Update src/assets/text_content.py

src/assets/text_content.py

@@ -1,202 +1,36 @@
-CHANGELOG_TEXT = f"""
-## [2023-06-19]
-- Added model type column
-- Hid revision and 8bit columns since all models are the same atm
 
-## [2023-06-16]
-- Refactored code base
-- Added new columns: number of parameters, hub likes, license
 
-## [2023-06-13] 
-- Adjust description for TruthfulQA
-
-## [2023-06-12] 
-- Add Human & GPT-4 Evaluations
-
-## [2023-06-05] 
-- Increase concurrent thread count to 40
-- Search models on ENTER
-
-## [2023-06-02] 
-- Add a typeahead search bar
-- Use webhooks to automatically spawn a new Space when someone opens a PR
-- Start recording `submitted_time` for eval requests
-- Limit AutoEvalColumn max-width
-
-## [2023-05-30] 
-- Add a citation button
-- Simplify Gradio layout
-
-## [2023-05-29] 
-- Auto-restart every hour for the latest results
-- Sync with the internal version (minor style changes)
-
-## [2023-05-24] 
-- Add a baseline that has 25.0 for all values
-- Add CHANGELOG
-
-## [2023-05-23] 
-- Fix a CSS issue that made the leaderboard hard to read in dark mode
-
-## [2023-05-22] 
-- Display a success/error message after submitting evaluation requests
-- Reject duplicate submission
-- Do not display results that have incomplete results 
-- Display different queues for jobs that are RUNNING, PENDING, FINISHED status 
-
-## [2023-05-15] 
-- Fix a typo: from "TruthQA" to "QA"
-
-## [2023-05-10] 
-- Fix a bug that prevented auto-refresh
-
-## [2023-05-10] 
-- Release the leaderboard to public
-"""
-
-TITLE = """<h1 align="center" id="space-title">🤗 Open LLM Leaderboard</h1>"""
+TITLE = """<h1 align="center" id="space-title"> KG LLM Leaderboard</h1>"""
 
 INTRODUCTION_TEXT = f"""
-📐 The 🤗 Open LLM Leaderboard aims to track, rank and evaluate LLMs and chatbots as they are released. 
-
-🤗 Anyone from the community can submit a model for automated evaluation on the 🤗 GPU cluster, as long as it is a 🤗 Transformers model with weights on the Hub. We also support evaluation of models with delta-weights for non-commercial licensed models, such as the original LLaMa release.
+🐨 KG LLM Leaderboard aims to track, rank, and evaluate the performance of released Large Language Models on traditional KBQA/KGQA datasets.
 
-Other cool benchmarks for LLMs are developped at HuggingFace, go check them out: 🙋🤖 [human and GPT4 evals](https://huggingface.co/spaces/HuggingFaceH4/human_eval_llm_leaderboard), 🖥️ [performance benchmarks](https://huggingface.co/spaces/optimum/llm-perf-leaderboard)
+The data on this page is sourced from a research paper. If you intend to use the data from this page, please remember to cite the following source: https://arxiv.org/abs/2303.07992
 """
 
 LLM_BENCHMARKS_TEXT = f"""
-# Context
-With the plethora of large language models (LLMs) and chatbots being released week upon week, often with grandiose claims of their performance, it can be hard to filter out the genuine progress that is being made by the open-source community and which model is the current state of the art. 
-
-📈 We evaluate models on 4 key benchmarks from the <a href="https://github.com/EleutherAI/lm-evaluation-harness" target="_blank">  Eleuther AI Language Model Evaluation Harness </a>, a unified framework to test generative language models on a large number of different evaluation tasks. 
-
-- <a href="https://arxiv.org/abs/1803.05457" target="_blank">  AI2 Reasoning Challenge </a> (25-shot) - a set of grade-school science questions.
-- <a href="https://arxiv.org/abs/1905.07830" target="_blank">  HellaSwag </a> (10-shot) - a test of commonsense inference, which is easy for humans (~95%) but challenging for SOTA models.
-- <a href="https://arxiv.org/abs/2009.03300" target="_blank">  MMLU </a>  (5-shot) - a test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more.
-- <a href="https://arxiv.org/abs/2109.07958" target="_blank">  TruthfulQA </a> (0-shot) - a test to measure a model’s propensity to reproduce falsehoods commonly found online. Note: TruthfulQA in the Harness is actually a minima a 6-shots task, as it is prepended by 6 examples systematically, even when launched using 0 for the number of few-shot examples.
-
-For all these evaluations, a higher score is a better score. 
-We chose these benchmarks as they test a variety of reasoning and general knowledge across a wide variety of fields in 0-shot and few-shot settings.
-
-# Some good practices before submitting a model
-
-### 1) Make sure you can load your model and tokenizer using AutoClasses:
-```python
-from transformers import AutoConfig, AutoModel, AutoTokenizer
-config = AutoConfig.from_pretrained("your model name", revision=revision)
-model = AutoModel.from_pretrained("your model name", revision=revision)
-tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
-```
-If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded. 
-
-Note: make sure your model is public!
-Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!
-
-### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
-It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of weights of your model to the `Extended Viewer`!
-
-### 3) Make sure your model has an open license!
-This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗
-
-### 4) Fill up your model card
-When we add extra information about models to the leaderboard, it will be automatically taken from the model card
-
-# Reproducibility and details
-
-### Details and logs
-You can find:
-- detailed numerical results in the `results` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/results
-- details on the input/outputs for the models in the `details` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/details
-- community queries and running status in the `requests` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/requests
-
-### Reproducibility
-To reproduce our results, here is the commands you can run, using [this version](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463) of the Eleuther AI Harness:
-`python main.py --model=hf-causal --model_args="pretrained=<your_model>,use_accelerate=True,revision=<your_model_revision>"`
-` --tasks=<task_list> --num_fewshot=<n_few_shot> --batch_size=2 --output_path=<output_path>`
-
-The total batch size we get for models which fit on one A100 node is 16 (8 GPUs * 2). If you don't use parallelism, adapt your batch size to fit. 
-*You can expect results to vary slightly for different batch sizes because of padding.*
-
-The tasks and few shots parameters are:
-- ARC: 25-shot, *arc-challenge* (`acc_norm`)
-- HellaSwag: 10-shot, *hellaswag* (`acc_norm`)
-- TruthfulQA: 0-shot, *truthfulqa-mc* (`mc2`)
-- MMLU: 5-shot, *hendrycksTest-abstract_algebra,hendrycksTest-anatomy,hendrycksTest-astronomy,hendrycksTest-business_ethics,hendrycksTest-clinical_knowledge,hendrycksTest-college_biology,hendrycksTest-college_chemistry,hendrycksTest-college_computer_science,hendrycksTest-college_mathematics,hendrycksTest-college_medicine,hendrycksTest-college_physics,hendrycksTest-computer_security,hendrycksTest-conceptual_physics,hendrycksTest-econometrics,hendrycksTest-electrical_engineering,hendrycksTest-elementary_mathematics,hendrycksTest-formal_logic,hendrycksTest-global_facts,hendrycksTest-high_school_biology,hendrycksTest-high_school_chemistry,hendrycksTest-high_school_computer_science,hendrycksTest-high_school_european_history,hendrycksTest-high_school_geography,hendrycksTest-high_school_government_and_politics,hendrycksTest-high_school_macroeconomics,hendrycksTest-high_school_mathematics,hendrycksTest-high_school_microeconomics,hendrycksTest-high_school_physics,hendrycksTest-high_school_psychology,hendrycksTest-high_school_statistics,hendrycksTest-high_school_us_history,hendrycksTest-high_school_world_history,hendrycksTest-human_aging,hendrycksTest-human_sexuality,hendrycksTest-international_law,hendrycksTest-jurisprudence,hendrycksTest-logical_fallacies,hendrycksTest-machine_learning,hendrycksTest-management,hendrycksTest-marketing,hendrycksTest-medical_genetics,hendrycksTest-miscellaneous,hendrycksTest-moral_disputes,hendrycksTest-moral_scenarios,hendrycksTest-nutrition,hendrycksTest-philosophy,hendrycksTest-prehistory,hendrycksTest-professional_accounting,hendrycksTest-professional_law,hendrycksTest-professional_medicine,hendrycksTest-professional_psychology,hendrycksTest-public_relations,hendrycksTest-security_studies,hendrycksTest-sociology,hendrycksTest-us_foreign_policy,hendrycksTest-virology,hendrycksTest-world_religions* (`acc` of `all`)
-
-### Quantization
-To get more information about quantization, see:
-- 8 bits: [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration), [paper](https://arxiv.org/abs/2208.07339)
-- 4 bits: [blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes), [paper](https://arxiv.org/abs/2305.14314)
-
-### Icons
-🟢 means that the model is pretrained
-🔶 that it is finetuned
-🟦 that is was trained with RL.
-If there is no icon, we have not uploaded the information on the model yet, feel free to open an issue with the model information!
-
-
-# In case of model failure
-If your model is displayed in the `FAILED` category, its execution stopped. 
-Make sure you have followed the above steps first. 
-If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
+ChatGPT is a powerful large language model (LLM) that
+covers knowledge resources such as Wikipedia and supports natural language question answering using its own knowledge. Therefore, there is
+growing interest in exploring whether ChatGPT can replace traditional
+knowledge-based question answering (KBQA) models. Although there
+have been some works analyzing the question answering performance of
+ChatGPT, there is still a lack of large-scale, comprehensive testing of various types of complex questions to analyze the limitations of the model.
+In this paper, we present a framework that follows the black-box testing specifications of CheckList proposed by Microsoft. We evaluate ChatGPT
+and its family of LLMs on eight real-world KB-based complex question answering datasets, which include six English datasets and two multilingual datasets.
+The total number of test cases is approximately 190,000.
 
 """
 
-EVALUATION_QUEUE_TEXT = f"""
-# Evaluation Queue for the 🤗 Open LLM Leaderboard
-These models will be automatically evaluated on the 🤗 cluster.
-"""
+
 
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
 CITATION_BUTTON_TEXT = r"""
-@misc{open-llm-leaderboard,
-  author = {Edward Beeching, Clémentine Fourrier, Nathan Habib, Sheon Han, Nathan Lambert, Nazneen Rajani, Omar Sanseviero, Lewis Tunstall, Thomas Wolf},
-  title = {Open LLM Leaderboard},
-  year = {2023},
-  publisher = {Hugging Face},
-  howpublished = "\url{https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard}"
-}
-@software{eval-harness,
-  author       = {Gao, Leo and
-                  Tow, Jonathan and
-                  Biderman, Stella and
-                  Black, Sid and
-                  DiPofi, Anthony and
-                  Foster, Charles and
-                  Golding, Laurence and
-                  Hsu, Jeffrey and
-                  McDonell, Kyle and
-                  Muennighoff, Niklas and
-                  Phang, Jason and
-                  Reynolds, Laria and
-                  Tang, Eric and
-                  Thite, Anish and
-                  Wang, Ben and
-                  Wang, Kevin and
-                  Zou, Andy},
-  title        = {A framework for few-shot language model evaluation},
-  month        = sep,
-  year         = 2021,
-  publisher    = {Zenodo},
-  version      = {v0.0.1},
-  doi          = {10.5281/zenodo.5371628},
-  url          = {https://doi.org/10.5281/zenodo.5371628}
-}
-@misc{clark2018think,
-      title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, 
-      author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
-      year={2018},
-      eprint={1803.05457},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI}
-}
-@misc{zellers2019hellaswag,
-      title={HellaSwag: Can a Machine Really Finish Your Sentence?}, 
-      author={Rowan Zellers and Ari Holtzman and Yonatan Bisk and Ali Farhadi and Yejin Choi},
-      year={2019},
-      eprint={1905.07830},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
+
+@article{tan2023evaluation,
+  title={Evaluation of ChatGPT as a question answering system for answering complex questions},
+  author={Tan, Yiming and Min, Dehai and Li, Yu and Li, Wenbo and Hu, Nan and Chen, Yongrui and Qi, Guilin},
+  journal={arXiv preprint arXiv:2303.07992},
+  year={2023}
 }
 @misc{hendrycks2021measuring,
       title={Measuring Massive Multitask Language Understanding}, 
@@ -206,11 +40,4 @@ CITATION_BUTTON_TEXT = r"""
       archivePrefix={arXiv},
       primaryClass={cs.CY}
 }
-@misc{lin2022truthfulqa,
-      title={TruthfulQA: Measuring How Models Mimic Human Falsehoods}, 
-      author={Stephanie Lin and Jacob Hilton and Owain Evans},
-      year={2022},
-      eprint={2109.07958},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}"""
+"""