LLM-as-a-Judge vs Human Evaluation

This is part one of our blog series on LLM-as-a-Judge!

Part 1: LLM-as-a-Judge vs Human Evaluation

Part 2: Best Practices For Creating Your LLM-as-a-Judge

Part 3: Tricks to Improve LLM-as-a-Judge

Gone are the days when evaluating AI systems meant endless hours of human review. Enter "LLM-as-a-Judge," a game-changing approach that uses LLMs to assess other LLMs.

Why the buzz? LLMs can now evaluate AI outputs faster and often more cost-effectively than human experts. Imagine having a tireless, knowledgeable assistant who can analyze thousands of LLM responses in a fraction of the time it takes a human team. But LLM judges aren't perfect - they can miss nuances humans would catch. That's why we're still refining this approach.

In this post, we'll explore the applications of LLM-as-a-Judge and the challenges we're tackling. Whether you're an AI pro or just curious, we have in-depth info for everyone!

What is LLM-as-a-Judge?

LLM-as-a-Judge refers to using LLMs to evaluate various components of AI systems. The methodology involves prompting a powerful LLM to assess the quality of diverse outputs, including those generated by other models or human annotations.

This approach is useful when statistical comparisons with ground truth are insufficient or impossible such as when ground truth is unavailable or when dealing with unstructured outputs that lack reliable evaluation metrics. The versatility of LLM-as-a-Judge- stems from its reliance on well-crafted prompts, leveraging the vast capabilities of LLMs to address virtually any question.

To get an idea, lets look at how the LLM-as-a-Judge method can be applied to Retrieval-Augmented Generation (RAG) evaluation: We can create a template containing retrieved chunks and a question, then ask the LLM to determine whether the chunks are relevant for answering the question. By providing the LLM with context chunks and an answer, we can ask it to verify whether the answer is grounded in the given context or if it introduces new factual information not present in the chunks.

Challenges in Human Evaluation

Human judges have been considered the gold standard in evaluating AI-generated outputs for years. However, this approach has its challenges, which can significantly impact the reliability and scalability of evaluations. We must understand these limitations as we strive to develop more effective and unbiased evaluation methods.

The recent paper Human Feedback is not Gold Standard sheds light on the shortcomings and potential biases inherent in using human preference scores for LLM evaluation and training. This research calls for more nuanced and objective approaches to assess model performance.

Impact of Confounding Factors

The study investigates how two confounding factors—assertiveness and complexity—influence human evaluations. By using instruction-tuned models to generate outputs with varying levels of these dimensions, researchers discovered a troubling trend: more assertive outputs tend to be perceived as more factually accurate, regardless of their actual content. This suggests that human evaluators may be unduly swayed by the confidence with which information is presented, rather than its veracity.

Humans tend to perceive more assertive outputs as more factually accurate, regardless of their actual content.

Bias in Preference Scores

The authors posit that preference scores, used to rate the quality of LLM outputs, are inherently subjective. This subjectivity implies that individual preferences may not be universally applicable and could introduce unintended biases into the evaluation process. Such biases can skew results and lead to misleading conclusions about model performance.

Low Coverage of Factual Errors

A concerning finding is that models might receive favorable ratings even when producing factually incorrect information, as long as human evaluators prefer the output style or presentation. This disconnect between perceived quality and factual accuracy poses a significant risk to the reliability of AI systems.

Harmful Feedback Loops

Presumably due to human bias towards assertive responses, preliminary evidence suggests that training models using human feedback may disproportionately increase the assertiveness of their outputs. This trend raises alarm bells about the potential for models to become overconfident in their responses, potentially misleading users and eroding trust in AI systems.

Resource Intensiveness

Conducting human evaluations at an enterprise scale is both expensive and time-consuming. The process demands:

• Coordination with annotators
• Development of custom web interfaces
• Creation of detailed annotation instructions
• Extensive data analysis
• Careful management of crowd workers

These resource requirements often act as a significant barrier to experimentation and system improvement, potentially stifling innovation in the field.

Approaches to LLM-as-a-Judge

With LLM-as-a-Judge evaluation supplanting human evaluation, three primary approaches have emerged: Single Output Scoring without reference, Single Output Scoring with reference, and Pairwise Comparison.

Each approach offers unique advantages and is suited to different evaluation scenarios. By understanding these methods, practitioners can select the most appropriate technique for their specific evaluation needs.

Single Output Scoring (without reference)

In this paradigm, the LLM is tasked with assigning scores based on predefined criteria. Key characteristics include:

• Scores are typically assigned on a discrete scale with a limited number of values.
• Each value on the scale should be clearly defined to ensure consistency in evaluation.
• The LLM relies solely on the output and the evaluation criteria provided in the prompt.

This method is particularly useful for straightforward evaluations where the quality of the output can be assessed independently.

Single Output Scoring (with reference)

This approach builds upon the first method by incorporating additional context:

• The prompt includes supplementary information, referred to as a "reference," to aid the LLM in its evaluation.
• References may include reasoning steps, expected answers, or other relevant details that simplify the LLM's task.

This method can lead to more nuanced and informed evaluations, especially for complex outputs.

Pairwise Comparison

The pairwise comparison paradigm involves a direct comparison between two outputs:

• The judge LLM is presented with two inputs and asked to select the superior one based on specified criteria.
• It helps mitigate some of the challenges associated with absolute scoring, as the LLM only needs to make a comparative judgment.

This method is particularly effective for relative assessments, such as determining which of two responses is more relevant or comprehensive.

While scoring schemas provide valuable quantitative assessments of LLM outputs, incorporating explanations from the evaluating LLM can significantly enrich the evaluation process. We gain deeper insights into its decision-making process by prompting the LLM to articulate its reasoning. These explanations offer numerous benefits:

• Meta-evaluation: They allow us to assess the reliability and consistency of the LLM-as-a-judge model itself.
• Training data: The explanations can serve as high-quality annotations for fine-tuning other models.
• Explainability: They provide transparent justifications for the scores, enhancing trust and understanding in the evaluation process.
• Diagnostic tool: Explanations can help identify specific strengths or weaknesses in the evaluated outputs, guiding targeted improvements.

It's important to note that no single evaluation method is universally superior. For instance, while pairwise comparisons excel at determining relative quality between outputs, they may not provide the absolute performance metrics that single output scoring methods offer. However, the outcomes of the single output scoring methods can become unreliable, as absolute scores tend to vary more than relative pairwise results, especially if the LLM judge gets updated/replaced, see Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

The pairwise comparison method scales poorly since the more samples/models, the more possible comparisons grow exponentially. However, having only some comparisons is enough in some cases, and we can still associate a global score, like the EvaluLLM method from Human-Centered Design Recommendations for LLM-as-a-Judge.

On the other hand, providing a reference requires more preparation but can greatly simplify the LLM’s evaluation process. This is especially relevant for complex tasks, such as evaluating the reasoning steps of a math problem, where not providing the reference could require the LLM to solve the problem on its own.

Advantages of LLM-as-a-Judge

As LLM-as-a-Judge gets supported by increasingly powerful language models, it often emerges as the most efficient evaluation method. Here are some key advantages it offers over traditional human evaluation:

• Scalability: LLMs can process vast amounts of data rapidly, making them ideal for large-scale evaluations.
• Cost-Effectiveness: By reducing the need for extensive human labor, this approach significantly cuts down on costs.
• Flexibility: LLMs can be fine-tuned or prompt-engineered for specific tasks, enhancing relevance and reducing bias.
• Complex Understanding: These models can evaluate intricate texts across various formats, providing nuanced assessments.
• Bias Reduction: Through systematic refinement of prompts and few-shot samples, LLMs can mitigate certain biases that human evaluators might inadvertently introduce.
  

Issues with LLM-as-a-Judge

LLM-based evaluations are also prone to biases, just like human annotations, as these LLMs are trained with human-annotated data. However, this challenge can be solved with the right approach.

Nepotism Bias

LLM evaluators inherently favor text generated by themselves. Suppose we ask GPT-4 to evaluate two responses to the question "What are the benefits of exercise?". One response is generated by GPT-4 itself, while the other is from a Claude Sonnet. Despite both answers being equally informative, GPT-4 might rate its own response higher, showcasing a preference for its own writing style and content structure.

Paper - LLM Evaluators Recognize and Favor Their Own Generations

Authority Bias

This bias involves attributing greater credibility to statements from perceived authorities, regardless of the evidence presented. An LLM might be asked to evaluate two explanations of quantum mechanics - one from a renowned physicist and another from a graduate student. Even if the graduate student's explanation is more accurate and up-to-date, the LLM might favor the physicist's explanation due to their perceived authority in the field.

Paper - Humans or LLMs as the Judge? A Study on Judgement Biases

Beauty Bias

LLMs might favor aesthetically pleasing text, potentially overlooking the accuracy or reliability of the content. When evaluating two product descriptions, an LLM might give a higher score to a poetically written but factually incomplete description over a plain but comprehensive one. For instance, "Our sleek, cutting-edge smartphone redefines mobile technology" might be preferred over "This phone has a 6-inch screen, 128GB storage, and 12MP camera.

Paper - Humans or LLMs as the Judge? A Study on Judgement Biases

Verbosity Bias

LLMs might equate quantity of information with quality, potentially prioritizing verbose text over succinct and accurate content. In evaluating two restaurant reviews, an LLM might favor a lengthy, detailed review that meanders through various topics over a concise, to-the-point review that effectively communicates the key points about food quality and service.

Paper - Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Positional Bias

LLMs might exhibit a bias towards information placement (beginning or end of a document may be deemed more important), potentially impacting text interpretation. When asked to summarize a long article, an LLM might give undue weight to information presented in the introduction and conclusion.

Paper - Large Language Models are not Fair Evaluators

Attention Bias (for lengthy text)

LLMs can sometimes miss contextual information present in the middle of the lengthy text. This bias suggests that the model may focus more on the beginning and end of passages, potentially leading to incomplete understanding or interpretation of the text. In evaluating a lengthy legal document, an LLM might accurately recall details from the opening statements and closing arguments but struggle to incorporate important nuances discussed in the middle sections, leading to an incomplete assessment.

Paper - Lost in the Middle: How Language Models Use Long Contexts

Our Approach to LLM-as-a-Judge

At Galileo, we adhere to the principle of 'Evaluation First' — every process begins and ends with the thorough evaluation and inspection of your application. In our ongoing research into the capabilities of various LLMs, particularly in detecting hallucinations, we pioneered a high-performance method ChainPoll for evaluating LLMs.

We developed ChainPoll to combine Chain-of-Thought prompting with polling to ensure robust and nuanced assessment.

Chain-of-Thought Reasoning

Chain-of-thought (CoT) prompting is a straightforward yet potent strategy for extracting more accurate answers from LLMs. By prompting the LLM to articulate its reasoning process step-by-step before presenting the final answer, ChainPoll ensures deeper engagement and comprehension.

Consider the analogy of solving a complex problem: Just as humans benefit from reflection before responding, LLMs excel when allowed to process information sequentially. CoT prompts facilitate this reflective process, significantly elevating the quality of generated responses.

Leveraging Response Diversity

ChainPoll extends CoT prompting by soliciting multiple, independently generated responses to the same prompt and aggregating these responses. By diversifying the pool of generated arguments, ChainPoll embraces the concept of self-consistency, wherein valid arguments converge towards the correct answer while invalid arguments scatter.

While closely related to self-consistency, ChainPoll introduces several key innovations. Unlike self-consistency, which relies on majority voting to select a single best answer, ChainPoll employs averaging to produce a nuanced score reflective of the LLM's level of certainty.

By capturing the breadth of responses and their associated levels of certainty, ChainPoll transcends simplistic binary judgments, offering a comprehensive evaluation framework adaptable to diverse use cases.

Prompt Engineering

ChainPoll has meticulously fine-tuned its prompts to minimize biases inherent in LLMs. While the LLM-as-a-Judge approach can be pretty costly, ChainPoll cuts costs significantly using concise, effective prompts and cost-efficient LLMs. Additionally, outputs are generated in batches, ensuring that latency remains low and performance is optimized.

Let me show you how easy it is to set up an LLM judge in Galileo. By default, these metrics use gpt-4o-mini for the LLM and 3 judges.

1import promptquality as pq
2
3pq.EvaluateRun(..., scorers=[
4    pq.CustomizedChainPollScorer(
5        scorer_name=pq.CustomizedScorerName.context_adherence_plus,
6        model_alias=pq.Models.gpt_4o_mini,
7        num_judges=3)
8    ])

Small Language Models for Evaluation

Several evaluation frameworks such as RAGAS, Trulens, and ARES have been developed to automate hallucination detection on a large scale. However, these methods often rely on static LLM prompts or fine-tuning on specific in-domain data, which limits their ability to generalize across various industry applications. Let's look at a middle ground between human evaluation and large language models.

Customer-facing dialogue systems require a highly accurate, quick, and cost-effective hallucination detection solution to ensure that hallucinations are identified and corrected before reaching the user. Current LLM prompt-based methods fall short of meeting the stringent latency requirements due to their model size.

Furthermore, while commercial LLMs like OpenAI's GPT models perform well, querying customer data through third-party APIs is expensive and raises privacy and security concerns. Fine-tuned BERT-sized models can offer competitive performance with lower latency, but they need annotated data for fine-tuning and have not been tested extensively for large-scale, cross-domain applications.

Enter our Evaluation Foundation Model research paper on Luna, a lightweight RAG hallucination detection model capable of generalizing across multiple industry domains and scaling efficiently for real-time deployment. Luna is a DeBERTa-large encoder, fine-tuned on a meticulously curated dataset of real-world scenarios.

How to Think About GenAI Evaluation

We’ve identified multiple evaluation approaches depending on different scenarios. The following diagram presents a structured approach to generative AI evaluation, breaking down the process into key components and methodologies. From defining scenarios to analyzing both objective and subjective outputs, this framework provides a comprehensive overview of how we can systematically evaluate AI models across various dimensions.

Conclusion

LLM-as-a-Judge marks a significant advancement in AI evaluation, offering unparalleled scalability and cost-effectiveness. While challenges persist, ongoing innovations are rapidly enhancing the accuracy and fairness of LLM judges. These approaches not only refine our evaluation methodologies but also push the boundaries of AI performance, paving the way for more sophisticated and reliable AI systems. Connect with us to learn more about our state-of-the-art evaluation capabilities.

References

Humans or LLMs as the Judge? A Study on Judgement Bias

ChainPoll: A High Efficacy Method for LLM Hallucination Detection

Human Feedback is not Gold Standard

Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators