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--- ml:mechanistic_interpretability [2025/05/29 07:11] – [Sparse Autoencoders] jmflanig
+++ ml:mechanistic_interpretability [2025/06/02 11:23] (current) – [Papers] jmflanig
@@ Line 12: / Line 12: @@
   * [[https://arxiv.org/pdf/2211.00593|Wang et al 2022 - Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 Small]]
   * **[[https://arxiv.org/pdf/2304.14997|Conmy et al 2023 - Towards Automated Circuit Discovery for Mechanistic Interpretability]]**
+  * [[https://arxiv.org/pdf/2304.14767|Geva et al 2023 - Dissecting Recall of Factual Associations in Auto-Regressive Language Models]]
   * [[https://arxiv.org/pdf/2305.00586|Hanna et al 2023 - How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model]]
   * [[https://arxiv.org/pdf/2404.14349|Rajaram et al 2024 - Automatic Discovery of Visual Circuits]]
@@ Line 20: / Line 21: @@
     * [[https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html|Olsson et al 2022 - In-context Learning and Induction Heads]] See the first figure for the description of an induction head.
     * [[https://arxiv.org/pdf/2404.07129|Singh et al 2024 - What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation]] See figure 1a for an induction head example.
+    * [[https://arxiv.org/pdf/2504.00132|Bakalova et al 2025 - Contextualize-then-Aggregate: Circuits for In-Context Learning in Gemma-2 2B]]
     * [[https://arxiv.org/pdf/2505.16694|Minegishi et al 2025 - Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence]]
     * [[https://arxiv.org/pdf/2505.20896|Wu et al 2025 - How Do Transformers Learn Variable Binding in Symbolic Programs?]]