G-Eval

G-Eval is a novel framework designed to evaluate the outputs of large language models (LLMs) using the interpretive and reasoning capabilities of the models themselves. Introduced in the paper “NLG Evaluation using GPT-4 with Better Human Alignment”, G-Eval represents a significant advancement in natural language generation (NLG) evaluation, producing scores that better align with human judgment compared to traditional methods like BLEU or ROUGE.

Applicable Models:

G-Eval has been extensively tested with GPT-3.5 and GPT-4 and can be extended to other LLMs with comparable capabilities, provided they support chain-of-thought (CoT) reasoning and output token probability analysis.

Background:

Conventional NLG evaluation metrics often fail to fully capture the semantic richness and contextual nuance of LLM outputs. G-Eval addresses this gap by leveraging LLMs to act as evaluators, combining chain-of-thought reasoning with a structured form-filling paradigm to deliver detailed, task-specific metrics. This innovative approach has demonstrated higher correlation with human evaluations, making it a robust tool for assessing complex LLM tasks like summarization, dialogue generation, and coherence measurement.

Formulae:

The G-Eval process can be summarized as follows:

1. Define Evaluation Task:

• Specify the evaluation criteria (e.g., coherence, relevance, factuality).

• Provide definitions for the criteria (e.g., “Coherence: the collective quality of all sentences in the actual output.”).

2. Generate Evaluation Steps:

• Use chain-of-thought reasoning to guide the LLM in breaking down the evaluation task into interpretable steps.

3. Construct the Prompt:

• Concatenate the evaluation steps with task-specific inputs (e.g., LLM-generated output).

• Request a score from the LLM based on a predefined scale (e.g., 1–5).

4. Optional Normalization:

• If access to raw token probabilities is available, refine the score by calculating a weighted summation of probabilities to reduce biases.

Applications:

• NLP Benchmarks: Evaluating outputs in text summarization, dialogue generation, and translation tasks.

• Human-Machine Interaction: Assessing coherence, factual correctness, or semantic relevance of LLM outputs.

• Research and Development: Implementing task-specific evaluation metrics in frameworks like DeepEval to benchmark LLMs.

Impact:

G-Eval offers a significant improvement in aligning automated evaluations with human judgment. By leveraging the semantic understanding of LLMs, G-Eval captures the full complexity of generative text, which traditional metrics struggle to assess. It provides flexibility for domain-specific criteria and enhances interpretability by generating explanations for evaluation scores. However, its reliance on LLMs introduces challenges like arbitrariness and bias, underscoring the need for careful implementation and validation.