add link to new preprint

README.md

@@ -6,7 +6,7 @@ A library for automatic data selection in active fine-tuning of large neural net
 
 Please cite our work if you use this library in your research ([bibtex below](#citation)):
 
-- [Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs]()
+- [Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs](https://arxiv.org/abs/2410.08020)
 - [Transductive Active Learning: Theory and Applications](https://arxiv.org/abs/2402.15898) (Section 4)
 
 ## Installation
@@ -57,7 +57,7 @@ To start a local server hosting the documentation run ```pdoc ./activeft --math`
 	title        = {Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs},
 	author       = {H{\"u}botter, Jonas and Bongni, Sascha and Hakimi, Ido and Krause, Andreas},
 	year         = 2024,
-	journal      = {arXiv Preprint}
+	journal      = {arXiv preprint arXiv:2410.08020}
 }
 
 @inproceedings{hubotter2024transductive,

activeft/__init__.py

@@ -88,7 +88,7 @@
 
 The following example demonstrates how to use `activeft` at "test-time" to obtain a model that is as good as possible on a specific test instance.
 For example, with a language model, this would fine-tune the model for a few gradient steps on data selected specifically for a given prompt.
-We refer to the following paper for more details: [Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs](TODO).
+We refer to the following paper for more details: [Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs](https://arxiv.org/abs/2410.08020).
 
 We can also use the intelligent retrieval of informative and relevant data outside a training loop — for example, for in-context learning and retrieval-augmented generation.
 

activeft/acquisition_functions/lazy_vtl.py

@@ -46,7 +46,7 @@ class LazyVTL(
     """
     Lazy Implementation of [VTL](vtl).[^1]
 
-    See Appendix F.2 of [Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs](TODO).
+    See Appendix F.2 of [Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs](https://arxiv.org/abs/2410.08020).
 
     [^1]: Hübotter, J., Bongni, S., Hakimi, I., and Krause, A. Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs. Preprint, 2024.
     """