Introduction to frameworks to evaluate your LLM
In today’s digital landscape, large language models (LLMs) are reshaping how we interact with technology, powering everything from search engines to creative writing tools. As their influence grows, ensuring these models are both effective and ethical becomes paramount. This blog post delves into the critical task of evaluating LLMs, emphasizing the need for comprehensive frameworks and metrics.
Evaluating LLMs extends beyond assessing their linguistic capabilities. It involves scrutinizing their operational safety, adherence to privacy standards, accuracy in information dissemination, and commitment to fairness. As LLMs become more sophisticated, their potential to revolutionize education, work, and entertainment is immense. However, this potential comes with the responsibility of mitigating risks related to bias, misinformation, and ethical misuse.
This article aims to introduce a collection of frameworks and metrics specifically crafted to rigorously evaluate LLMs. While the well-established metrics have been detailed in numerous documents, here I provide a concise summary outlining various benchmarks and scoring systems for assessing large language models (LLMs) across five main categories:
- General Knowledge Benchmarks: Focus on the LLM’s breadth of knowledge, crucial for general-purpose chatbots like ChatGPT or Claude. Metrics such as MMLU (Massive Multitask Language Understanding) and TriviaQA are highlighted, assessing knowledge across a wide range of topics through multiple-choice questions and trivia, respectively.
- Logical Reasoning Benchmarks: These benchmarks test the LLM’s depth of understanding and its ability to exhibit logical reasoning, despite being fundamentally next-word predictors. Examples include HellaSwag, assessing commonsense knowledge, and GSM8k, focusing on grade school math problems.
- Coding Benchmarks: Evaluate an LLM’s capability in coding-related tasks, such as debugging and completion. Benchmarks like HumanEval (by OpenAI) and MBPP (Mostly Basic Python Programming by Google Research) are mentioned, requiring the LLM to solve coding problems with specific outputs.
- Homogeneity Scores: Referred to as summarization or similarity scores, these metrics, including BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation), assess the similarity between LLM outputs and human-labeled data, focusing on translation and summarization tasks.
- Standardized Tests: LLM performance is measured against standardized tests taken by humans, such as the Uniform Bar Exam, LSAT, SAT, GRE, and even specialized exams like the Medical Knowledge Self-Assessment Program and the Advanced Sommelier theory knowledge. This category ranges from being a novelty to potentially useful in professional contexts.
Each category serves a unique purpose in evaluating the capabilities and performance of LLMs, from general knowledge and logical reasoning to specific applications in coding, language translation, and professional standards.
HuggingFace
Supposed that you have a finetune model and you want to evaluate this model using huggingface, First you need to create a repository in huggingface hub, and then push your model and tokenizer to the hub with the following commands, you need a huggingface API key to do this.
llm = LLMfull(model_name="phi2")
llm.load(model_id="/media/vankhoa/code/public/phi-2", hf_auth=None, device="cuda:0")
llm.model.push_to_hub("vankhoa/test_phi2", use_auth_token=os.environ["HF_AUTH_TOKEN_WRITE"])
llm.tokenizer.push_to_hub("vankhoa/test_phi2", use_auth_token=os.environAfter pushing models and tokenizer, you will find your model on huggingface hub like this image, and you need to add license and readme file, by clicking Edit model card.
Then you can go to Open LLM Leaderboard and submit your model.
Input your model name, and submit for evaluation, the result with be available after a while (a few days for me).
OpenAI/evals
The openai/evals platform isn’t just another tool; it’s a critical resource crafted to evaluate large language models (LLMs) and the complex infrastructures that integrate them at their core. In a time where passing the Turing Test stands as the pinnacle for AI, guaranteeing thorough evaluation of these models holds greater significance than ever before. Let’s delve into the assessment of LLMs using openai/evals.
A particular tutorial that can guide us in utilizing this library.
EleutherAI/lm-evaluation-harness
This package offers a comprehensive framework designed for evaluating generative language models across a diverse range of tasks.
Key Features:
- Incorporates over 60 standard academic benchmarks for LLMs, encompassing hundreds of subtasks and variants.
- Compatible with models loaded via transformers (including quantization via AutoGPTQ), GPT-NeoX, and Megatron-DeepSpeed, featuring a flexible tokenization-agnostic interface.
- Enables fast and memory-efficient inference with vLLM.
- Supports integration with commercial APIs such as OpenAI and TextSynth.
- Facilitates evaluation on adapters (e.g., LoRA) supported in HuggingFace’s PEFT library.
- Accommodates both local models and benchmarks.
- Promotes reproducibility and comparability across papers through evaluation with publicly available prompts.
- Provides easy customization for prompts and evaluation metrics.
- The Language Model Evaluation Harness serves as the backend for the renowned Open LLM Leaderboard by 🤗 Hugging Face, utilized in numerous academic papers and internally by esteemed organizations including NVIDIA, Cohere, BigScience, BigCode, Nous Research, and Mosaic ML.
This library serves as the backend framework for evaluating Hugging Face’s Language Model Leaderboard (LLM). If you wish to evaluate your model locally without publishing it on Hugging Face, this library provides the tools to do so.
Conclusion
In this tutorial, I’ve presented various frameworks for evaluating your large language model across multiple metrics and datasets. With this knowledge, you can determine which model best suits your application’s needs. Be sure to explore my other blog post on creating a Zotero assistant for your research.
