# GD-COMET: A Geo-Diverse Commonsense Inference Model

arXiv: https://arxiv.org/abs/2310.15383 · HTML: https://mlsanity.com/paper/2310.15383/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 25 of 25)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019)
- [From Recognition to Cognition: Visual Commonsense Reasoning](https://mlsanity.com/paper/1811.10830/) (2018)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [Visually Grounded Reasoning across Languages and Cultures](https://mlsanity.com/paper/2109.13238/) (2021)
- [Social Biases in NLP Models as Barriers for Persons with Disabilities](https://mlsanity.com/paper/2005.00813/) (2020)
- [Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://mlsanity.com/paper/2203.13722/) (2022)
- [Extracting Cultural Commonsense Knowledge at Scale](https://mlsanity.com/paper/2210.07763/) (2022)
- [Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning](https://mlsanity.com/paper/2106.06937/) (2021)
- [Measuring Social Biases in Grounded Vision and Language Embeddings](https://mlsanity.com/paper/2002.08911/) (2020)
- [Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning](https://mlsanity.com/paper/2109.06860/) (2021)
- [Automated Storytelling via Causal, Commonsense Plot Ordering](https://mlsanity.com/paper/2009.00829/) (2020)
- [R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge](https://mlsanity.com/paper/2004.13248/) (2020)
- [Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources](https://mlsanity.com/paper/2103.11320/) (2021)
- [Towards an Atlas of Cultural Commonsense for Machine Reasoning](https://mlsanity.com/paper/2009.05664/) (2020)
- [Commonsense Properties from Query Logs and Question Answering Forums](https://mlsanity.com/paper/1905.10989/) (2019)
- [Paragraph-level Commonsense Transformers with Recurrent Memory](https://mlsanity.com/paper/2010.01486/) (2020)
- [StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes](https://mlsanity.com/paper/2205.14036/) (2022)
- [GIVL: Improving Geographical Inclusivity of Vision-Language Models with Pre-Training Methods](https://mlsanity.com/paper/2301.01893/) (2023)
- [COMET-M: Reasoning about Multiple Events in Complex Sentences](https://mlsanity.com/paper/2305.14617/) (2023)

## Built on by (most cited first, 13 of 13)

- [Towards Measuring and Modeling "Culture" in LLMs: A Survey](https://mlsanity.com/paper/2403.15412/) (2024)
- [Culture is Not Trivia: Sociocultural Theory for Cultural NLP](https://mlsanity.com/paper/2502.12057/) (2025)
- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [Cultural Commonsense Knowledge for Intercultural Dialogues](https://mlsanity.com/paper/2402.10689/) (2024)
- [From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models](https://mlsanity.com/paper/2407.00263/) (2024)
- [A survey of diversity quantification in natural language processing: The why, what, where and how](https://mlsanity.com/paper/2507.20858/) (2025)
- [Steering LLMs for Culturally Localized Generation](https://mlsanity.com/paper/2603.23301/) (2026)
- [Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs](https://mlsanity.com/paper/2604.19292/) (2026)
- [Every Token Counts: Exact Likert-Scale Distributions for Measuring LLM Attitudes and Biases](https://mlsanity.com/paper/2608.10503/) (2026)
- [Arguments that Alter Minds: LLM Rationales Sway Human (and LLM) Notions of Plausibility](https://mlsanity.com/paper/2510.08091/) (2025)
- [Speaking the Right Language: The Impact of Expertise Alignment in User-AI Interactions](https://mlsanity.com/paper/2502.18685/) (2025)
- [ALIGN: Word Association Learning for Cultural Alignment in Large Language Models](https://mlsanity.com/paper/2508.13426/) (2025)
- [LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction](https://mlsanity.com/paper/2601.17971/) (2026)

## Abstract (arXiv)

With the increasing integration of AI into everyday life, it's becoming crucial to design AI systems that serve users from diverse backgrounds by making them culturally aware. In this paper, we present GD-COMET, a geo-diverse version of the COMET commonsense inference model. GD-COMET goes beyond Western commonsense knowledge and is capable of generating inferences pertaining to a broad range of cultures. We demonstrate the effectiveness of GD-COMET through a comprehensive human evaluation across 5 diverse cultures, as well as extrinsic evaluation on a geo-diverse task. The evaluation shows that GD-COMET captures and generates culturally nuanced commonsense knowledge, demonstrating its potential to benefit NLP applications across the board and contribute to making NLP more inclusive.