2 ways to Assessing and Evaluating LLM Outputs: Ensuring Relevance, Accuracy, and Coherence of LLMs

Table of Contents

• Assessing LLM Outputs
  • Relevance
  • Factual Accuracy
  • Coherence
• Tools and Frameworks for Evaluation
  • LangSmith
  • OpenAI Evals
  • Other Platforms
• Code Demonstrations
  • Evaluating with LangSmith
  • Using OpenAI Evals
• Conclusion

Assessing LLM Outputs

Relevance

• Semantic Similarity Metrics: Use metrics like cosine similarity with embedding vectors to measure how closely the output relates to the input.
• Keyword Matching: Identify essential keywords in the input and check for their presence in the output.
• Contextual Evaluation: Ensure the response is appropriate within the given context, especially in multi-turn conversations.

Factual Accuracy

• Automated Fact-Checking: Integrate fact-checking APIs or databases to verify factual statements.
• Prompt Engineering: Instruct the model to cite sources or express uncertainty when unsure.
• Human Review: Have subject matter experts review outputs for accuracy.

Coherence

• Language Quality Metrics: Use metrics like perplexity to assess the fluency of the text.
• Structural Analysis: Check for logical progression in arguments and consistency in narratives.
• Discourse Markers: Ensure the appropriate use of conjunctions and transitional phrases.

Tools and Frameworks for Evaluation

LangSmith

• Experiment Tracking: Keep track of different prompts and parameters.
• Evaluation Suite: Automate the evaluation of LLM outputs against benchmarks.
• Observability: Monitor the performance and behavior of LLMs in real-time.

OpenAI Evals

• Custom Evaluations: Create evaluations tailored to specific use cases.
• Automated Metrics: Use built-in metrics or define custom ones.
• Community Sharing: Share and use evaluations created by others.

Other Platforms

• Galileo: Focuses on data-centric AI, helping to identify data issues affecting model performance.
• Truera: Provides model intelligence and observability, focusing on explainability and fairness.
• Traceloop: Offers monitoring and debugging tools for machine learning pipelines.

While I’ve primarily worked with LangSmith and OpenAI Evals, platforms like Galileo, Truera, and Traceloop also offer valuable features for LLM evaluation and observability.



Code Demonstrations

Evaluating with LangSmith

Let’s walk through how to use LangSmith to evaluate LLM outputs. Setup First, install the necessary packages:

pip install langchain langsmith

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Initializing LangSmith Client

from langchain.client import LangChainPlusClient

# Initialize the LangSmith client

client = LangChainPlusClient(api_url="https://api.smith.langchain.com")

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Creating an LLM and Chain

from langchain.llms import OpenAI

from langchain.chains import LLMChain

from langchain.prompts import PromptTemplate

# Define the LLM

llm = OpenAI(temperature=0)

# Create a prompt template

prompt = PromptTemplate(template="Translate '{text}' to French.", input_variables=["text"])

# Create an LLMChain

chain = LLMChain(llm=llm, prompt=prompt)

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Running the Chain and Logging Results

# Test data

inputs = [{"text": "Hello, world!"}, {"text": "Good morning"}]

# Run the chain and log outputs

for input_data in inputs:

output = chain.run(input_data)

# Log the input and output to LangSmith

client.log_run(

chain_id=chain.chain_id,

inputs=input_data,

outputs={"translation": output}

)

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Evaluating Outputs LangSmith allows you to evaluate outputs using built-in or custom evaluators.

from langchain.evaluation import load_evaluator

# Load a predefined evaluator

evaluator = load_evaluator("translation_accuracy")

# Evaluate the outputs

for input_data in inputs:

output = chain.run(input_data)

evaluation = evaluator.evaluate(input_data["text"], output)

print(f"Input: {input_data['text']}")

print(f"Output: {output}")

print(f"Evaluation: {evaluation}")

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Using OpenAI Evals

OpenAI Evals enables you to evaluate model outputs systematically. Setup

pip install openai-evals

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Creating a Custom Evaluation

import openai

import openai_evals

# Initialize OpenAI API key

openai.api_key = 'your-api-key'

# Define prompts and references

prompts = ["What is the capital of France?"]

references = ["Paris is the capital of France."]

# Create a custom eval class

class CustomEval(openai_evals.Eval):

def __init__(self, *args, **kwargs):

super().__init__(*args, **kwargs)

def eval_example(self, example):

prompt = example["prompt"]

reference = example["reference"]

# Get model response

response = openai.Completion.create(

engine="text-davinci-003",

prompt=prompt,

max_tokens=50

)

result = response.choices[0].text.strip()

# Compare with reference

score = self.compute_score(reference, result)

return {"result": result, "score": score}

def compute_score(self, reference, result):

# Simple string comparison for demonstration

return int(reference.lower() == result.lower())

# Run the evaluation

eval = CustomEval()

for prompt, reference in zip(prompts, references):

example = {"prompt": prompt, "reference": reference}

evaluation = eval.eval_example(example)

print(f"Prompt: {prompt}")

print(f"Model Output: {evaluation['result']}")

print(f"Score: {evaluation['score']}")

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Note: Replace ‘your-api-key’ with your actual OpenAI API key.

Conclusion

Assessing the relevance, factual accuracy, and coherence of LLM outputs is essential for building reliable applications. Tools like LangSmith and OpenAI Evals provide robust frameworks for evaluating and monitoring LLM performance. While each platform has its strengths, combining automated tools with human evaluation often yields the best results. By systematically evaluating LLM outputs, we can enhance their quality and ensure they meet the desired standards.

References
• LangSmith Documentation
• OpenAI Evals GitHub Repository
• LangChain GitHub Repository