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@@ -145,6 +145,7 @@ The full [anserini evaluation log](https://huggingface.co/prithivida/Splade_PP_e
 ## 5. Roadmap and future directions for Industry Suitability.
 - **Custom/Domain Finetuning**: OOD Zeroshot performance of SPLADE models is great but unimportant in the industry setting as we need the ability to finetune on custom datasets or domains. Finetuning SPLADE on a new dataset is not cheap and needs labelling of queries and passages.
   So we will continue to see how we can enable economically finetuning our recipe on custom datasets without expensive labelling.
 - **Multilingual SPLADE**: Training cost of SPLADE i.e (GPU budget) directly proportional to Vocab size of the base model, So Mulitlingual SPLADE either using mbert or XLMR can be expensive as they have

 ## 5. Roadmap and future directions for Industry Suitability.
+- **Improve efficiency**: This is a bottomless pit, Will continue to improve serving and retrieval efficiency.
 - **Custom/Domain Finetuning**: OOD Zeroshot performance of SPLADE models is great but unimportant in the industry setting as we need the ability to finetune on custom datasets or domains. Finetuning SPLADE on a new dataset is not cheap and needs labelling of queries and passages.
   So we will continue to see how we can enable economically finetuning our recipe on custom datasets without expensive labelling.
 - **Multilingual SPLADE**: Training cost of SPLADE i.e (GPU budget) directly proportional to Vocab size of the base model, So Mulitlingual SPLADE either using mbert or XLMR can be expensive as they have