G-Eval

What is G-Eval?

G-Eval is an advanced evaluation framework for Natural Language Generation (NLG), utilizing large language models (LLMs) to assess generated text. This powerful tool ensures that output aligns closely with human reasoning, enhancing both quality and relevance.

Understanding NLG Challenges

While NLG enables computers to produce human-like text, challenges like irrelevant information or hallucinations can occur. Evaluating these outputs thoroughly is crucial to maintaining high-quality interactions.

Evaluation Metrics

• Statistical methods: BLEU, ROUGE, METEOR
• Model-based methods: NLI, BLEURT, G-Eval
• Combined methods: BERTScore, MoverScore

G-Eval’s Unique Approach

G-Eval employs a model such as GPT-4 to evaluate text based on defined criteria, aiming for high correlation with human judgment. Users can set specific evaluation metrics, such as conciseness and relevance, tailored to the task at hand.

The Evaluation Process

1. Introduce the task and define criteria: Provide clear evaluation criteria to generate evaluation steps.
2. Execution: Input the generated text and context to the LLM, then obtain a score from 1 to 5, with 5 being optimal.

Detailed Example

Consider evaluating a generated article summary. First, the LLM receives a prompt detailing the task and evaluation metrics like coherence and relevancy. For instance:

“You will receive a summary of an article. Rate the summary based on one metric.”

The LLM analyzes the summary for structure and relevance, assigning scores for each.

Chain of Thought (CoT) Prompting

G-Eval uses CoT prompting to deconstruct complex tasks into manageable steps, enhancing evaluations to better reflect human reasoning. For coherence, the steps might include:

1. Identify the article's main points.
2. Check if the summary logically covers these points.
3. Assign a coherence score from 1 to 5.

Advanced Scoring Methods

By incorporating probability-weighted scoring or repeated evaluations, G-Eval can derive scores sensitive to subtle textual nuances.

Conclusion

G-Eval offers a comprehensive framework for enhancing NLG systems. Its adaptability to custom metrics and human-like evaluation process makes it an indispensable tool for achieving high-quality AI communications.