Implicit Embeddings Benchmark

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Paper: Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning. Accepted to ICML 2026 as a position paper; the citation below identifies the 2025 arXiv record.

Read the literature guide for introduction and related-work context, and the evidence and scope notes before using a claim. If you discuss the research agenda for implicit semantics in text embeddings or the accompanying pilot evaluation, please cite the paper when it supports your statement. Code use or an experimental comparison is not required. BibTeX · Citation metadata.

Overview

This benchmark evaluates embedding models on their ability to capture implicit semantics - aspects not well covered by existing benchmarks like MTEB. The focus is on understanding how well embedding models can capture nuanced meaning beyond explicit similarity.

Note: This benchmark contains the preliminary experiments used in our ICML 2026 position paper: Position: Text embeddings should capture implicit semantics, not just surface meaning.

Requirements

• Python 3.7+
• sentence-transformers
• scikit-learn
• pandas
• numpy

Install the required packages:

pip install sentence-transformers scikit-learn pandas numpy

Datasets

The benchmark includes multiple datasets:

• PUB (Pragmatics Understanding Benchmark): Contains multiple tasks in JSONL format
• P-Stance: A large dataset for stance detection in political domain
• Social Bias Frames: A dataset for detecting various types of bias in text
• Implicit Hate Speech: A dataset for understanding implicit hate speech
• Article-Bias-Prediction: A dataset for detecting political bias in news articles

Running the Benchmark

Basic Usage

Run the benchmark on a specific embedding model:

python run_benchmark.py --model all-MiniLM-L6-v2

This will evaluate the model on all the default datasets (pub, pstance, sbic) and save the results to a directory named after the model.

Advanced Usage

Specify which datasets to evaluate:

python run_benchmark.py --model all-MiniLM-L6-v2 --datasets pub pstance

Specify a custom output directory:

python run_benchmark.py --model all-MiniLM-L6-v2 --output ./custom_results

Force CPU mode (useful if you have GPU issues):

python run_benchmark.py --model all-MiniLM-L6-v2 --cpu

Skip datasets that have already been evaluated (useful for resuming interrupted runs):

python run_benchmark.py --model all-MiniLM-L6-v2 --skip-existing

Understanding Results

Results are saved in the output directory with the following structure:

results/
└── all-MiniLM-L6-v2/
    ├── pub_results.csv
    ├── pub_results.json
    ├── pstance_results.csv
    ├── pstance_results.json
    └── summary.json

Each dataset has its own CSV and JSON file with detailed metrics:

• Accuracy
• F1 score (macro, micro, weighted)
• Precision and recall
• Evaluation time

The summary.json file contains the aggregated results from all datasets.

You can also specify a custom output directory using the --output parameter:

python run_benchmark.py --model all-MiniLM-L6-v2 --output ./custom_results

Evaluation Approach

The benchmark uses different approaches for evaluating embedding models based on task type:

Classification Tasks

For standard classification tasks:

1. Load the embedding model using sentence-transformers
2. Encode text samples using the model
3. Train a logistic regression classifier on the embeddings
4. Evaluate performance on test set
5. Calculate and report metrics (accuracy, F1 scores, etc.)

Zero-Shot Classification Tasks

For zero-shot classification tasks (like implicature recovery, figurative language understanding):

1. Load the embedding model using sentence-transformers
2. For each test sample:
   • Encode the input text
   • Encode all possible option texts
   • Compute cosine similarity between the input embedding and each option embedding
   • Predict the option with highest similarity
3. Calculate and report accuracy

Pair Classification Tasks

For pair classification tasks (like agreement detection):

1. Load the embedding model using sentence-transformers
2. For each pair of texts:
   • Encode both texts
   • Compute cosine similarity between the embeddings
3. Find the optimal threshold that maximizes accuracy:
   • Test multiple thresholds from -1 to 1
   • For each threshold, convert similarities to binary predictions
   • Calculate accuracy for each threshold
   • Select threshold with highest accuracy
4. Report optimal threshold and corresponding metrics

The current pair evaluator selects the threshold using the evaluated test labels. These are best-threshold diagnostic scores; a deployment estimate requires selecting the threshold on separate development data. The random baseline also estimates its label distribution from the test labels.

This approach allows us to assess how well the embedding model captures the semantic distinctions necessary for each task.

Adding New Models

To evaluate a new model, simply specify its name or path when running the benchmark:

python run_benchmark.py --model <model_name_or_path>

The model must be compatible with the sentence-transformers library.

Setup

1. Install dependencies:

Using pip

pip install -r requirements.txt

Using conda

# Create a new conda environment
conda create -n implicit_embeddings python=3.11

# Activate the environment
conda activate implicit_embeddings

# Install dependencies
pip install -r requirements.txt

# Optional: deactivate the environment when done
# conda deactivate

2. Download datasets:

# Download PUB dataset
python scripts/download_pub.py

# Download P-Stance dataset
python scripts/download_pstance.py

# Download Social Bias Frames dataset
python scripts/download_sbic.py

# Download Implicit Hate Speech dataset
python scripts/download_implicit_hate.py

Citation

This paper has been accepted to appear in ICML 2026. If you use this benchmark or find our work helpful, please cite:

@article{sun2025text,
  title={Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning},
  author={Sun, Yiqun and Huang, Qiang and Tung, Anthony K. H. and Yu, Jun},
  year={2025},
  url={https://arxiv.org/abs/2506.08354},
  journal={arXiv preprint arXiv:2506.08354},
  eprint={2506.08354},
  archivePrefix={arXiv},
  note={Accepted to ICML 2026 (position paper)}
}

License

MIT License