Developing Custom Evaluation Tasks

While standard benchmarks like GLUE and SuperGLUE provide valuable points of comparison, they often paint an incomplete picture of an LLM's performance in real-world, specialized applications. Your specific use case might involve a unique domain, a novel interaction pattern, or require capabilities not thoroughly tested by existing public datasets. In such scenarios, developing custom evaluation tasks becomes not just beneficial, but essential for understanding if your model truly meets the required performance standards. This section details the process of designing, implementing, and analyzing these tailored evaluations.

Defining the Evaluation Goal

Before writing a single line of code or collecting any data, the first step is to articulate precisely what you need to measure. Standard benchmarks often assess general linguistic competence or performance on well-established NLP tasks. Custom evaluations, however, typically target more specific behaviors or knowledge pertinent to your application. Ask yourself:

• What specific capability is critical for my application? Is it summarizing internal legal documents accurately? Generating syntactically correct SQL queries based on natural language requests? Maintaining a consistent, empathetic persona in a customer service chatbot? Answering questions based on a proprietary knowledge base?
• What does "good performance" look like for this capability? This requires moving past generic ideas of quality. Define concrete success criteria. For SQL generation, success might mean the query is executable and returns the correct data. For summarization, it might involve including specific entities or adhering to a length constraint.
• How does this differ from existing benchmarks? Understanding this gap clarifies the unique value your custom evaluation provides.

Clarity at this stage is important. A vague goal like "evaluate if the model is good at finance" is difficult to act upon. A specific goal like "evaluate the model's ability to extract the 'total revenue' figure from quarterly earnings reports with >95% accuracy" provides a clear target for task design and metric development.

Designing the Task Format

Once the goal is clear, you need to design the task format that will elicit the desired behavior from the model. The format should mirror how the model will be used in production as closely as possible. Common formats include:

• Classification: Assigning predefined labels to inputs (e.g., classifying customer feedback as "Positive", "Negative", "Neutral"; identifying the intent behind a user query).
• Extraction: Identifying and pulling specific pieces of information from text (e.g., extracting names, dates, and locations from news articles; pulling important terms from research papers).
• Generation: Producing text based on a prompt (e.g., writing email drafts, generating code documentation, summarizing meetings).
• Question Answering (QA): Answering questions based on provided context or internal knowledge (e.g., answering FAQs based on company policies, querying a technical manual).
• Ranking: Ordering a set of items based on relevance or preference (e.g., ranking search results, ordering product recommendations).
• Dialogue: Simulating a multi-turn conversation to assess coherence, helpfulness, or task completion ability.

Consider the input your model will receive and the output you expect. For instance, if evaluating the model's ability to follow complex instructions, the task might involve providing a detailed prompt outlining constraints and desired outputs, then assessing the generated text against those constraints.

Data Collection and Curation

The quality of your custom evaluation hinges directly on the quality of the evaluation data.

• Sources: Data can come from various places. Production logs might offer realistic examples of user interactions. Domain experts can craft high-quality examples specific to their field. If real data is scarce, synthetic data generation (potentially using another LLM with careful review) can be an option, though it carries the risk of introducing biases or artifacts.
• Annotation: If your task requires human judgment (e.g., rating the helpfulness of a response, identifying if a summary captures the main points), you'll need clear annotation guidelines. These guidelines should precisely define the labels or scores, provide examples of edge cases, and aim to minimize ambiguity. Invest time in training annotators and measuring inter-annotator agreement (IAA) to ensure consistency. Tools like Cohen's Kappa ($\kappa$) or Fleiss' Kappa can quantify IAA. High IAA suggests your guidelines are clear and the task is well-defined.
• Gold Standard: Establish ground truth or "gold standard" answers/labels for your evaluation set. For classification or extraction, this is usually straightforward. For generative tasks, it's more complex. There might be multiple valid ways to summarize a document or answer a question. In these cases, your gold standard might include multiple acceptable references, or your evaluation metric might need to account for this variability.
• Dataset Size: The size required depends on the task and desired statistical significance. Even a smaller, high-quality dataset (e.g., 100-500 carefully curated examples) can be highly informative for identifying specific failure modes, though larger sets are better for quantitative analysis. Ensure your dataset covers a diverse range of inputs and potential challenges.

Developing Evaluation Metrics

Standard metrics like accuracy, F1-score, BLEU, or ROUGE can be starting points, but they often fail to capture the nuances of custom tasks. You frequently need to develop bespoke metrics aligned with your specific evaluation goal.

• Rule-Based Metrics: These involve programmatic checks based on predefined rules. They are useful for assessing adherence to format, inclusion of required elements, or avoidance of forbidden content.

  • Example: Checking if a generated API call contains the correct function name and required parameters.
  • Example: Verifying if a summary is within a specified word count range.

  import re
  
  def check_report_format(generated_text: str) -> bool:
      """Checks if the generated text includes a 'Summary:' section
         and a 'Recommendations:' section."""
      has_summary = bool(re.search(r"Summary:", generated_text, re.IGNORECASE))
      has_recommendations = bool(re.search(r"Recommendations:", generated_text, re.IGNORECASE))
      return has_summary and has_recommendations
  
  # Example Usage:
  report = """
  Analysis Complete.
  Summary: Sales increased by 10%.
  Recommendations: Invest in marketing.
  """
  is_valid_format = check_report_format(report)
  print(f"Report format valid: {is_valid_format}") # Output: Report format valid: True
  

• Model-Based Metrics: Leverage other models (potentially smaller, specialized ones) to evaluate the output.

  • Example: Using a toxicity classifier to score the safety of generated dialogue.
  • Example: Using another LLM or an embedding model to assess the semantic similarity between a generated answer and a gold standard answer, going past lexical overlap (like BLEU/ROUGE).
  • Example: Using a code analysis tool to check generated code for syntax errors or vulnerabilities.

• Human Evaluation: Indispensable when assessing subjective qualities like helpfulness, coherence, creativity, factual correctness (especially for information outside the model's training data), or adherence to a specific tone/persona. Designing a good human evaluation requires:

  • Clear Rubrics: Define specific criteria and scoring scales (e.g., Likert scales from 1-5 for helpfulness, binary judgments for factual accuracy).
  • Comparative Evaluation: Often, asking humans to compare two outputs (e.g., from Model A vs. Model B) and choose the better one is easier and more reliable than assigning absolute scores.
  • Blinding: Ensure evaluators don't know which model produced which output to avoid bias.

Implementation and Execution

With the task defined, data collected, and metrics chosen, you need to build the evaluation pipeline.

• Input/Output Handling: Write code to format your evaluation data into prompts suitable for the model and parse the model's generated output.
• Model Inference: Integrate with your model serving system or inference library to run the model on the evaluation dataset.
• Metric Calculation: Implement the logic for your custom metrics (rule-based, model-based, or processing human judgments).
• Automation: Automate the pipeline as much as possible to allow for efficient re-evaluation as the model is updated.

Here's a simplified structure using PyTorch for running evaluation:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Assume custom_data_loader yields (prompt, gold_reference) pairs
# Assume custom_metric_function(generated_text, gold_reference) -> score

def run_custom_evaluation(model_name, custom_data_loader, custom_metric_function):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    model.eval() # Set model to evaluation mode

    results = []
    total_score = 0.0
    num_samples = 0

    with torch.no_grad(): # Disable gradient calculations for inference
        for prompt, gold_reference in custom_data_loader:
            inputs = tokenizer(prompt, return_tensors="pt").to(device)

            # Generate output (adjust parameters as needed)
            outputs = model.generate(
                **inputs,
                max_new_tokens=100,
                pad_token_id=tokenizer.eos_token_id
            )
            generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
            # Extract only the generated part if needed
            generated_response = generated_text[len(prompt):]

            # Apply custom metric
            score = custom_metric_function(generated_response, gold_reference)
            results.append({
                "prompt": prompt,
                "generated": generated_response,
                "gold": gold_reference,
                "score": score
            })
            total_score += score
            num_samples += 1

    average_score = total_score / num_samples if num_samples > 0 else 0
    print(f"Average custom score: {average_score:.4f}")
    return results, average_score

# --- Placeholder definitions ---
# def load_my_custom_data():
#     # Load your specific data format here
#     # Example: yield "Generate SQL for users table:", "SELECT * FROM users;"
#     pass
#
# def my_sql_metric(generated, gold):
#     # Example: Check if generated SQL is valid and matches gold semantically
#     # Return 1.0 for match, 0.0 otherwise (simplistic)
#     is_valid_sql = True # Placeholder check
#     matches_gold = generated.strip().lower() == gold.strip().lower() # Simplistic check
#     return 1.0 if is_valid_sql and matches_gold else 0.0
#
# custom_data_loader = load_my_custom_data()
# results, avg_score = run_custom_evaluation("gpt2", custom_data_loader, my_sql_metric)
# --- End Placeholder definitions ---

Note: This is a simplified example. Production evaluation often involves more sophisticated generation strategies, batching, and error handling.

Analysis and Iteration

Simply calculating an aggregate score isn't enough. The real value comes from analyzing the results to understand why the model succeeds or fails.

• Error Analysis: Manually review a sample of the evaluation instances, particularly those where the model performed poorly. Categorize the errors. Is the model hallucinating facts? Failing to follow instructions? Producing incorrectly formatted output? Showing bias?
• Qualitative Insights: Look for patterns in successes and failures. Are there specific types of prompts or topics the model struggles with?
• Iterate: Use the insights from your analysis to refine the model (e.g., through further fine-tuning on data similar to the failure cases), the evaluation task itself (e.g., clarifying instructions), the evaluation data (e.g., adding more challenging examples), or the metrics (e.g., designing a metric that better captures a specific failure mode). Custom evaluation development is often an iterative process.

Iterative development cycle for custom LLM evaluation tasks.

Challenges and Considerations

Developing custom evaluations requires careful thought and resources:

• Cost and Effort: Creating high-quality datasets and annotation guidelines, especially those requiring domain expertise or extensive human labeling, can be time-consuming and expensive.
• Metric Validity: Ensuring your custom metrics accurately reflect the true quality criteria is challenging. A metric might be easy to compute but fail to correlate well with actual user satisfaction or task success.
• Bias: Evaluation datasets and metrics can inadvertently contain biases present in the source data or annotation process. Actively look for and mitigate potential biases related to demographics, viewpoints, or other sensitive attributes.
• Scalability: Human evaluation, while valuable, doesn't scale easily for frequent, large-scale testing. Balance detailed human analysis with more scalable automated metrics.
• Maintenance: As the application or model evolves, the custom evaluation may need updates to remain relevant.

Despite these challenges, well-designed custom evaluations provide indispensable insights into your LLM's capabilities and shortcomings, guiding development efforts far more effectively than relying solely on generic benchmarks. They bridge the gap between abstract language modeling performance and tangible application success.