Evaluating the Moral Beliefs Encoded in LLMs

January 26, 2024 · View on GitHub

Authors: Nino Scherrer*, Claudia Shi*, Amir Feder and David Blei

Paper: Evaluating the Moral Beliefs Encoded in LLMs (NeurIPS 2023 - Spotlight).

Dataset: https://huggingface.co/datasets/ninoscherrer/moralchoice

@inproceedings{scherrer2023evaluating,
  title={Evaluating the Moral Beliefs Encoded in LLMs},
  author={Nino Scherrer and Claudia Shi and Amir Feder and David Blei},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023},
  url={https://openreview.net/forum?id=O06z2G18me}
}

tl;dr

This paper presents a case study on the design, administration, post-processing, and evaluation of surveys on large language models (LLMs). It comprises two components:

A statistical method for eliciting beliefs encoded in LLMs. We introduce statistical measures and evaluation metrics that quantify the probability of an LLM "making a choice", the associated uncertainty, and the consistency of that choice.

We apply this method to study what moral beliefs are encoded in different LLMs, especially in ambiguous cases where the right choice is not obvious. We design a large-scale survey comprising 680 high-ambiguity moral scenarios (e.g., "Should I tell a white lie?") and 687 low-ambiguity moral scenarios (e.g., "Should I stop for a pedestrian on the road?"). Each scenario includes a description, two possible actions, and auxiliary labels indicating violated rules (e.g., "do not kill"). We administer the survey to 28 open- and closed-source LLMs. We find that:

In unambiguous scenarios, most models "choose" actions that align with commonsense. In ambiguous cases, most models express uncertainty.
Some models are uncertain about choosing the commonsense action because their responses are sensitive to the question-wording.
Some models reflect clear preferences in ambiguous scenarios. Specifically, OpenAI's gpt-4, Anthropic's claude-v1.1, claude-instant-v1.1 and Google's text-bison-001 (PaLM 2) show strong agreement.

Overview (ToC)

Evaluating the Moral Beliefs Encoded in LLMs

Install

General Requirements:

>> python -m pip install -r requirements.txt

API-Keys: You must add you API-key's into the corresponding files in api_keys.

Data

Survey:
- Low and High-Ambiguity Scenarios in data/scenarios
- Question Templates in data/question_templates
- Response Templates in data/response_templates
Survey Responses:
- Responses of 28 LLMs in data/responses/paper

All available data is also shared through HuggingFace: https://huggingface.co/datasets/ninoscherrer/moralchoice

Model Evaluations

You can run a new evaluation on supported models by running:

>> python -m src.evaluate
    --experiment-name "moraltest" 
    --dataset "low" 
    --model "openai/gpt-4"
    --question-types "ab" "repeat" "compare" 
    --eval-nb-samples 5

This will generate multiple pickle files, i.e., one per (scenario, model, question_type) combination. This allows to resume interrupted experiments efficiently.

Finally, you can collect and aggregate all results into a single csv per model using:

>> python -m src.collect 
    --experiment-name "moraltest" 
    --dataset "low"

Visualizations

Jupyter Notebooks to be uploaded

Models

Supported Models

AI21
- J2-Instruct (API):
  - ai21/j2-grande-instruct
  - ai21/j2-jumbo-instruct
Cohere
- Command (API):
  - cohere/command-xlarge
  - cohere/command-medium
OpenAI:
- GPT3 (API):
  - openai/text-ada-001
  - openai/text-babbage-001
  - openai/text-curie-001
  - openai/text-davinci-001
- GPT3.5 (API):
  - openai/text-davinci-002
  - openai/text-davinci-003
  - openai/gpt-3.5-turbo
- GPT-4 (API):
  - openai/gpt-4
Anthropic
- Claude (API):
  - anthropic/claude-v1.2
  - anthropic/claude-v1.3
  - anthropic/claude-v2.0
- Claude Instant (API):
  - anthropic/claude-instant-v1.0
  - anthropic/claude-instant-v1.1
Google
- Flan-T5 (HF-Hub):
  - google/flan-t5-small
  - google/flan-t5-base
  - google/flan-t5-large
  - google/flan-t5-xl
  - google/flan-t5-xxl
- PaLM 2 (API):
  - google/text-bison-001
Meta
- OPT-IML-Regular (HF-Hub):
  - meta/opt-iml-regular-small
  - meta/opt-iml-regular-large
- OPT-IML-Max (HF-Hub):
  - meta/opt-iml-max-small
  - meta/opt-iml-max-large
BigScience
- Bloomz (HF-Hub):
  - bigscience/bloomz-560m
  - bigscience/bloomz-1b1
  - bigscience/bloomz-1b7
  - bigscience/bloomz-3b
  - bigscience/bloomz-7b1
  - bigscience/bloomz-7b1-mt
  - bigscience/bloomz-7b1-p3

Adding New Models

For most API-based models from supported providers, you can simply extend the MODELS dict in models.py
Otherwise, you can integrate the required model handler in models.py

Adding More Question Templates

You can add further question templates to our evaluation procedure by:

Adding <question_name>.json to data/question_templates with the question template
Adding <question_name>.json to data/response_templates with common answer patterns

Note:

To retrieve common answer patterns for a specific question template, it is beneficial to generate a set of answers and then cluster common-prefixes.
The current evaluation workflow only supports binary decision settings. But the pipeline can be extended to more options if needed.

🚧 Limitation 🚧: Semantic Matching

To map LLM outputs (i.e., sequences of tokens) to actions, we employ a rule-based matching function. This matching procedure is mainly based on common answer variations/patterns of the evaluated LLMs under the specific question templates. On average, we are able to match ~97% of the answers to action1, action2 and refusal and only classify ~3% of the answers as invalid. However, as this matching procedure does not account for all possible answer paraphrases,there may be some obvious answer matches in the response that were classified as invalid responses.

In future work, we intend to improve the current pipeline to also account for a broader set of answer variations by using a prompted LLMs or a bidirectional entailment algorithm (such as introduced in Kuhn et al. 2023) for matching.