interact

Official Implementation for the intelligibility protocol, PXP.

Installation

We have very minimal dependencies, and you can install them using the following command:

pip install -r requirements.txt

You might want to create a virtual environment (or use conda) to avoid conflicts with your system packages. We use python3.9.18 for all experiments.

Data

For the RAD task, please write to Prof. Sidong Liu [email]. For the DRUG task, please write to Shreyas V [email]. Please mention "[INTERACT]" in the subject. You can then use src/preprocess.py to generate the data in the correct format, for the experiments. This will also summarize the data, using the summarize function from src/utils.py.

Reproducing our results

To reproduce our RAD results, you can run the following command:

python src/interact.py --num_iter=5

This will output the counts of one-way and two-way intelligible sessions, create a tags.txt file of the actual tags exchanged between the two agents and also save the D (data.pkl), M (messages.pkl) and C (context.pkl) (from Procedure 1 in the paper) to the results/ folder. To reproduce the trend in Figure 3 from the paper, we ran the above command 5 times and manually extracted how many one-way intelligible sessions (upto an interaction limit) were generated per agent. Reproducing the DRUG results requires an expert and so the outcome may be stochastic.

Static / Real-time feedback

In general the code allows for interaction between both static and real-time human feedback and an LLM (interfaced by the XMachine). To use the approach with custom data,

• you can use some form of static human feedback (like RAD), stored in data as a CSV,
• as with the DRUG task, one can create an analogous real-time feedback system, using the command line and a real expert human for feedback.

How to use the code for a different task

Here, we precisely describe how to use the code for a different task, say MATS (i.e. Materials Science).

• Decide the type of feedback you have access to, static (CSV with some predictions and explanations) or real-time (human expert)
• If it is static then you would need to add the data to the data/ folder.
• Now, depeding on the type of feedback, you should implement a MATSAgent class in src/agents.py which should inherit from Agent, and borrow code from RADAgent (if static) and DRUGAgent (if real-time).
• Following this, implement MATSMachine and MATSHuman classes in the same file.
• With this, you need to change the create_agent in src/agent.py to also be compatible with the new task.
• Finally, you have to implement the MATS class in src/tasks.py which should inherit from Task and borrow code from RAD and DRUG appropriately.
• Now, you can run the code using the following command: (add this task to the choices for the --task argument)

python src/interact.py --num_iter=5 --task=MATS

Example

This is an example interaction (from the RAD task) generated by using the PXP protocol and our implementation (As explained in the paper, this is a special case of the protocol such that the human-agent can never revise it's internal model).

Citation

@misc{srinivasan2024implementationapplicationintelligibilityprotocol,
      title={Implementation and Application of an Intelligibility Protocol for Interaction with an LLM}, 
      author={Ashwin Srinivasan and Karan Bania and Shreyas V and Harshvardhan Mestha and Sidong Liu},
      year={2024},
      eprint={2410.20600},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2410.20600}, 
}