Update README.md

README.md

@@ -75,6 +75,12 @@ Breeze-7B-Instruct-64k-v0.1 can solve tasks such as question answering and summa
 | Mistral-7B-v0.1                           | 33.01        | 42.23          | 35.86      | 37.63      |
 
 
+The benchmarks **TMMLU+**, **DRCD**, and **Table** source from [MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2).
+[MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2) derives from [TCEval-v1](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval)
+ and [ikala/tmmluplus](https://huggingface.co/datasets/ikala/tmmluplus). **MMLU** sources from [hails/mmlu_no_train](https://huggingface.co/datasets/hails/mmlu_no_train).
+ We use the code revised from [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate **TMMLU+**, **DRCD**, **Table**, and **MMLU**. 
+ 
+
 ## Chat Model Performance
 
 | Models                                     |        | TMMLU+ (ACC) | TMMLU+ (ACC) | DRCD (EM) | Table (ACC) | MT-Bench-tw (Score) | MMLU (ACC) | MMLU (ACC) | MT-Bench (Score) |
@@ -106,6 +112,13 @@ Breeze-7B-Instruct-64k-v0.1 can solve tasks such as question answering and summa
 | Taiwan-LLM-13B-v2.0-chat                            | 27.74        | 33.69          | 27.03      | 29.43      |
 | Taiwan-LLM-7B-v2.1-chat                             | 25.58        | 31.76          | 27.36      | 27.61      |
 
+The benchmarks **TMMLU+**, **DRCD**, **Table**, and **MT-Bench-tw** source from [MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2).
+[MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2) derives from [TCEval-v1](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval)
+ and [ikala/tmmluplus](https://huggingface.co/datasets/ikala/tmmluplus). **MMLU** sources from [hails/mmlu_no_train](https://huggingface.co/datasets/hails/mmlu_no_train).
+ **MT-Bench** source from [lmsys/mt_bench_human_judgments](https://huggingface.co/datasets/lmsys/mt_bench_human_judgments).
+ We use the code revised from [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate **TMMLU+**, **DRCD**, **Table**, and **MMLU**. 
+ We use the code revised from [fastchat llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) to evaluate **MT-Bench-tw** and **MT-Bench**.
+
 
 ## Inference Performance
 In this test, we use the first 700 characters of the [web article](https://health.udn.com/health/story/5976/7699252?from=udn_ch1005_main_index) as the input and ask the model to write the same article again.