Use Phoenix Evaluators

The following are simple functions on top of the LLM Evals building blocks that are pre-tested with benchmark datasets.

All evals templates are tested against golden datasets that are available as part of the LLM eval library's benchmarked datasets and target precision at 70-90% and F1 at 70-85%.

Retrieval Eval

RAG individual retrieval

Tested on:

MS Marco, WikiQA

Hallucination Eval

Hallucinations on answers to public and private data

Tested on:

Hallucination QA Dataset, Hallucination RAG Dataset

Toxicity Eval

Is the AI response racist, biased or toxic

Tested on:

WikiToxic

Q&A Eval

Private data Q&A Eval

Tested on:

WikiQA

Summarization Eval

Summarization performance

Tested on:

GigaWorld, CNNDM, Xsum

Code Generation Eval

Code writing correctness and readability

Tested on:

WikiSQL, HumanEval, CodeXGlu

Supported Models.

The models are instantiated and usable in the LLM Eval function. The models are also directly callable with strings.

model = OpenAIModel(model_name="gpt-4",temperature=0.6)
model("What is the largest costal city in France?")

We currently support a growing set of models for LLM Evals, please check out the API section for usage.

Model	Support
GPT-4	✔
GPT-3.5 Turbo	✔
GPT-3.5 Instruct	✔
Azure Hosted Open AI	✔
Palm 2 Vertex	✔
AWS Bedrock	✔
Litellm	✔
Huggingface Llama7B	(use litellm)
Anthropic	✔
Cohere	(use litellm)

How we benchmark pre-tested evals

The above diagram shows examples of different environments the Eval harness is desinged to run. The benchmarking environment is designed to enable the testing of the Eval model & Eval template performance against a designed set of datasets.

The above approach allows us to compare models easily in an understandable format:

Hallucination Eval	GPT-4	GPT-3.5
Precision	0.94	0.94
Recall	0.75	0.71
F1	0.83	0.81