Update README.md

README.md

@@ -66,10 +66,8 @@ MERaLiON-AudioLLM is trained to mainly address 6 tasks, namely `Automatic Speech
 `Spoken Dialogue Summarization` (SDS), `Speech Instruction` (SI), and `Paralinguistics` (PARA). 
 
 We benchmark MERaLiON-AudioLLM with a series of test sets from the [AudioBench benchmark](https://github.com/AudioLLMs/AudioBench) 
-against three well-known AudioLLMs: `Qwen2-Audio 7B`, `WavLLM`, and `SALMONN`. We also compared with a cascaded model, 
-which feeds the transcriptions recognized by Whisper-large-v2 and the instruction prompts to a Gemma2 9B CPT SEA-LIONv3 Instruct model to
-get the responses. We tuned its hyperparameters and prompt template to optimise performance across
-various speech-to-text tasks. As is shown in the following table, MERaLiON-AudioLLM performs better in the Singapore local context, 
+against three well-known AudioLLMs: `Qwen2-Audio 7B`, `WavLLM`, `SALMONN`, and a cascaded model. 
+As is shown in the following table, MERaLiON-AudioLLM performs better in the Singapore local context, 
 as evidenced by evaluation results on Singapore's [Multitask National Speech Corpus](https://huggingface.co/datasets/MERaLiON/Multitask-National-Speech-Corpus-v1) (MNSC) datasets. 
 
 > [!NOTE]