arena-Lite
  * [`./varco_arena/prompts/`](./varco_arena/prompts/__init__.py) defines the prompts with `yaml` file and the class objects for those. Edit those as your need.
* I want tailored judge prompts for each line of the test set row (i.e. ~100th row - `prompt1`, 101st~ - `prompt2`)
  * You could see `load_prompt` at the above link receives `promptname` + `task` as a parameters to load the prompt. The function is called at [`./varco_arena/manager.py:async_run`](./varco_arena/manager.py).

## Special Thanks to (contributors)
- Minho Lee (@Dialogue Model Team, NCSOFT) [github](https://github.com/minolee/)
  - query wrapper
  - rag prompt
- Jumin Oh (@Generation Model Team, NCSOFT)
  - overall prototyping of the system in haste


## Citation
If you found our work helpful, consider citing our paper!
```
@misc{son2024varcoarenatournamentapproach,
      title={VARCO Arena: A Tournament Approach to Reference-Free Benchmarking Large Language Models},
      author={Seonil Son and Ju-Min Oh and Heegon Jin and Cheolhun Jang and Jeongbeom Jeong and Kuntae Kim},
      year={2024},
      eprint={2411.01281},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.01281},
}
```

## Paper Experimental Results (raw data)
https://huggingface.co/datasets/fgenie777/Arena-Lite-Experiments-Result-Data/tree/main