Update README.md

Add info on evaluation

README.md

@@ -63,25 +63,53 @@ Llama-Krikri-8B-Instruct is the result of post-training Llama-Kriki-8B-Base and
 - Conversion or structured extraction (e.g., XML, JSON) in data-to-text & text-to-data settings.
 - Analytical thinking and Chain-of-Thought (CoT) reasoning for problem-solving.
 
+## Post-training Methodology
+
 We used a multi-stage process in order to build Llama-Krikri-8B-Instruct which includes:
-- 2-stage Supervised Fine-Tuning with a combination of Greek & English instruction-response pairs
+- 2-stage Supervised Fine-Tuning with a combination of Greek & English instruction-response pairs (& multi-turn conversations)
   - **Stage 1**: **856,946** instruction-response pairs (371,379 Greek + 485,567 English)
   - **Stage 2**: **638,408** instruction-response pairs (279,948 Greek + 358,460 English)
-- Alignment with a combination of Greek & English preference triplets
+- Alignment with a combination of Greek & English preference triplets (Instruction - Chosen Response - Rejected Response)
   - **Length Normalized DPO**: **92,394** preference triplets (47,132 Greek + 45,262 English)
- 
+
+## Post-training Data Construction
+
 To build the SFT & DPO data, we utilized various methodologies including:
 - Collecting existing high-quality datasets such as [Tulu 3](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture), [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk), [MAGPIE Ultra](https://huggingface.co/datasets/argilla/magpie-ultra-v1.0), [Orca Agent Instruct](https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1), [IFEval Like Data](https://huggingface.co/datasets/argilla/ifeval-like-data), [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), [NVIDIA HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2), [Intel Orca](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs), [UltraMedical](https://huggingface.co/datasets/TsinghuaC3I/UltraMedical-Preference), and other datasets focused on safety, truthfulness, and instruction-following.
 - Translating various data into Greek using an in-house translation tool.
+- Regenerating translated data and contrasting the translated with the regenerated responses (i.e., for creating preference triplets).
 - Distilling (with the MAGPIE methodology) models which exhibit strong performance in Greek, such as [Gemma 2 27B IT](https://huggingface.co/google/gemma-2-27b-it).
 - Scoring data with the [Skywork Reward Gemma 2 27B v0.2](https://huggingface.co/Skywork/Skywork-Reward-Gemma-2-27B-v0.2) Reward Model and filtering using rule-based filters.
 - Creating data for sentence and document translation using high-quality parallel corpora mainly from [ELRC-SHARE](https://elrc-share.eu/).
-- Synthetically extracting question-answer pairs (RAG) and multi-turn dialogues from diverse sources such as Wikipedia, EUR-LEX, Greek School Books, and Kallipos.
+- Synthetically extracting question-answer pairs and multi-turn dialogues from diverse sources such as Wikipedia, EUR-LEX, Greek School Books, and Kallipos.
 
 # Evaluation
 
+In the table below, we report the scores for [Greek IFEval](https://huggingface.co/datasets/ilsp/ifeval_greek) (strict) and [English IFEval](https://huggingface.co/datasets/google/IFEval) (strict) for various chat models that exhibit strong performance.
+
+We can observe that *Llama-Krikri-8B-Instruct exhibits the strongest performance* in instruction following for both Greek and English across all the models we tested. In particular, it surpasses Llama-3.1-8B-Instruct by **+21.7%** and **+7.3%** on the Greek and English IFEval respectively.
+
+|       | IFEval EL (strict) | IFEval EN (strict) | 
+|---------------- |---------------- |-----------------| 
+| Qwen 2.5 7B Instruct | 46.2% | 74.8% | 
+| EuroLLM 9B Instruct | 51.3% | 64.5% | 
+| Aya Expanse 8B | 50.4% | 62.2% | 
+| Meltemi 7B v1.5 Instruct | 32.7% | 41.2% | 
+| Llama-3.1-8B Instruct | 45.8% | 75.1% | 
+| Llama-Krikri-8B Instruct | **67.5%** | **82.4%** |
+
+
+We also used the [Arena-Hard-Auto](https://huggingface.co/datasets/lmarena-ai/arena-hard-auto-v0.1) automatic evaluation tool, as well the translated (and post-edited) version for Greek that is publicly available [here](https://huggingface.co/datasets/ilsp/m-ArenaHard_greek).
+
+Below, we show the scores for the Greek version of Arena-Hard-Auto for various open and closed chat models that were determined using **gpt-4o-2024-08-06 as the judge model** and **gpt-4o-mini-2024-07-18 as the baseline model** (i.e., by default 50% score).
+![image/png]()
+
+**Please note** that [recent research](https://arxiv.org/pdf/2502.01534?) has shown that judge models are biased towards student models, i.e., models finetuned on distilled data from a stronger/larger teacher model. While post-training data of GPT-4o-Mini are undisclosed, it would be very reasonable to assume that it has been trained -at least partly- with GPT-4o serving as the teacher model and therefore that the **judge is biased towards the baseline model**.
+
+Below, we show the scores for the original Arena-Hard-Auto dataset for various open and closed chat models. We followed the original methodology of using **gpt-4-1106-preview as the judge model** and **gpt-4-0314 as the baseline model**.
+![image/png]()
 
-🚨 **More information on post-training, methdology, and evaluation coming soon.** 🚨
+🚨 **More information on post-training, methodology, and evaluation coming soon.** 🚨
 
 
 # How to use