README.md

metadata

license: cc-by-4.0
pretty_name: Olfactory Perception (OP) Benchmark
language:
  - en
  - ar
  - bn
  - de
  - el
  - es
  - fa
  - fr
  - hi
  - it
  - ja
  - ko
  - pl
  - pt
  - ru
  - sv
  - sw
  - tr
  - uk
  - zh
task_categories:
  - question-answering
tags:
  - olfaction
  - smell
  - chemistry
  - SMILES
  - benchmark
  - llm-evaluation
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: test
        path: Benchmark/OP_Benchmark.csv
dataset_info:
  config_name: default
  features:
    - name: question_ID
      dtype: string
    - name: compound.name_1
      dtype: string
    - name: SMILES_1
      dtype: string
    - name: compound.name_2
      dtype: string
    - name: SMILES_2
      dtype: string
    - name: OPTIONS
      dtype: string
    - name: question_category
      dtype: string
    - name: prompt.1
      dtype: string
    - name: prompt.2
      dtype: string
    - name: answer
      dtype: string
    - name: other_info
      dtype: string

Olfactory Perception (OP) Benchmark

The OP benchmark evaluates what large language models know about smell. It contains 1,010 questions across eight task categories, each asked in two formats: with compound names and with isomeric SMILES.

Task	Code	Questions	Format
Odor classification	OC	175	Odorous / odorless
Odor primary descriptor	OPD	175	1 of 4 descriptors
Odor intensity	OIn	175	Which of two compounds is more intense (+ 0–100 ratings)
Odor pleasantness	OPl	175	Which of two compounds is more pleasant (+ 0–100 ratings)
Rate-all-that-apply	RATA	100	All applicable descriptors from 138
Odor similarity of mixtures	OS	100	4-level similarity (+ 0–1 distance)
Olfactory receptor activation	ORA	80	All activated receptors from 4–10 candidates
Smell identification test	SIT	30	1 of 4 odor sources

The release also includes the RATA task translated into 20 further languages (Benchmark/4-Intermediate_1/Translations_GPT5.2/).

• Paper: Benchmark for Assessing Olfactory Perception of Large Language Models, NeurIPS 2026 (Evaluations and Datasets Track)
• Code, prompts, evaluation and analysis scripts: https://github.com/Satarifard/Olfactory-Perception-Benchmark
• Authors: Eftychia Makri (Yale University), Nikolaos Nakis (Yale University), Laura Sisson (Patina), Geetanjali Minsky (MIT Media Lab), Leandros Tassiulas (Yale University), Vahid Satarifard (Yale University), Nicholas A. Christakis (Yale University)
• Contact: eftychia.makri@yale.edu, vahid.satarifard@yale.edu

Columns

Column	Description
question_ID	Unique question identifier (UUID)
question_category	odor_classification, primary_odor_descriptor, odor_intensity, odor_pleasantness, rata, mixture_similarity, or_activation, or smell_identification
compound.name_1, SMILES_1	First compound, or first mixture (semicolon-separated)
compound.name_2, SMILES_2	Second compound or mixture (paired and mixture tasks only)
OPTIONS	Answer options (semicolon-separated)
prompt.1	Full prompt with isomeric SMILES
prompt.2	Full prompt with compound names
answer	Ground-truth answer (semicolon-separated for RATA and ORA)
other_info	Supplementary values, e.g. human ratings for OIn/OPl

Sources

Questions are built from: Mayhew et al. 2022 (OC); the IFRA Fragrance Ingredient Glossary, 2020 (OPD); Keller et al. 2017 (OIn, OPl); GS-LF as used by Lee et al. 2023 (RATA); Snitz et al. 2013, Bushdid et al. 2014, and Ravia et al. 2020 (OS); M2OR, Lalis et al. 2024 (ORA); and the Leibniz-LSB@TUM Odorant Database, Kreissl et al. 2022 (SIT). Please cite the original sources as well when using the corresponding tasks.

License

CC BY 4.0.

Citation

@inproceedings{makri2026op,
  title     = {Benchmark for Assessing Olfactory Perception of Large Language Models},
  author    = {Makri, Eftychia and Nakis, Nikolaos and Sisson, Laura and Minsky, Geetanjali and Tassiulas, Leandros and Satarifard, Vahid and Christakis, Nicholas A.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}