Update README.md

README.md

@@ -68,18 +68,63 @@ weights = {
 At the end of evaluation the script will print the metrics and store the entire run in a log file. If you want to add your model to the 
 leaderboard please create a PR with the log file of the run and details about the model.
 
-If we use the existing README.md files in the repositories as the golden output, we would get a score of 56.6 on this benchmark. 
+If we use the existing README.md files in the repositories as the golden output, we would get a score of 56.79 on this benchmark. 
 We can validate it by running the evaluation script with `--oracle` flag.
 The oracle run log is available [here](oracle_results_20240912_155859.log).
 
 # Leaderboard
 
+The current SOTA model on this benchmark in zero shot setting is **Gemini-1.5-Flash-Exp-0827**. 
+It scores the highest across a number of different metrics.
+
+bleu: 0.0072
+rouge-1: 0.1196
+rouge-2: 0.0169
+rouge-l: 0.1151
+cosine_similarity: 0.3029
+structural_similarity: 0.2416
+information_retrieval: 0.4450
+code_consistency: 0.0796
+readability: 0.3790
+weighted_score: 0.2443
+
 | Model | Score | BLEU | ROUGE-1 | ROUGE-2 | ROUGE-l | Cosine-Sim | Structural-Sim | Info-Ret | Code-Consistency | Readability | Logs |  
 |:-----:|:-----:|:----:|:-------:|:-------:|:-------:|:----------:|:--------------:|:--------:|:----------------:|:-----------:|:----:|
+| llama3.1-8b-instruct | 24.43 | 0.72 | 11.96 | 1.69 | 11.51 | 30.29 | 24.16 | 44.50 | 7.96 | 37.90 | [link](llama3.1-8b-instruct-fp16_results_20240912_185437.log) |
 | mistral-nemo-instruct-2407 | 25.62 | 1.09 | 11.24 | 1.70 | 10.94 | 26.62 | 24.26 | 52.00 | **8.80** | 37.30 | [link](mistral-nemo-12b-instruct-2407-fp16_results_20240912_182234.log) |
 | gpt-4o-mini-2024-07-18 | 32.16 | 1.64 | 15.46 | 3.85 | 14.84 | 40.57 | 23.81 | 72.50 | 4.77 | 44.81 | [link](gpt-4o-mini-2024-07-18_results_20240912_161045.log) | 
 | gpt-4o-2024-08-06 | 33.13 | 1.68 | 15.36 | 3.59 | 14.81 | 40.00 | 23.91 | 74.50 | 8.36 | 44.33 | [link](gpt-4o-2024-08-06_results_20240912_155645.log) | 
 | gemini-1.5-flash-8b-exp-0827 | 32.12 | 1.36 | 14.66 | 3.31 | 14.14 | 38.31 | 23.00 | 70.00 | 7.43 | **46.47** | [link](gemini-1.5-flash-8b-exp-0827_results_20240912_134026.log) | 
 | **gemini-1.5-flash-exp-0827** | **33.43** | 1.66 | **16.00** | 3.88 | **15.33** | **41.87** | 23.59 | **76.50** | 7.86 | 43.34 | [link](gemini-1.5-flash-exp-0827_results_20240912_144919.log) | 
 | gemini-1.5-pro-exp-0827 | 32.51 | **2.55** | 15.27 | **4.97** | 14.86 | 41.09 | **23.94** | 72.82 | 6.73 | 43.34 | [link](gemini-1.5-pro-exp-0827_results_20240912_141225.log) | 
-| oracle-score | 56.79 | 100.00 | 100.00  | 100.00  | 100.00  | 100.00  | 98.24 | 59.00 | 11.01 | 14.84 | [link](oracle_results_20240912_155859.log) | 
+| oracle-score | 56.79 | 100.00 | 100.00  | 100.00  | 100.00  | 100.00  | 98.24 | 59.00 | 11.01 | 14.84 | [link](oracle_results_20240912_155859.log) | 
+
+## Few-Shot
+
+This benchmark is interesting because it is not that easy to few-shot your way to improve performance. There are couple of reasons for that:
+
+1) The average context length required for each item can be up to 100k tokens which makes it out of the reach of most
+models except Google Gemini which has a context legnth of up to 2 Million tokens.
+
+2) There is a trade-off in accuracy inherit in the benchmark as adding more examples makes some of the metrics like `information_retrieval`
+and `readability` worse. At larger contexts models do not have perfect recall and may miss important information. 
+
+Our experiments with few-shot prompts confirm this, there is 1
+
+bleu: 0.1924
+rouge-1: 0.3231
+rouge-2: 0.2148
+rouge-l: 0.3174
+cosine_similarity: 0.6149
+structural_similarity: 0.3317
+information_retrieval: 0.5950
+code_consistency: 0.1148
+readability: 0.2765
+weighted_score: 0.3397
+
+| Model | Score | BLEU | ROUGE-1 | ROUGE-2 | ROUGE-l | Cosine-Sim | Structural-Sim | Info-Ret | Code-Consistency | Readability | Logs |  
+|:-----:|:-----:|:----:|:-------:|:-------:|:-------:|:----------:|:--------------:|:--------:|:----------------:|:-----------:|:----:|
+| 0-shot-gemini-1.5-flash-exp-0827 | 33.43 | 1.66 | 16.00 | 3.88 | 15.33 | 41.87 | 23.59 | 76.50 | 7.86 | 43.34 | [link](gemini-1.5-flash-exp-0827_results_20240912_144919.log) | 
+| 1-shot-gemini-1.5-flash-exp-0827 | 35.40 | 21.81 | 34.00 | 24.97 | 33.61 | 61.53 | 37.60 | 61.00 | 12.89 | 27.22 | [link](1-shot-gemini-1.5-flash-exp-0827_results_20240912_183343.log) | 
+| 3-shot-gemini-1.5-flash-exp-0827 | 33.43 | 1.66 | 16.00 | 3.88 | 15.33 | 41.87 | 23.59 | 76.50 | 7.86 | 43.34 | [link](gemini-1.5-flash-exp-0827_results_20240912_144919.log) | 
+| 5-shot-gemini-1.5-flash-exp-0827 | 33.97 | 19.24 | 32.31 | 21.48 | 31.74 | 61.49 | 33.17 | 59.50 | 11.48 | 27.65 | [link](5-shot-gemini-1.5-flash-exp-0827_results_20240912_180343.log) |