Elicitation

Elicitation is a Python package for training and evaluating Information Elicitation Agents (IEAs), based on the paper YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents, presented at ACL 2026.

IEAs are conversational agents designed to actively elicit information from users, rather than passively respond to requests.

Installation

pip install elicitation

Features

• Utilities for:
  • Generating elicitor utterances
  • Converting utterances into structured dialogues
• Evaluation metrics tailored to elicitation behavior:
  • Conformity (distributional alignment with human elicitors)
  • Progression (forward movement of dialogue)
  • Turn-Length Ratio (TLR) (elicitor vs. respondent verbosity)

Examples

Example scripts and sample data are available in the examples/ directory.

Configuration

Create a configuration file at config/config.yaml:

paths:
  proj_store: "./proj_store"
  models: "/data/models"

• proj_store: directory for intermediate outputs and logs
• models: path to local or downloaded model checkpoints

Running Examples

• example_dialoguegen.py: End-to-end pipeline to generate elicitor utterances and convert outputs into dialogue format.
• example_conformity_cossim.py: Computes Conformity (embedding similarity).
• example_conformity_perplexity.py: Computes Conformity (perplexity-based).
• example_interaction_metrics.ipynb: Interactive notebook for Progression and Turn-Length Ratio.

Typical Workflow

The package follows a three-stage pipeline consistent with the YIELD evaluation setup :

1. Generate Elicitor Utterances

Use a base or fine-tuned model to generate the next elicitor turn given dialogue context.

from elicitation.utils import generate_utterances

output_file = generate_utterances(
    model_choice=base_model_path,
    finetuning_dataset=data_input_folder,
    model_type="finetuned",  # or "prompted"
    adapter_model=adapter_model_path,  # required if finetuned
    prompt_file=None,  # required if prompted
    batch_size=8,
    save_dir=proj_store,
)

2. Convert to Dialogue Format

Convert generated outputs into structured dialogues for evaluation.

from elicitation.utils import utterances_to_dialogue

utterances_to_dialogue(output_file)

3. Evaluate

Conformity (Embedding Similarity)

from elicitation.metrics import conformity_cossim

results = conformity_cossim(input_filepath, embedding_model_path)

Conformity (Perplexity)

from elicitation.metrics import conformity_perplexity

results = conformity_perplexity(
    input_filepath,
    base_model_path,
    adapter_model_path,
    use_adapter=True
)

Interaction Metrics (Dialogue-Level)

from elicitation.metrics import progression, turn_length_ratio

progression_df = progression(dialogues, embedding_model, k=2, gamma=0.9)
tlr_df = turn_length_ratio(dialogues)

Citing Elicitation

If you use this resource in your projects, please cite the following paper.

@misc{De_Lima_YIELD_A_Large-Scale_2026,
author = {De Lima, Victor and Yang, Grace Hui},
doi = {10.48550/arXiv.2604.10968},
title = {{YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents}},
url = {https://arxiv.org/abs/2604.10968},
year = {2026}
}