From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline

Tianle Li, Wei-Lin Chiang, Evan Frick, Lisa Dunlap, Tianhao Wu, Banghua Zhu, Joseph E. Gonzalez, Ion Stoica

Introduction

The proliferation of Large Language Models (LLMs) has spurred advancements, leading to models trained on vast datasets with significantly enhanced capabilities. However, this innovation also presents challenges in designing effective evaluation benchmarks. Many existing benchmarks are unable to keep up with the rapidly advancing capabilities of these models, becoming saturated and unable to differentiate between state-of-the-art models. Additionally, many benchmarks are designed for automatic evaluation with restrictive prompts, such as close-ended or multiple-choice questions, which do not accurately reflect a model’s performance in real-world interactions. Recent developments in benchmark evaluation have made it feasible to evaluate more open-ended prompts, yet generating high-quality prompts at scale remains challenging. Furthermore, static benchmarks are prone to over-fitting and potential test leakage over time, reducing their effectiveness in ranking models.

To address these issues, an effective benchmark must confidently differentiate model capabilities, reflect real-world human interactions, and update frequently to stay relevant and avoid overfitting. Live crowdsourced evaluation platforms, like the Chatbot Arena , address some of these concerns by collecting real-world user prompts with preference labels. However, without quality control, these platforms can include low-quality prompts that do not adequately differentiate top models.

In this work, we introduce BenchBuilder, a data pipeline designed to automatically build high-quality benchmarks from live crowdsourced data source such as the Chatbot Arena. BenchBuilder identifies diverse benchmark prompts that can confidently separate model performance. It uses seven key indicators of high-quality prompts, such as specificity and domain knowledge, and employs an LLM to annotate prompts for these indicators. The resulting benchmark, Arena-Hard-Auto v0.1, uses an LLM judge to estimate human preference against a baseline model, making BenchBuilder and Arena-Hard-Auto v0.1 a fully automated system. We further explore how to measure the quality of benchmarks by introducing metrics to assess a benchmark’s ability to confidently separate models and its agreement with human preferences. Comparing Arena-Hard-Auto v0.1 to current leading benchmarks such as AlpacaEval and MT-Bench , we demonstrate that Arena-Hard-Auto v0.1 offers significantly stronger separability with tighter confidence intervals. Additionally, Arena-Hard-Auto v0.1 achieves 89.1% agreement with Chatbot Arena rankings, making it an excellent predictor of downstream performance. Arena-Hard-Auto v0.1 provides a cost-effective and informative evaluation benchmark, and the BenchBuilder pipeline serves as a valuable tool for developers seeking to automatically generate high-quality benchmarks from vast data sources.

To summarize, our work makes the following contributions:

We propose a novel data curation pipeline, BenchBuilder, to automatically construct high-quality benchmarks from crowdsourced data.

We propose metrics to capture desired properties in a benchmark, and validate Arena-Hard-Auto v0.1 achieves significantly better separability and agreement than existing benchmarks.

We open-source both the Arena-Hard-Auto v0.1 benchmark and BenchBuilder pipeline111Our code is available at: https://github.com/lm-sys/arena-hard-auto.

Related works

LLM benchmarks. We briefly review widely used LLM benchmarks. Most existing benchmarks are static and ground-truth-based (e.g., multi-choice question answering). They cover a wide range of domains, including math, science, coding, and reasoning. Common ones include MMLU , MATH , GSM-8K , HumanEval , DROP , BigBench , HellaSwag , AGIEval , GPQA , as well as comprehensive collection like HELM . Many have considered task-based evaluation such as IFEval , SWE-Bench , or AgentBench . As LLMs become widely adopted in open-ended scenarios involving interaction with humans (e.g., chatbot), many have considered human evaluation using domain experts or crowd raters such as Amazon Mechanical Turk to examine models’ response quality. As an alternative to human labeling, previous work has shown that LLM-as-a-judge can be effective human preference proxies (e.g., AlpacaFarm , MT-bench , AlpacaEval ).

Benchmark leakage. A fundamental limitation of static benchmarks is the potential risk of test set leakage (i.e., contamination). Existing works have suggested a growing risk of contamination, which undermines the reliability of benchmarks over time, motivating the need for benchmarks that are more frequently updated.

Live benchmarks. DynaBench identifies these challenges and recommends creating living and continuously evolving benchmarks. Recent works LiveCodeBench , R2E , as well as the community based live evaluation, Chatbot Arena . However, none of these focus on developing a pipeline for automatic benchmark curation.

How do you measure benchmarks?

Given a benchmark consists of a dataset $D$ of prompts and scoring function $f$ to measure the performance of model $\pi_{i}$ on prompt $\mathrm{p}$ , we outline two key properties that an LLM chatbot benchmark approximating human preference should possess to provide a meaningful measurement of capabilities between models:

Separability: the benchmark should separate models with high confidence.

Alignment with Human Preference: $f$ should agree with human preference.

While previous works have focused on alignment, separability is also a crucial consideration when comparing models of similar quality (e.g., different checkpoints from the same training run). However, achieving high-confidence separability is challenging due to limitations in prompt design and inherent variances in LLM evaluations. Overly simplistic prompts fail to distinguish between models, while the randomness in human and LLM judgments leads to inconsistent predictions. As a result, it is often difficult to confidently determine if a model’s apparent performance reflects a genuine difference in capability or merely noisy observations, highlighting a need for methods to verify whether a benchmark can reliably separate similar models.

Statistical measures like Pearson and Spearman Correlations, commonly used in benchmarks such as AlpacaEval to measure correlation to human preference ranking, may fail to adequately address model separability and ranking instability. In addition, these measures only provide a coarse signal of ranking correlation without quantifying the magnitude of performance differences between model pairs.

To address these issues, we develop three novel metrics: Separability with Confidence, Agreement with Confidence, and Pair Rank Brier Score.

Separability with Confidence quantifies the benchmark’s confidence by measuring its consistency in predicting the winner of a model pair across random seeds through bootstrapping. This is done by calculating the percentage of model pairs that have non-overlapping confidence intervals of their benchmark scores. A higher percentage indicates that the benchmark is more confident in distinguishing between the performance of different models, as the confidence intervals of their scores do not overlap.

Agreement with Confidence Interval measures how well benchmarks A and B confidently distinguish between two models with the same ordering. Given two models $\pi_{1},\pi_{2}$ to evaluated on A and B, we assign the following scores:

If both A and B can confidently separate $\pi_{1},\pi_{2}$ , we assign a score of 1 if the rankings match across A and B, otherwise -1.

If either A or B cannot separate $\pi_{1},\pi_{2}$ with confidence, we assign a score of 0.

The final agreement score across models is the average score across all unique model pairs. An agreement score of 1 implies that benchmark A confidently agrees with benchmark B on the preference of every unique model pair. Conversely, a score of -1 indicates that A confidently disagrees with B on every unique model pair.

Pair Rank Brier Score further assesses an LLM benchmark’s capability to predict the ranking of a pair of competing models by rewarding confidence in correct predictions while penalizing confidence when incorrect. Consider two models $\pi_{1}>\pi_{2}$ with disparate quality. Although two benchmarks A and B predict the same ranking $\pi_{1}>\pi_{2}$ , they predict $P(\pi_{1}>\pi_{2})$ as $.60$ and $.90$ , respectively (undetectable by Spearman correlation). These benchmarks would result in very different Brier scores, reflecting their ability to quantify the magnitude of performance difference between the models. If both benchmarks give the wrong prediction of the winner, we prefer the benchmark with a less confident prediction. Brier score weighs a benchmark’s accuracy and its ability to quantify the appropriate level of uncertainty in its predictions.

For each model pair, the benchmark’s forecast probability $f(\pi_{i}<\pi_{j})$ can be estimated as $P(f(\pi_{i})<f(\pi_{j}))$ with $f(\pi_{i})\sim\mathcal{N}(\mu_{i},\sigma_{i}^{2})$ and $f(\pi_{j})\sim\mathcal{N}(\mu_{j},\sigma_{j}^{2})$ , where $\mu_{i}$ and $\sigma^{2}_{i}$ are the bootstrapped mean and variance by evaluating multiple times. The Brier Score Loss is then calculated over the benchmark’s prediction for each model pair with respect to the ground truth probability, where Brier Score is defined as $\frac{1}{N}\sum_{\{i,j\}}(f(\pi_{j}<\pi_{i})-O(\pi_{j}<\pi_{i}))^{2}$ , where $N$ is the number of model pairs and ${O}$ is the ground truth outcome.

Separability with Confidence, Agreement with Confidence Interval, and Pair Rank Brier Score each provide useful perspectives for evaluating the effectiveness of LLM benchmarks. However, no single metric is intended to be individually sufficient. Ultimately, these metrics are complementary and should be considered together holistically. We claim that together, these metrics offer a robust framework for assessing benchmark performance, balancing the need for clear differentiation with alignment to human preferences.

The BenchBuilder Pipeline and Arena-Hard-Auto v0.1 Dataset

BenchBuilder automatically extracts diverse, high quality prompts from a dataset of 200,000 user queries collected via Chatbot Arena. This is done by assigning a quality score to each prompt and by sampling prompts with a high quality score evenly across a wide range of topics. An outline of our data creation pipeline is shown in Figure 1.

Key Prompt Qualities • Specificity: Does the prompt ask for a specific, well-defined output without leaving any ambiguity? • Domain Knowledge: Does the prompt test the AI’s knowledge and understanding in a specific domain or set of domains? • Complexity: Does the prompt have multiple components, variables, or levels of depth and nuance? • Problem-Solving: Does the prompt require active problem-solving: analyzing and clearly defining the problem and systematically devising and implementing a solution? • Creativity: Does the prompt require a creative approach or solution? • Technical Accuracy: Does the prompt require an answer with a high degree of technical accuracy, correctness and precision? • Real-world Application: Does the prompt relate to real-world applications? To discover high quality prompts, we define seven qualities of a prompt which signify the skills required to successfully answer, including specificity, domain knowledge, complexity, and more shown in the figure above. These qualities are used by an LLM annotator to assign a “quality score” to a given prompt $\mathrm{p}$ , which indicates how many of the above qualities are seen in $\mathrm{p}$ . Full instruction prompt is in Section 11.

To ensure that the filtered prompts represent a wide range of tasks, we adopt a topic modeling pipeline in BERTopic by first converting each prompt with OpenAI’s embedding model (text-embedding-3-small ), reducing dimension with UMAP, and using a hierarchical-based clustering algorithm (HDBSCAN) to identify clusters which are then summarized using GPT-4-Turbo .

As some topic clusters contain mostly trivial or ill defined prompt like “hi”, we select the topic clusters with mean quality score >=6 out of 7 and sample prompts equally across each of these clusters. The resulting dataset consists of mostly well-defined, technical problem-solving queries as required in the above key criteria. Dataset statistics and further description of the filtering process used for Arena-Hard-Auto v0.1 along are in the following section.

2 Arena-Hard-Auto v0.1: A set of challenging, high-quality prompts from Chatbot Arena

We utilize the BenchBuilder pipeline to create Arena-Hard-Auto v0.1, a set of 500 high-quality prompts taken from 200,000 prompts from Chatbot Arena, an open source platform for LLM evaluation. We first remove any duplicate prompts, multi-turn conversations, or non-English conversations, and obtain over 4,000 topic clusters and 75,000 queries covering a wide range of domains.

Then, we use the GPT-3.5-Turbo as judge to assign a “quality score” to each prompt and remove prompts which either have a score < 5 or which belong to a topic cluster with a mean score < 3. We present the statistics in Table 1. Notably, around 18% out of 75,000 queries are higher quality according to GPT-3.5-Turbo. We then use GPT-4-Turbo as judge to assign a quality score and ensuring the quality of the remaining prompts, resulting with over 500 high quality clusters. We randomly sample 500 prompts from these clusters, creating Arena-Hard-Auto v0.1. Additionally, we confirm our final dataset does not contain any personally identifiable information or offensive content.

In Figure 3, we show examples of topic clusters ranging from low to high mean scores provided by GPT-3.5-Turbo. We can observe clusters with higher scores often correlate to challenging topics or tasks for LLMs like game development or mathematical proofs. On the other hand, clusters with lower scores point to trivial or ambiguous questions like "Flirty Texting Strategies". We present several example prompts and their respective topic cluster below.

[htb] Cluster 1: Greetings and Well-Being Inquiry (Mean Score: 2.7) ⬇ Yo, what up my brother (Qualities: None) Cluster 2: US Presidents Query (Mean Score: 3.2) ⬇ Who was the president of the US in 1975 (Qualities: Specificity, Domain-Knowledge, Technical Accuracy, Real-World) Cluster 3: Physics Problem Solving (Mean Score: 5.0) ⬇ A 50,000 kg airplane initially flying at a speed of 60.0 m/s accelerates at 5.0 m/s2 for 600 meters. What is its velocity after this acceleration? What is the net force that caused this acceleration? (Qualities: Specificity, Domain-Knowledge, Complexity, Problem-Solving, Technical Accuracy, Real-World) Cluster 4: OpenCV Image Processing Technique (Mean Score: 5.5) ⬇ you are given a task to detect number of faces in each frame of any video using pytorch and display the number in the final edited video. (Qualities: All)

To see whether “quality score” assigned during BenchBuilder’s pipeline correlates with separability and agreement, we sample 50 prompts per score and compare the responses from GPT-4 and Llama-2-70b-Chat , with GPT-4-Turbo as judge. In Figure 4 (Left), we observe a strong correlation between high potential score and the win-rate of GPT-4-Turbo over Llama-2-70b-Chat. A similar trend is also observed in other model pairs such as Claude Sonnet vs Haiku and Mistral-Large vs Mixtral .

Since qualities assigned by GPT-4-Turbo are critical to ensure the final prompt selection is high-quality, we verified its accuracy as annotator. We construct “ground truth” labels for 200 randomly sampled queries by collecting majority votes from GPT-4o , Claude-3-Opus , and Gemini-1.5-Pro . GPT-4-Turbo has 85.6% accuracy against “ground truth” labels, validating its ability as annotator.

Evaluation with LLM-as-a-Judge

Evaluating models on challenging queries such as Arena-Hard-Auto v0.1 is a non-trivial task. Most queries involve deep domain knowledge and problem solving skills, requiring expert-level judgment to evaluate the answer quality. Unfortunately, it is prohibitively expensive and time consuming to use human judges. Following LLM-as-a-Judge and AlpacaFarm , we employ LLM-as-a-judge framework to approximate human preference.

To obtain a score for model $\pi_{i}$ on prompt $\mathrm{p}$ , we consider the pairwise comparison setup against a strong baseline model $\pi_{base}$ (GPT-4-0314) and employ a strong judge model (e.g., GPT-4-Turbo or Gemini-1.5-Pro) to measure preference. We obtain a score for each output $\pi_{i}(\mathrm{p})$ by asking the judge to rank its preference of $\pi_{i}(\mathrm{p})$ or $\pi_{base}(\mathrm{p})$ on a Likert scale ( $1=$ prefers $\pi_{i}(\mathrm{p})$ much less than $\pi_{base}(\mathrm{p})$ , $5=$ prefers $\pi_{i}(\mathrm{p})$ much more than $\pi_{base}(\mathrm{p})$ ). This way, a model will be penalized more in big losses than small losses, which we find to be effective in separating models. We also employ chain-of-thought (CoT) to prompt the LLM judge to generate its own answer first before giving judgments. The full judge prompt can be found in Section 11.

To avoid potential position bias, we adopt a two-game setup – per query we swap the models on the first and second position. This results in 1000 judgments per model evaluation. Following Chatbot Arena, we adopt the Bradley-Terry model to produce model’s the final model scores. We aggregate all pairwise comparisons to the baseline model for all models and bootstrapping the comparisons to retrieve a bootstrapped confidence interval of all models’ win-rate against the baseline, producing a ordered ranking of all models by their win-rates.

Experimental Results

To compare Arena-Hard-Auto v0.1’s separability and alignment with humans against other widely used benchmarks, MT-Bench and AlpacaEval 2.0 Length Controlled , we obtain 95% confidence interval of model performances via applying 100 rounds of bootstrapping on judgment results for each benchmark. For AlpacaEval, we use pre-existing results from their repository. We obtain MT-Bench judgment with no modification to their recommended evaluation setup. For Arena-Hard-Auto v0.1, we employ the system proposed in section 5 by choosing gpt-4-0314 as baseline model for pairwise comparison.

In order to compare these benchmarks fairly, we use a set of top-20 models††footnotemark: on Chatbot Arena (2024/04/13) that are also presented on AlpacaEval leaderboard (2024/04/13) as ground truth for human preferences on the model ranking orders. In subsection 6.3, we individually compare Arena-Hard-Auto v0.1 to Chatbot Arena within a more similar distribution of user queries. We compute separability using 95% confidence intervals and consider the human preference ranking by Chatbot Arena as the reference to calculate agreement, Spearman correlation, and Pair Rank Brier Score across AlpacaEval, MT-Bench, and Arena-Hard-Auto v0.1.

2 Separability and Agreement Across Benchmarks

In Table 2, Arena-Hard-Auto v0.1 shows the highest separability (87.4%) against widely adopted LLM benchmarks and offers highest agreement (89.1%) to Chatbot Arena at $25 cost. In Figure 3, we show Arena-Hard-Auto v0.1 offers significantly stronger separability against MT Bench with tighter confidence intervals. With only 500 prompts, Arena-Hard-Auto v0.1 achieve impressive alignment to and even higher separability than Chatbot Arena Ranking, which constitutes for approximately 1 million real-world human preferences.

Notably, we observe a significant gap between MT-bench’s Spearman Correlation (91.3%) and confidence agreement (22.6%) to Chatbot Arena, an example where Spearman Correlation fails to account for variance of the rankings, and hence cannot adequately measure important ranking granularity of top LLMs. We present a visual comparison between Arena-Hard-Auto v0.1 and MT-Bench in Figure 3, highlighting Arena-Hard-Auto v0.1’s improved separability.

3 Comparing to a similar distribution of human preference

We set Chatbot Arena Category Hard Prompts (English) as ground truth reference. Chatbot Arena Category Hard Prompt is a human preference ranking leaderboard based on votes from a harder subset of 200,000+ prompts extracted from over 1 million Chatbot Arena queries using a similar method as BenchBuilder. In Table 5, we show Arena-Hard-Auto v0.1 achieving an overall higher alignment (92.0% Confidence Agreement) to human preferences.

4 Comparing to Random Subsets of Arena

To further verify BenchBuilder pipeline’s ability to select high-quality prompts, we compare Arena-Hard-Auto v0.1 to two sets of 500 randomly selected prompts from the 75,000 user queries prior to pipeline extraction. We compare their ranking preferences of the set of top-20 models produced by GPT-4-1106-Preview as judge. In Table 4, we observe Arena-Hard-Auto v0.1 is significantly better than both random baselines, verifying BenchBuilder’s effectiveness at selecting superior prompts for a LLM benchmark.

5 Ensemble-as-Judges

We study four popular LLMs222GPT-4-1106-Preview, Claude-3-Opus-20240229, Gemini-1.5-Pro-0514, and Llama-3-70b-instruct as judge on Arena-Hard-Auto v0.1. We statistically compare their ranking preference of the set of top-20 models111gpt-4-turbo-2024-04-09, claude-3-opus-20240229, claude-3-sonnet-20240229, gpt-4-0314 , gpt-4-0613, mistral-large-2402, qwen1.5-72b-chat , mistral-medium, claude-2.0, gpt-3.5-turbo-0613, claude-2.1, gemini-pro , mixtral-8x7b-instruct-v0.1, gpt-3.5-turbo-0314, yi-34b-chat , tulu-2-dpo-70b , dbrx-instruct-preview , vicuna-33b , starling-lm-7b-alpha , llama-2-70b-chat to the Chatbot Arena Leaderboard in Table 3. Out of the four judges, GPT-4-1106-Preview has the highest separability and agreement to human ranking preference. We also observe GPT-4-1106-Preview and Gemini-1.5-Pro have much higher agreement than Claude-3-Opus and Llama-3-70b-instruct, with more than 20% improvement.

We also experiment combining GPT-4-1106-Preview and Gemini-1.5-Pro as an ensemble judge by combining their judgment results. This achieves an even higher separability and agreement to human ranking preference, with agreement reaching 91.5%, demonstrating effectiveness of judging LLMs with Ensemble-as-Judges.

Limitations

Although our data source comes from diverse distributions, we acknowledge possible biases within our pipeline. For example, the 7 qualities we defined might not be all-encompassing and could potentially bias towards a limited distribution of prompts, such as technical fields. Further, prompt selection process could be biased by LLM annotators. Additionally, for model evaluation, we are employing LLMs as judges, who are known to suffer from verbosity bias . In Table 7, we present how verbosity can effect LLM as Judge on Arena-Hard-Auto v0.1. We also acknowledge potential self-bias in LLM judges (e.g., GPT-4 Judge prefers GPT-4 answers) in Table 3. Arena-Hard-Auto v0.1 also does not evaluate multi-turn or non-English interactions.

In the future, we plan to further the exploration of a more generalized and diverse set of prompt qualities, establishing a pipeline to systematically produce benchmark across a more comprehensive and inclusive distribution of prompts while maintaining high separability and alignment to humans. Additionally, we believe our approach of utilizing LLM-as-a-judge can be improved, such as developing a system of Ensemble-as-Judges, similar to Judges with Jury , to reduce biases and improve correlation.

Conclusions

We introduced BenchBuilder, a data curation pipeline that transforms crowdsourced data into high-quality benchmarks by seven key qualities. This pipeline enables building challenging and evolving benchmarks which is crucial for evaluating today’s advanced language models. Our evaluation metrics, including separability and agreement with confidence, provide a comprehensive assessment of benchmarks. We show the resulting benchmark, Arena-Hard-Auto v0.1, significantly improves separability and alignment with human preferences over existing benchmarks, achieving 89.1% agreement with Chatbot Arena rankings at only $25 per evaluation. We expect Arena-Hard-Auto v0.1 to be useful for LLM developers to evaluate their models with confidence and BenchBuilder to be a valuable tool for developers seeking to extract high-quality benchmark from vast amounts of data with minimal human effort.

Acknowledgment

We thank Matei Zaharia, Yann Dubois, Anastasios Angelopoulos, Lianmin Zheng, Lewis Tunstall, Nathan Lambert, Xuechen Li, Naman Jain, Ying Sheng, Maarten Grootendorst for their valuable feedback. We thank Siyuan Zhuang and Dacheng Li for the valuable review and debug of the code. We thank Microsoft AFMR for Azure OpenAI credits support. We also thank Together.ai & Anyscale for open model endpoint support.

References

Appendix

Prompts

Additional Examples

[htb] User: Use ABC notation to write a melody in the style of a folk tune. GPT-4: X:1 T:Untitled Folk Tune M:4/4 L:1/8 K:G|:G2A2|B2A2|G2E2|D4|E2F2|G2F2|E2C2|B,4|A2B2|c2B2|A2F2|E4|D2E2|F2E2|D2B,2|C4:| GPT-3.5-Turbo: Sure! Here’s a simple melody in the style of a folk tune written in ABC notation: X:1 T:Folk Tune M:4/4 L:1/4 K:C CDE|FGA|BAG|FED| CDE|FGA|BAG|c2z| CDE|FGA|BAG|FED| CDE|FGA|BAG|c2z|| This melody is in the key of C major and consists of four measures. Feel free to modify it or add more sections to create your own folk tune! Judgement: My own answer to the prompt would be: X:1 T:My Folk Melody M:3/4 L:1/8 K:D A2|:d2f2a2|b2a2f2|e2d2B2|A4F2| G2A2B2|A2G2F2|E2D2F2|1D6:|2D4A2| |:d2f2a2|b2a2f2|e2c2A2|B4A2| G2A2B2|A2G2F2|E2D2F2|1D6:|2D4|| This melody is in the key of D major, has a 3/4 time signature, and is structured in a typical AAB form common in folk music. It uses a range of an octave, which is typical for folk tunes that need to be easily singable. Now, let’s evaluate the responses from Assistant A and Assistant B. Assistant A’s Answer: Assistant A provides a melody in ABC notation that is coherent and follows the structure of a folk tune. The melody is in 4/4 time, which is common for folk music, and it is in the key of G major. The melody has a two-part form (indicated by the repeat signs |: and :|), which is typical in folk music. The range of the melody is appropriate for a folk tune, and the rhythm is simple and repetitive, which is characteristic of the genre. Assistant B’s Answer: Assistant B also provides a melody in ABC notation that is coherent and could be considered in the style of a folk tune. The melody is in 4/4 time and the key of C major. The melody consists of four measures repeated twice, which is a simple structure that could be found in folk music. However, Assistant B’s melody is less traditional in its form compared to Assistant A’s, as it does not have a distinct A and B section, which is more common in folk tunes. Additionally, the use of “z” for a half-measure rest is less typical in folk tunes, which usually have a more continuous melodic line. Comparison: Both assistants provided melodies that could be considered folk tunes. Assistant A’s melody has a more traditional structure with a clear A and B section, while Assistant B’s melody is simpler and does not follow the traditional AAB or AB form. Assistant A’s melody is also more detailed in terms of the ABC notation, providing a full eight-measure section that is repeated, which gives a more complete sense of the tune. Assistant B’s melody is correct in its notation but is more basic and less representative of a traditional folk tune structure. In terms of helpfulness, relevance, and conciseness, both assistants provided relevant and concise answers. However, Assistant A’s answer is slightly more helpful due to the more traditional structure of the provided melody. Creativity and novelty are not particularly applicable in this context, as the task is to create a melody in a traditional style rather than to innovate. Both assistants showed an understanding of the folk tune style, but Assistant A’s answer aligns more closely with traditional folk structures. There are no significant mistakes or inaccurate information in either assistant’s answers. However, Assistant B’s use of the rest “z” in a folk tune is less conventional. My final verdict is: Assistant A is slightly better: [[A>B]].