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  ## Results
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- Below, we present the performance of **LlamaLens** , where *"English"* refers to the English-instructed model and *"Native"* refers to the model trained with native language instructions compared to existing SOTA (if available) and the Llama-Instruct baseline, The “Δ” (Delta) column here is
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- calculated as **(LLamalens – SOTA)**.
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  ---
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  ## Arabic
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- | **Task** | **Dataset** | **Metric** | **SOTA** | **Llama3.1-instruct** | **Llamalens-English** | **Llamalens-Native** | **Δ (LLamalens - SOTA)** |
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  |:----------------------------------:|:--------------------------------------------:|:----------:|:--------:|:---------------------:|:---------------------:|:--------------------:|:------------------------:|
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  | Attentionworthiness Detection | CT22Attentionworthy | W-F1 | 0.412 | 0.158 | 0.425 | 0.454 | 0.013 |
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  | Checkworthiness Detection | CT24_checkworthy | F1_Pos | 0.569 | 0.610 | 0.502 | 0.509 | -0.067 |
 
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  ## Results
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+ Below, we present the performance of **L-Lens: LlamaLens** , where *"Eng"* refers to the English-instructed model and *"Native"* refers to the model trained with native language instructions. The results are compared against the SOTA (where available) and the Base: **Llama-Instruct 3.1 baseline**. The **Δ** (Delta) column indicates the difference between LlamaLens and the SOTA performance, calculated as (LlamaLens – SOTA).
 
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  ## Arabic
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+ | **Task** | **Dataset** | **Metric** | **SOTA** | **Base** | **L-Lens-Eng** | **L-Lens-Native** | **Δ (L-Lens (Eng) - SOTA)** |
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  |:----------------------------------:|:--------------------------------------------:|:----------:|:--------:|:---------------------:|:---------------------:|:--------------------:|:------------------------:|
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  | Attentionworthiness Detection | CT22Attentionworthy | W-F1 | 0.412 | 0.158 | 0.425 | 0.454 | 0.013 |
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  | Checkworthiness Detection | CT24_checkworthy | F1_Pos | 0.569 | 0.610 | 0.502 | 0.509 | -0.067 |