Update papers.bib

_bibliography/papers.bib

@@ -21,7 +21,7 @@ @inproceedings{LMCor
       title={Small Language Models Improve Giants by Rewriting Their Outputs}, 
       author={G. Vernikos and A. Bražinskas and J. Adamek and J. Mallinson and A. Severyn and E. Malmi},
       booktitle = "EACL",
-      year={2023},
+      year={2024},
       abstract = {Large language models (LLMs) have demonstrated impressive few-shot learning capabilities, but they often underperform compared to fine-tuned models on challenging tasks. 
         Furthermore, their large size and restricted access only through APIs make task-specific fine-tuning impractical. 
         Moreover, LLMs are sensitive to different aspects of prompts (e.g., the selection and order of demonstrations) and can thus require time-consuming prompt engineering.