Official Implementation for Discursive Socratic Questioning (DiSQ)

Official implementation for our paper: Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models’ Understanding of Discourse Relations (2024) Yisong Miao , Hongfu Liu, Wenqiang Lei, Nancy F. Chen, Min-Yen Kan. ACL 2024.
Paper PDF: https://yisong.me/publications/acl24-DiSQ-CR.pdf
Slides: https://yisong.me/publications/acl24-DiSQ-Slides.pdf
Poster: https://yisong.me/publications/acl24-DiSQ-Poster.pdf

Installation 📀💻

git clone git@github.com:YisongMiao/DiSQ-Score.git
conda activate
cd DiSQ-Score
cd scripts
pip install -r requirements.txt

Evaluate one model with one line command 🏃🧪🔬

Would you like to know the DiSQ Score for any language model? You are welcome to use this one line command!

We provide a simplified command to evaluate any language model (LM) that has been hosted in the HuggingFace model hub. You are recommended to use this for any new model (especially those not studied in our paper).

bash scripts/one_model.sh <modelurl>

The <modelurl> variable specifies the shortened path in the huggingface hub, for example,

bash scripts/one_model.sh meta-llama/Meta-Llama-3-8B

Specify Your Path 🏎️🛣️

Before running the bash files, please edit the bash file to specify your path to your local HuggingFace Cache.
For example, in scripts/one_model.sh:

#!/bin/bash

# Please define your own path here
huggingface_path=YOUR_PATH

you may change YOUR_PATH to the absolute directory location of your Huggingface Cache (e.g. /disk1/yisong/hf-cache).
We recommend at least 200GB free space.

An output text file will be saved at data/results/verbalizations/Meta-Llama-3-8B.txt, which contains:

=== The results for model: Meta-Llama-3-8B ===
Dataset: pdtb
DiSQ Score: 0.206
Targeted Score: 0.345
Counterfactual Score: 0.722
Consistency: 0.827
DiSQ Score for Comparison.Concession: 0.188
DiSQ Score for Comparison.Contrast: 0.22
DiSQ Score for Contingency.Reason: 0.164
DiSQ Score for Contingency.Result: 0.177
DiSQ Score for Expansion.Conjunction: 0.261
DiSQ Score for Expansion.Equivalence: 0.221
DiSQ Score for Expansion.Instantiation: 0.191
DiSQ Score for Expansion.Level-of-detail: 0.195
DiSQ Score for Expansion.Substitution: 0.151
DiSQ Score for Temporal.Asynchronous: 0.312
DiSQ Score for Temporal.Synchronous: 0.084
=== End of the results for model: Meta-Llama-3-8B ===
=== The results for model: Meta-Llama-3-8B ===
Dataset: ted
DiSQ Score: 0.233
Targeted Score: 0.605
Counterfactual Score: 0.489
Consistency: 0.787
DiSQ Score for Comparison.Concession: 0.237
DiSQ Score for Comparison.Contrast: 0.268
DiSQ Score for Contingency.Reason: 0.136
DiSQ Score for Contingency.Result: 0.211
DiSQ Score for Expansion.Conjunction: 0.268
DiSQ Score for Expansion.Equivalence: 0.205
DiSQ Score for Expansion.Instantiation: 0.194
DiSQ Score for Expansion.Level-of-detail: 0.222
DiSQ Score for Expansion.Substitution: 0.176
DiSQ Score for Temporal.Asynchronous: 0.156
DiSQ Score for Temporal.Synchronous: 0.164
=== End of the results for model: Meta-Llama-3-8B ===

Step by step walk through 🚶🧪🔬

Preliminary: Dataset 📀💿💽

We store our datasets in JSON files located at data/datasets/dataset_pdtb.json and data/datasets/dataset_ted.json. For example, let's take one instance from the PDTB dataset:

"2": {
        "Didx": 2,
        "arg1": "and special consultants are springing up to exploit the new tool",
        "arg2": "Blair Entertainment, has just formed a subsidiary -- 900 Blair -- to apply the technology to television",
        "DR": "Expansion.Instantiation.Arg2-as-instance",
        "Conn": "for instance",
        "events": [
            [
                "special consultants springing",
                "Blair Entertainment formed a subsidiary -- 900 Blair -- to apply the technology to television"
            ],
            [
                "special consultants exploit the new tool",
                "Blair Entertainment formed a subsidiary -- 900 Blair -- to apply the technology to television"
            ]
        ],
        "context": "Other long-distance carriers have also begun marketing enhanced 900 service, and special consultants are springing up to exploit the new tool. Blair Entertainment, a New York firm that advises TV stations and sells ads for them, has just formed a subsidiary -- 900 Blair -- to apply the technology to television.  "
    },

Here are the fields in this dictionary entry:

• Didx: The discourse ID.
• arg1 and arg2: The two arguments.
• DR: The discourse relation.
• Conn: The discourse connective.
• events: A list of pairs, storing the event pairs predicted as salient signals.
• context: The discourse context.

Step 1 Question Generation 🙋🧑‍🏫

cd DiSQ-Score
bash scripts/question_generation.sh

This bash file will call question_generation.py to generate questions under different configurations.

The arguments for question_generation.py are as follows:

• --dataset: Specifies the dataset, either pdtb or ted.
• --modelname: Aliases for models have been created. 13b refers to LLaMA2-13B, 13bchat to LLaMA2-13B-Chat, and vicuna-13b to Vicuna-13B. The specific URLs for these models can be found in disq_config.py.
• --version: Specifies which version of the prompt templates to use, with options v1, v2, v3, and v4.
• --paraphrase: Replaces the standard questions with their paraphrased versions, with options p1 and p2. Unlike the standard ones that call qa_utils.py, the paraphrased functions call qa_utils_p1.py and qa_utils_p2.py, respectively.
• --feature: Specifies which linguistic features to use for the discussion questions. Linguistic features include conn (discourse connective), and context (discourse context). Historical QA data requires a seperate script.

The output will be stored at, for example, data/questions/dataset_pdtb_prompt_v1.json under the configuration dataset==pdtb and version==v1.

We ask our users to generate the questions themselves because this approach is automatic and helps save space in our GitHub repository (which could add up to ~200 MB). If you are unable to run the bash file, please contact us for the question files.

Step 2 Question Answering 💭💬

cd DiSQ-Score
bash scripts/question_answering.sh

This bash file will call question_answering.py to perform Discursive Socratic Questioning (DiSQ) for any given model. question_answering.py takes all the arguments from question_generation.py, plus the following new arguments:

• --modelurl: Specifies the URL for any new models not currently in the config file. For example, 'meta-llama/Meta-Llama-3-8B' specifies the LLaMA3-8B model and will overwrite the modelname argument.
• --hf-path: Specifies the path to store the large model parameters. At least 200 GB of free disk space is recommended.
• --device_number: Specifies the ID of the GPU to use.

The output will be stored at, for example, data/results/13bchat_dataset_pdtb_prompt_v1/. The prediction for each question is a list of tokens and their probabilities, stored in a pickle file within the folder.

Caveat: The Wizard model has been taken down by the developers. We advise users not to try these models. Check the discussion thread at: https://huggingface.co/posts/WizardLM/329547800484476.

Step 3 Evaluation and Scoring ☑️💯

cd DiSQ-Score
bash scripts/eval.sh

This bash file will call eval.py to evaluate the previously obtained model predictions.

eval.py takes the same set of parameters as question_answering.py.

The outcome of the evaluation will be stored in disq_score_pdtb.csv if the specified dataset is PDTB.

There are 20 columns in the CSV file, namely:

• taskcode: Indicates the configuration being tested, e.g., dataset_pdtb_prompt_v1_13bchat.
• modelname: Specifies which language model is being tested.
• version: Indicates the version of the prompt.
• paraphrase: The parameter for paraphrase.
• feature: Specifies which feature has been used.
• Overall: The overall DiSQ Score.
• Targeted: Targeted score, one of the three components in the DiSQ Score.
• Counterfactual: Counterfactual score, one of the three components in the DiSQ Score.
• Consistency: Consistency score, one of the three components in the DiSQ Score.
• Comparison.Concession: The DiSQ Score for this specific discourse relation.
• ... (other discourse relations)

Note that we choose the best results among versions v1 to v4 to marginalize the impact of prompt templates.

To do so, eval.py automatically extracts the best results:

taskcode	modelname	version	paraphrase	feature	Overall	Targeted	Counterfactual	Consistency	Comparison.Concession	Comparison.Contrast	Contingency.Reason	Contingency.Result	Expansion.Conjunction	Expansion.Equivalence	Expansion.Instantiation	Expansion.Level-of-detail	Expansion.Substitution	Temporal.Asynchronous	Temporal.Synchronous
dataset_pdtb_prompt_v4_7b	7b	v4			0.074	0.956	0.084	0.929	0.03	0.083	0.095	0.095	0.077	0.054	0.086	0.068	0.155	0.036	0.047
dataset_pdtb_prompt_v1_7bchat	7bchat	v1			0.174	0.794	0.271	0.811	0.231	0.435	0.132	0.173	0.214	0.105	0.121	0.15	0.199	0.107	0.04
dataset_pdtb_prompt_v2_13b	13b	v2			0.097	0.945	0.112	0.912	0.037	0.099	0.081	0.094	0.126	0.101	0.113	0.107	0.077	0.083	0.093
dataset_pdtb_prompt_v1_13bchat	13bchat	v1			0.253	0.592	0.545	0.785	0.195	0.485	0.129	0.173	0.289	0.155	0.326	0.373	0.285	0.194	0.028
dataset_pdtb_prompt_v2_vicuna-13b	vicuna-13b	v2			0.325	0.512	0.766	0.829	0.087	0.515	0.201	0.352	0.369	0.0	0.334	0.46	0.199	0.511	0.074

For example, this table shows the best result for PDTB datasets for available open-source models, which reproduce the radar figure in our paper:

Discussion Experiments 🔬🤔