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--- trans_learn:reading_group_2024_spring [2024/04/10 06:23] – chekaki
+++ trans_learn:reading_group_2024_spring [2024/07/17 05:08] (current) – chekaki
@@ Line 9: / Line 9: @@
 [[https://docs.qq.com/sheet/DQ0xsY1p2TEFoVXNo?tab=fn20u6 | 2024 Spring Reading Group Schdule]]
+===== Topic =====
+We will focus on the following topics for this year's reading group
+  * Efficient adaptation of LLM & foundation models cross domains and tasks
+  * Learning to transfer for continuous adaptation  (with a focus on domain dynamics learning)
+  * Impactful application of above topics in the medical domain
+The selected paper should have been a seminal work in its filed or a representative of the state-of-the-arts that made large impact. This rule can be relaxed if the paper is highly relevant to our current line of research. We would also prefer works that are well-written with good theoretical support.
 -----------------------------
 ===03/13/2024===
   * Presenter: Xuechao Chen
-  * Paper:
+  * Paper: LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS
-  * Slides:
+  * Slides: {{ :trans_learn:paperreading_lora.pptx |}}
 ===03/27/2024===
@@ Line 26: / Line 35: @@
   * Slides:{{ :trans_learn:reading_group_240410.pptx |}}
-===== Topic =====
-We will focus on the following topics for this year's reading group
-  * Efficient adaptation of LLM & foundation models cross domains and tasks
+===05/15/2024===
-  * Learning to transfer for continuous adaptation  (with a focus on domain dynamics learning)
+  * Presenter: Xiangyu Chen
-  * Impactful application of above topics in the medical domain
+  * Paper: Merging by Matching Models in Task Parameter Subspaces
+  * Slides:{{ :trans_learn:model_merging_240515.pptx |}}
-The selected paper should have been a seminal work in its filed or a representative of the state-of-the-arts that made large impact. This rule can be relaxed if the paper is highly relevant to our current line of research. We would also prefer works that are well-written with good theoretical support.
+===06/19/2024===
+  * Presenter: Haohua Wang
+  * Paper: Direct Preference Optimization: Your Language Model is Secretly a Reward Model
+  * Slides:{{ :|}}
+-----------------------------
 ===== Format =====