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--- |
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dataset_info: |
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features: |
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- name: repo_name |
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dtype: string |
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- name: repo_commit |
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dtype: string |
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- name: repo_content |
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dtype: string |
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- name: repo_readme |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 29227644 |
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num_examples: 158 |
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- name: test |
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num_bytes: 8765331 |
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num_examples: 40 |
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download_size: 12307532 |
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dataset_size: 37992975 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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license: apache-2.0 |
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task_categories: |
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- summarization |
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tags: |
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- code |
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size_categories: |
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- n<1K |
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--- |
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|
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# Generate README Eval |
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|
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The generate-readme-eval is a dataset (train split) and benchmark (test split) to evaluate the effectiveness of LLMs |
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when summarizing entire GitHub repos in form of a README.md file. The datset is curated from top 400 real Python repositories |
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from GitHub with at least 1000 stars and 100 forks. The script used to generate the dataset can be found [here](_script_for_gen.py). |
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For the dataset we restrict ourselves to GH repositories that are less than 100k tokens in size to allow us to put the entire repo |
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in the context of LLM in a single call. The `train` split of the dataset can be used to fine-tune your own model, the results |
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reported here are for the `test` split. |
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|
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To evaluate a LLM on the benchmark we can use the evaluation script given [here](_script_for_eval.py). During evaluation we prompt |
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the LLM to generate a structured README.md file using the entire contents of the repository (`repo_content`). We evaluate the output |
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response from LLM by comparing it with the actual README file of that repository across several different metrics. |
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|
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In addition to the traditional NLP metircs like BLEU, ROUGE scores and cosine similarity, we also compute custom metrics |
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that capture structural similarity, code consistency, readbility ([FRES](https://simple.wikipedia.org/wiki/Flesch_Reading_Ease)) and information retrieval (from code to README). The final score |
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is generated between by taking a weighted average of the metrics. The weights used for the final score are shown below. |
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|
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``` |
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weights = { |
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'bleu': 0.1, |
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'rouge-1': 0.033, |
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'rouge-2': 0.033, |
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'rouge-l': 0.034, |
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'cosine_similarity': 0.1, |
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'structural_similarity': 0.1, |
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'information_retrieval': 0.2, |
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'code_consistency': 0.2, |
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'readability': 0.2 |
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} |
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``` |
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|
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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 |
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leaderboard please create a PR with the log file of the run and details about the model. |
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|
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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. |
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We can validate it by running the evaluation script with `--oracle` flag. |
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The oracle run log is available [here](oracle_results_20240912_155859.log). |
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|
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# Leaderboard |
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|
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The current SOTA model on this benchmark in zero shot setting is **Gemini-1.5-Flash-Exp-0827**. |
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It scores the highest across a number of different metrics. |
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|
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bleu: 0.0072 |
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rouge-1: 0.1196 |
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rouge-2: 0.0169 |
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rouge-l: 0.1151 |
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cosine_similarity: 0.3029 |
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structural_similarity: 0.2416 |
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information_retrieval: 0.4450 |
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code_consistency: 0.0796 |
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readability: 0.3790 |
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weighted_score: 0.2443 |
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|
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| Model | Score | BLEU | ROUGE-1 | ROUGE-2 | ROUGE-l | Cosine-Sim | Structural-Sim | Info-Ret | Code-Consistency | Readability | Logs | |
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|:-----:|:-----:|:----:|:-------:|:-------:|:-------:|:----------:|:--------------:|:--------:|:----------------:|:-----------:|:----:| |
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| 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) | |
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| 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) | |
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| 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) | |
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| 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) | |
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| 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) | |
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| **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) | |
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| 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) | |
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| 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) | |
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|
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## Few-Shot |
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|
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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: |
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|
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1) The average context length required for each item can be up to 100k tokens which makes it out of the reach of most |
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models except Google Gemini which has a context legnth of up to 2 Million tokens. |
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|
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2) There is a trade-off in accuracy inherit in the benchmark as adding more examples makes some of the metrics like `information_retrieval` |
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and `readability` worse. At larger contexts models do not have perfect recall and may miss important information. |
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|
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Our experiments with few-shot prompts confirm this, the maximum overall score is at 1-shot and adding more examples doesn't help after that. |
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|
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| Model | Score | BLEU | ROUGE-1 | ROUGE-2 | ROUGE-l | Cosine-Sim | Structural-Sim | Info-Ret | Code-Consistency | Readability | Logs | |
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|:-----:|:-----:|:----:|:-------:|:-------:|:-------:|:----------:|:--------------:|:--------:|:----------------:|:-----------:|:----:| |
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| 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) | |
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| **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) | |
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| 3-shot-gemini-1.5-flash-exp-0827 | 33.10 | 20.02 | 32.70 | 22.66 | 32.21 | 58.98 | 34.54 | 60.50 | **13.09** | 20.52 | [link](3-shot-gemini-1.5-flash-exp-0827_results_20240912_191049.log) | |
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| 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) | |
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