This repository contains the code and the data for the paper "Steering Conversational Large Language Models for Long Emotional Support Conversations"
The original ESConv dataset is available under esconv/ directory. You can run the process_esconv.sh to
convert the data into a format that we use in our experiments. It will create a json file inside the same folder
called conversations.json. You can run the script with the following command:
bash process_esconv.shThen you can postprocess the generated responses using prompting/postprocess.py script. A sample of the generated data
is available in data/ directory. Each file contains one incomplete conversation and a few continuations using
different strategies. Along with this information we also provide the exact prompt that we used to generate each continuation.
We are on the mission to complete this dataset and make it available for the public. We will update this section once each part of the dataset is ready. You can currently download the first version of the dataset from this link
| Model Used | Number of Conversations | Number of Continuations | Download Link |
|---|---|---|---|
| LLama-7b-chat | 9,354 | 41,994 | Download |
| LLama-13b-chat | 9,354 | 41,994 | Download |
| LLama-70b-chat | 5,479 | 24,760 | Download |
For our experiments we use LLaMa v2 chat models with 4bit quantization. You can follow the instruction in the following links to get access to 7b, 13b and 70b models on huggingface.
All of the experiments are conducted using the transformers library. We use bitsandbytes to quantize the models. We also
run inference on the models using one A100 GPU with 80GB memory.
You can run the experiments in the paper using the following commands:
cd prompting
bash llama7b.sh
bash llama13b.sh
bash llama70b.shThis will generate the sampled data collections for the experiments in the paper. The rest of the
analysis will be done using prompting/strategy_following_comparison.ipynb notebook.
We also provide the code to visualize the attention that each span of the prompt receives. You can run the following Flask app to visualize the attention weights:
cd attention_visualizer
python app.py --data_dir /path/to/generated/pickle/files/from/previous/stepMake sure you provide a directory with pickle files in the format we produce in the previous section
using prompting/multiple_strategy_continuation.py. You will see a similar html page to the following
in which you can select a span and visualize the weights on the prompt text. Also you will get top
20 tokens that the model attends to on the left side of the page:
