Common Sense: the Dark Matter of Language and Intelligence (VLDB 2023 Keynote)
Summary: Argues that commonsense knowledge is the “dark matter” missing from scale-driven LLMs, producing brittle, nonsensical errors. Demonstrates that smaller academic models can outperform giant models when augmented with explicit commonsense knowledge and inference‑time reasoning. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yejin Choi
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,384 | Are Large Language Models a Good Replacement of Taxonomies? | 2024 | VLDB | 4.5259772e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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