This is the official repo for our COLM 2024 paper Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMs.
Yilun Hua and Yoav Artzi
To evaluate IDEFICS, Gemini, GPT4, and Claude, create a new environment with python>=3.10 and run pip install -r requirements.txt.
To evaluate LLaVA, create a new environment with python>=3.10, run pip install -r requirements.llava.txt, then go to the directory LLaVA and run pip install -e ..
For open-sourced models, their checkpoints will be automatically downloaded when running our code. You will need API access for the closed-source models (Gemini, GPT4, and Claude). Please refer to their websites and documentions for API access.
To evaluate other models, it's best to create separate environments for them and follow their official instruction for installation.
Edit the arguments in run_experiment.sh to specify the speaker and listener models and their respective checkpoint names. For GPT, Gemini, and Claude, also specify your API key and organization key (if applicable).
See run_icca.py for the example input values of the arguments.
Section 4, 5 and Appendix D of our paper explain the reference game variants we designed for evaluating MLLMs. These variants can be easily achieved by ICCA by modifying their respective arguments.
Speaker Experiments
You can control what prompt the speaker model receives using the intro_texts_spkr.json, which currently shows examples of soliciting efficient communication with instructions of varied levels of explicitness.
Listener Experiments
You can modify the arguments in interaction_args_lsnr.json for different game variants introduced in our paper, including hiding the images after Trial 1, controlling whether to shuffle between trials, when to introduce misleading images, and etc. You can also modify the instructions given to the model in intro_texts_lsnr.json.
You can customize the models in MLLMs.py. You can change the interaction template, such as how the model receives the image, the phrasing of the system feedback, text generation hyperparameters, and etc.
For each new model, you can implement a new wrapper class that inherits from the ModelWrapper class in MLLMs.py. You can then conduct prompt engineering and model-specific customization following the instructions above.