While a product-level utility metric [2] functions as an Overall Evaluation Criteria (OEC) to evaluate any feature (LLM-based or otherwise), we also measure usage and engagement with the LLM features directly to isolate its impact on user utility. Below we share the categories of...
How to evaluate a RAG application Before we begin, it is important to distinguish LLM model evaluation from LLM application evaluation. Evaluating LLM models involves measuring the performance of a given model across different tasks, whereas LLM application evaluation is about evaluating different compone...
吴恩达《如何构建、评估和迭代LLM代理|How to Build, Evaluate, and Iterate on LLM Agents》中英字幕 01:02:12 吴恩达《用直接偏好优化对齐LLMs|Aligning LLMs with Direct Preference Optimization》中英字幕 58:07 吴恩达《高效服务大型语言模型|Efficiently Serving LLMs》中英字幕 吴恩达《Mitigating LLM Hallucin...
Language models have become an essential part of the burgeoning field of artificial intelligence (AI) psychology. I discuss 14 methodological considerations that can be used to design more robust, generalizable studies that evaluate the cognitive abiliti
We are now ready to evaluate the models! Which model should we choose? Oracle Loss Functions The main problem of evaluating uplift models is that, even with a validation set and even with a randomized experiment or AB test, we donot observeour metric of interest: the Individual Treatment Eff...
Part 2: How to Evaluate Your LLM Application Part 3: How to Choose the Right Chunking Strategy for Your LLM Application What is an embedding and embedding model? An embedding is an array of numbers (a vector) representing a piece of information, such as text, images, audio, video, etc....
the test fold is then used to evaluate the model performance. After we have identified our “favorite” algorithm, we can follow-up with a “regular” k-fold cross-validation approach (on the complete training set) to find its “optimal” hyperparameters and evaluate it on the independent te...
“How to ensure an LLM produces desired outputs?”“How to prompt a model effectively to achieve accurate responses?” We will also discuss the importance of well-crafted prompts, discuss techniques to fine-tune a model’s behavior and explore approaches to improve output consistency and reduce ...
Assess your model’s performance and make adjustments as needed. If the results are unsatisfactory, explore prompt engineering or furtherFine-tune LLMto align the model’s outputs with human preferences. 4. Evaluate and Iterate Regularly conduct evaluations using metrics and benchmarks. Iterate betwee...
Clone the model from the repo as above, and launch aVLLM serverrunning the model. Then, usegen_api_answer.pyto access the OpenAI-compatible API from VLLM. This might look likepython gen_api_answer.py --model Meta-Llama-3.1-405B --bench-name live_bench --api-base <your endpoint. Oft...