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@@ -183,6 +183,9 @@ Mor Geva, Roei Schuster, Jonathan Berant, Omer Levy. [[paper](https://arxiv.org
13. **Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space**.(EMNLP 2022)
Mor Geva, Avi Caciularu, Kevin Ro Wang, Yoav Goldberg. [[paper](https://arxiv.org/abs/2203.14680)]
+
+14. **PMET: Precise Model Editing in a Transformer.**
+Xiaopeng Li, Shasha Li, Shezheng Song, Jing Yang, Jun Ma, Jie Yu. [[paper](https://arxiv.org/abs/2308.08742)] [[code](https://github.com/xpq-tech/PMET.git)]
#### More Related Papers
1. **FRUIT: Faithfully Reflecting Updated Information in Text**. (NAACL 2022)
@@ -230,6 +233,7 @@ Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, Mor Geva. [[paper](https://arx
| **Fact Knowledge** | [ZSRE](https://github.com/nicola-decao/KnowledgeEditor), [CounterFact](https://rome.baulab.info/), [CounterFact+](https://arxiv.org/abs/2305.17553),[ECBD](https://github.com/yasumasaonoe/entity_knowledge_propagation) [MQUAKE](https://github.com/yasumasaonoe/entity_knowledge_propagation) |
| **Sentiment** | [Convsent](https://arxiv.org/abs/2206.06520) |
| **Bias** | [Bias in Bios](https://arxiv.org/pdf/2304.00740.pdf) |
+| **Reasoning** | [Eva-KELLM](https://arxiv.org/pdf/2308.09954.pdf) |
| **Toxic Information** | [RealToxicityPrompts](https://arxiv.org/abs/2009.11462) |