CLEAR: Error Analysis via LLM-as-a-Judge Made Easy

CLEAR (Comprehensive LLM Error Analysis and Reporting) is an interactive, open-source package for LLM-based error analysis. It helps surface meaningful, recurring issues in model outputs by combining automated evaluation with powerful visualization tools.

The workflow consists of two main phases:

1. Analysis
   Generates textual feedback for each instance; Identifies system-level error categories from these critiques and quantifies their frequencies.

2. Interactive Dashboard
   An intuitive dashboard provides a comprehensive view of model behavior. Users can:

   • Explore aggregate visualizations of identified issues
   • Apply dynamic filters to focus on specific error types or score ranges
   • Drill down into individual examples that illustrate specific failure patterns

CLEAR makes it easier to diagnose model shortcomings and prioritize targeted improvements.

You can run CLEAR as a full pipeline, or reuse specific stages (generation, evaluation, or just UI).

🚀 Quickstart

Requires Python 3.10+ and the necessary credentials for a supported provider.

1. Installation

Option 1 (Recommended for development): Clone the repo and set up a virtual environment:

git clone https://github.com/IBM/CLEAR.git
cd CLEAR
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .

📦 Option 2: Install via pip (Latest Release)

pip install clear-eval

` 2. ### Set provider type and credentials CLEAR requires a supported LLM provider and credentials to run analysis. See supported providers ↓

⚠️ Using a private proxy or openai deployment? You must configure your model names explicitly (see below). Otherwise, default model names will be used automatically for supported providers.

3. Run on sample data:

The sample dataset is a small subset of the GSM8K math problems. For running on the sample data and default configuration, you simpy have to set your provider and run

run-clear-eval-analysis --provider=openai # or rits, watsonx

This will:

• Run the full CLEAR pipeline
• Save results under: results/gsm8k/sample_output/

4. View results in the interactive dashboard:

run-clear-eval-dashboard

Or set the port with

run-clear-eval-dashboard --port <port>

Then:

• Upload the generated ZIP file from results/gsm8k/sample_output/
• Explore issues, scores, filters, and drill into examples

5. To explore the dashboard without running any analysis:

Run the dashboard:

run-clear-eval-dashboard

Then you can load the pre-generated sample output zip. you can manually upload a sample .zip file located at:

<your-env>/site-packages/clear_eval/sample_data/gsm8k/analysis_results_gsm8k_default.zip

📁 Or just download it directly from the GitHub repo.

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📂 Analyzing your own data

📄 Input Data Format

CLEAR takes a CSV file as input, with each row representing a single instance to be evaluated.

Required Columns

Column	Used When	Description
id	Always	Unique identifier for the instance
model_input	Always	Prompt provided to the generation model
response	Using pre-generated responses	Pre-generated model response (ignored if generation is enabled)
ground_truth	Performing reference based analysis	Ground-truth answer for evaluation (optional)
others	--input_columns is used	Additional input columns to show in dashboard (e.g. question)

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🚀 Running the analysis

CLEAR can be run via the CLI or Python API.

Option 1: CLI commands

Each stage has its own entry point:

run-clear-eval-analysis --config_path path/to/config.yaml    # run full pypeline
run-clear-eval-generation --config_path path/to/config.yaml  # run generation only
run-clear-eval-evaluation --config_path path/to/config.yaml  # Assume generation responses are given, run evaluation

• If --config_path is specified, all parameters are taken from the config unless explicitly overridden
• CLI flags passed directly override corresponding config values

Option 2: Python API

from clear_eval.analysis_runner import run_clear_eval_analysis, run_clear_eval_generation, run_clear_eval_evaluation

run_clear_eval_analysis(
    config_path="configs/sample_run_config.yaml"
)

You may also pass overrides instead of using a config file:

from clear_eval.analysis_runner import run_clear_eval_analysis

run_clear_eval_analysis(
    run_name="my_data",
    provider="openai",
    data_path="my_data.csv",
    gen_model_name="gpt-3.5-turbo",
    eval_model_name="gpt-4",
    output_dir="results/gsm8k/",
    perform_generation=False,
    input_columns=["question"]
)

📊 Launching the Dashboard

run-clear-eval-dashboard

Upload the ZIP file generated in your --output-dir when prompted.

🎛 Supported CLI Arguments

Arguments can be provided via:

• A YAML config file (--config_path)
• CLI flags
• Python function parameters (when using the API)

⚠️ Boolean arguments (perform_generation, is_reference_based, resume_enabled)
These must be set explicitly to true or false in YAML, CLI, or Python.
On the CLI, use --flag True or --flag False (case-insensitive).

⚠️ Naming Convention
Parameter names use snake_case in YAML and Python, but use --kebab-case in CLI.
For example:

• YAML: perform_generation: true
• Python: perform_generation=True
• CLI: --perform-generation True

Argument	Description	Default
--config_path	Path to a YAML config file (all values loaded unless overridden by CLI args)
--run_name	Unique run name (used in result file names)
--data_path	Path to input CSV file
--output_dir	Output directory to write results
--provider	Model provider: openai, watsonx, rits
--eval_model_name	Name of judge model (e.g. gpt-4o)
--gen_model_name	Name of the generator model to evaluate. If not running generations - the generator name to display.
--perform_generation	Whether to generate responses or use existing response column	True
--is_reference_based	Use reference-based evaluation (requires ground_truth column in input)	False
--resume_enabled	Whether to reuse intermediate outputs from previous runs stored in output_dir	True
--evaluation_criteria	Custom criteria dictionary for scoring individual records: {"criteria_name1":"criteria_desc1", ...}supported for yaml config and python.	None
--input_columns	Comma-separated list of additional input fields (other than model_input) to appear in the results and dashboard (e.g. question)	None

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🔑Supported providers and credentials

Depending on your selected --provider:

Provider	Required Environment Variables
openai	OPENAI_API_KEY, [OPENAI_API_BASE if using proxy ]
watsonx	WATSONX_APIKEY, WATSONX_URL, WATSONX_SPACE_ID or PROJECT_ID
rits	RITS_API_KEY

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