Praetor: A Fine-Grained Generative LLM Evaluator with Instance-Level Customizable Evaluation Criteria
Authors: Yongqi Leng, Renren Jin, Yue chen, Zhuowen Han, Ling Shi, Jianxiang Peng, Lei Yang, Juesi Xiao, Deyi Xiong
Accepted at: acl25

Praetor: A Fine-Grained Generative LLM Evaluator with Instance-Level Customizable Evaluation Criteria

Yongqi Leng1, Renren Jin1, Yue Chen1, Zhuowen Han1, Ling Shi1, Jianxiang Peng1, Lei Yang1, Juesi Xiao2, Deyi Xiong1,2*
1TJUNLP Lab, College of Intelligence and Computing, Tianjin University, Tianjin, China
2School of New Media and Communication, Tianjin University, Tianjin, China
{lengyq,rrjin,dyxiong}@tju.edu.cn

Abstract

With the increasing capability of large language models (LLMs), LLM-as-a-judge has emerged as a new evaluation paradigm. Compared with traditional automatic and manual evaluation, LLM evaluators exhibit better interpretability and efficiency. Despite this, existing LLM evaluators suffer from limited use scenarios and poor flexibility. To mitigate these issues, we propose Praetor, a fine-grained generative LLM evaluator with instance-level customizable evaluation criteria. To train Praetor, we curate a large-scale dataset guided with a hierarchical guideline covering a wide range of tasks and instance-level evaluation criteria. We train Praetor on this dataset in a multi-task learning fashion, which enables to evaluate LLMs in either pointwise grading or pairwise comparison way and support two languages simultaneously with a high flexibility of setting evaluation criteria. Extensive experiments demonstrate that Praetor outperforms previous LLM evaluators and instruction-tuned LLMs on multiple benchmarks, setting new SOTA results. It also exhibits the potential for generating critiques as scalable feedback to further improve LLMs. Our model and related resources are released at https://github.com/tjunlp-lab/Praetor.

1 Introduction

Recent years have witnessed that LLMs have been rapidly developed to achieve capabilities close to human level performance on numerous natural language processing tasks (Touvron et al., 2023a; OpenAI, 2023). Such generation capabilities, however, bring new evaluation challenges. Traditional evaluation methods are no longer suitable to comprehensively assess LLMs as they are able to generate open-ended content in a wide range of tasks (Li et al., 2024b; Guo et al., 2023). To address this challenge, LLM-as-a-judge (LLM-powered evaluation) has been widely explored and emerged as a new evaluation paradigm.

Existing studies on LLM-powered evaluation can be roughly categorized into two groups: prompting LLMs to evaluate other LLMs and fine-tuning LLMs as evaluators (Li et al., 2024a). Both methodologies suffer from their own limitations. Prompt-based methods usually resort on proprietary LLMs such as GPT-4 (OpenAI, 2023), which bring challenges of fairness, reproducibility and high API call costs. Although fine-tuning-based methods avoid these issues, they usually suffer from restricted usage scenarios and poor flexibility, such as single language support and single evaluation mode, no support for optional provision of evaluation criteria and customized evaluation criteria, which greatly limit the use of LLM evaluators.

To address the above challenges, we propose Praetor, an LLM evaluator with 7B parameters as shown in Figure 1. Praetor not only supports LLM evaluation in both Chinese and English (Multi-language support), but also enables two evaluation modes: pointwise grading and pairwise comparison (Multi-mode evaluation). Additionally,

![A diagram showing Praetor as a Chinese and English LLM evaluator that supports both pointwise grading and pairwise comparison with high flexibility.](page_1012_355_388_296.png)

Figure 1: Praetor is a Chinese and English LLM evaluator that supports both pointwise grading and pairwise comparison with high flexibility.
<table>
  <tr>
    <th rowspan="2">Models</th>
    <th colspan="2">Language</th>
    <th colspan="3">Mode</th>
    <th colspan="2">Flexibility</th>
  </tr>
  <tr>
    <th>Zh</th>
    <th>En</th>
    <th>Point</th>
    <th>Pair</th>
    <th>Cust.</th>
    <th>Opt.</th>
  </tr>
  <tr>
    <td>PandaLM</td>
    <td>x</td>
    <td>✓</td>
    <td>x</td>
    <td>✓</td>
    <td>x</td>
    <td>x</td>
    <td>x</td>
  </tr>
  <tr>
    <td>Prometheus-v1.0</td>
    <td>x</td>
    <td>✓</td>
    <td>✓</td>
    <td>x</td>
    <td>✓</td>
    <td>x</td>
    <td>x</td>
  </tr>
  <tr>
    <td>Prometheus-v2.0</td>
    <td>x</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>x</td>
    <td>x</td>
  </tr>
  <tr>
    <td>CritiqueLLM</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>x</td>
    <td>x</td>
    <td>x</td>
  </tr>
  <tr>
    <td>Themis</td>
    <td>x</td>
    <td>✓</td>
    <td>✓</td>
    <td>x</td>
    <td>x</td>
    <td>x</td>
    <td>x</td>
  </tr>
  <tr>
    <td>Auto-J</td>
    <td>x</td>
    <td>✓</td>
    <td>✓</td>
    <td>x</td>
    <td>x</td>
    <td>x</td>
    <td>x</td>
  </tr>
  <tr>
    <td>Auto-J-bilingual</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>x</td>
    <td>x</td>
    <td>x</td>
  </tr>
  <tr>
    <td>CompassJudger-1</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>x</td>
    <td>x</td>
  </tr>
  <tr>
    <td><b>Praetor (Ours)</b></td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
  </tr>
</table>

Table 1: Comparison between Praetor and currently used LLM evaluators. Cuts. and Opt. denote customized evaluation criteria and optional provision of evaluation criteria, respectively.

Praetor enjoys a high flexibility of setting evaluation criteria, i.e., evaluating LLMs with or without evaluation criteria. When customized evaluation criteria are provided, Praetor is able to strictly follow these criteria for fine-grained evaluation. When evaluation criteria are not provided, Praetor is able to evaluate LLMs with the most appropriate evaluation dimensions (<b>High flexibility</b>). All these advantages provide Praetor with greater flexibility during the usage stage, which many other LLM evaluators do not have. A detailed comparison between Praetor and other LLM evaluators is shown in Table 1.

To enable such advantages, we curate a high-quality and diverse LLM-evaluator training dataset. We collect questions from multiple open-source instruction datasets, generate responses through multiple LLMs, and then construct instance-level evaluation criteria, reference responses, and critiques with the guidance of powerful proprietary LLMs and a well-designed hierarchical guideline (Tan et al., 2024). During data curation, we execute a strict data quality control strategy. We perform data filtering and validation through the collaboration of multiple LLMs and human quality reviewers. With such a data quality control strategy, we construct a training dataset containing ~947K samples that cover 9 task types and 68 subtypes. On this created dataset, we train Praetor in a multi-task learning manner by setting multiple training objectives to enable aforementioned comprehensive evaluation capabilities.

We conduct experiments on multiple benchmarks in pointwise grading (\S4.4) and pairwise comparison (\S4.5). Our results show that Praetor not only achieves the best performance, but also has more comprehensive evaluation capabilities compared to previous LLM evaluators. Ablation results further demonstrate the importance of instance-level evaluation criteria and the effectiveness of data quality control strategies (\S4.6). We also examine the quality (\S5.1) and usefulness (\S5.2) of Praetor’s generated critiques, demonstrating Praetor’s ability to provide scalable feedback for improving LLMs.

In summary, our main contributions are as follows:

• We construct a large-scale training dataset with 947K samples, covering 9 evaluation tasks and 68 subtasks. Data instances are validated by powerful LLMs and human quality reviewers.

• We present Praetor, an LLM evaluator with instance-level customizable evaluation criteria, supporting multiple evaluation modes in both Chinese and English and enjoying high flexibility of evaluation criteria setting.

• Extensive experiments demonstrate the superior evaluation performance of Praetor over previous strong LLM evaluators, and its ability of generating critiques as feedback to improve LLM outputs.

2 Related Work

Prompting LLMs for Evaluation  Benefiting from the power of LLMs, LLM-as-a-judge for generative evaluation has become a promising evaluation approach. Previous studies conduct evaluation for a variety of natural language tasks by prompting proprietary LLMs, usually in three evaluation modes. The first evaluation mode is pointwise grading, which resorts LLMs to score the responses of other LLMs (Liu et al., 2024a; Zheng et al., 2023; Wang et al., 2023; Guo et al., 2024). The second evaluation mode is pairwise comparison, where a pair of responses is provided and LLMs are prompted to judge which one is better (Liusie et al., 2024; Zheng et al., 2023; Dubois et al., 2023). The third evaluation mode is ranking, where multiple responses are to be ranked by LLMs (Hou et al., 2024; Ji et al., 2023). Using proprietary LLMs for evaluation has achieved satisfactory performance, but brings other problems, e.g., fairness challenges due to the use of closed-source models, reproducibility challenges due to the uncontrolled version updates of proprietary LLMs, and the high cost of API calls (Kim et al., 2024a).
Figure 2: Illustration of the data construction pipeline and training method for Praetor. \( q, a, c, r \) and \( l \) denote question, response, evaluation criteria, reference response, and critique and result, respectively.

Fine-tuning LLMs as Evaluators To address these challenges in prompting LLMs for evaluation, subsequent studies propose to construct specialized LLM evaluators by fine-tuning open-source models. PandaLM (Wang et al., 2024) is fine-tuned for pairwise comparison while Prometheus (Kim et al., 2024a) enables pointwise grading. Auto-J (Li et al., 2024c) and Prometheus 2 (Kim et al., 2024b) are capable of both pointwise grading and pairwise comparison. However, these evaluators are constrained by their specific training tasks, resulting in a fixed evaluation pattern that holds a weak generalization ability (Huang et al., 2024). Themis (Hu et al., 2024) and CompassJudger-1 (Cao et al., 2024) benefit from complex training data construction. Compared with the above LLM evaluators, Praetor considers more comprehensive evaluation scenarios and enjoys multiple evaluation modes, languages and high flexibility in evaluation criteria.

3 Praetor

We construct instruction datasets for two different evaluation tasks, i.e., pointwise grading and pairwise comparison. To ensure data quality, we perform careful quality filtering and validation through human-LLM collaboration. We perform supervised fine-tuning of Praetor on this dataset in a multi-task learning way. Figure 2 illustrates our data construction pipeline and training method.

3.1 Data Creation

We first introduce the core elements for evaluation training data. For pointwise grading, the core data elements are: question (\( q \)), answer (\( a \)), [evaluation criteria (\( c \))], [reference response (\( r \))], critique and result (\( l \)), where [] denotes optional elements. For pairwise comparison, the single response is replaced with a pair of responses while other elements are the same as those in pointwise grading. All these elements need to be organized into coherent texts by instruction templates.

In the process of data construction and quality validation, we use several LLMs for data generation, data annotation, and quality evaluation for above data elements. For simplicity, we denote them uniformly as data generator, data annotator and quality evaluator. The data generator is used to generate data elements while the data annotator is labeling data type, and the quality evaluator for scoring the quality of data elements. The complete information of used LLMs (as the data generator, data annotator and quality evaluator) and prompts for data generation, data annotation and quality evaluation can be found in Appendix A.1 and A.2.

Next, we elaborate our construction strategies for each data element separately.

Question We collect questions from several open source instruction datasets (e.g., InstructWild (Ni et al., 2023), UltraFeedback (Cui et al., 2023)). The list of used instruction datasets can be found in Appendix A.3. Once data sources are collected, we use a quality evaluator to score data quality and a data annotator to label task types. We filter out questions with low quality scores and try to ensure that the data of various task types are balanced.

Response To increase the diversity of data sources and response score distributions, we use multiple data generators (e.g., Yi-1.5-6B-Chat (Young et al., 2024), Qwen2-7B-Instruct (Yang et al., 2024a)) with varying abilities to generate responses for questions. Subsequently, we use a quality evaluator to score the quality of responses and filter out low quality responses.

Evaluation Criteria For evaluation criteria, we use a hierarchical guideline and instance-level evaluation criteria. Tan et al. (2024) propose a hierarchical guideline for the evaluation of LLM evaluators. They suggest that LLM evaluators should follow
<table>
  <tr>
    <th>Data Element</th>
    <th>Question</th>
    <th>Response</th>
    <th>Evaluation Criteria</th>
    <th>Reference Response</th>
    <th>Critique and Result</th>
    <th>Avg.</th>
  </tr>
  <tr>
    <td>Human Score</td>
    <td>2.68</td>
    <td>2.77</td>
    <td>2.73</td>
    <td>2.81</td>
    <td>2.76</td>
    <td>2.75</td>
  </tr>
</table>

Table 2: Results of human scoring of the training dataset quality, where the full score is 3.

three guiding principles in their evaluations: (1) the response must follow human instructions faithfully; (2) the response should provide a factually and logically correct answer; (3) the style of the response should be consistent with human preferences. The guidelines explicitly separate objective metrics (such as instruction following and factual correctness) from subjective metrics (such as style) and influence the final evaluation result through importance level.

We use a data generator to generate evaluation criteria for each question, guided by this hierarchical guideline. Previous studies construct task-level evaluation criteria, where all questions in the same task type share a same set of evaluation criteria (Li et al., 2024c; Liu et al., 2024b; Yu et al., 2024). However, even for the same class of questions, there is still no guarantee that the multiple dimensions of evaluation criteria can be perfectly suited to each sample. We believe that such coarse-grained evaluation criteria decrease the overall data quality and may affect the performance of the evaluator. Two cases are provided in Appendix A.4 to illustrate this. We hence construct instance-level evaluation criteria with finer granularity and better relevance to questions. Finally, we use the quality evaluator to score the relevance and applicability of instance-level evaluation criteria to the corresponding questions, and evaluation criteria with low relevance are regenerated until high quality is achieved.

Reference Response Zheng et al. (2023) and Kim et al. (2024b) show that it is important to include reference responses in evaluation process to improve the evaluation accuracy of LLM evaluators. Therefore, we use two different data generators to generate reference responses. Subsequently, we use the quality evaluator to score the reference responses, and we choose the response with the highest score as the final reference response.

Critique and Result As the most important part of our data elements, we take the question, response, evaluation criteria, and reference response as input and prompt two different data generators to generate critiques and results in a chain-of-thought (CoT) way. To ensure the high quality of critiques and results, we first perform score consistency filtering, which filters out data scored inconsistently by the two data generators. Subsequently, we use a quality evaluator to examine the chain-of-thought consistency between critique and result, filtering out samples with inconsistent critique and result. Ultimately, for each sample, we randomly select one of the critiques generated by the two data generators. Additionally, considering the impact of label distribution on the model, we perform label balancing filtering to ensure a balanced distribution of labels for both pointwise grading data and pairwise comparison data.

Instruction Template Previous work has shown that the diversity of data contributes to the generalization ability of LLM (Sanh et al., 2022). To this end, we construct multiple templates of evaluation instructions by human writing and LLM generation. These templates are applied by random assignment when integrating into textual data.

![Distribution of tasks for the large-scale training dataset we have constructed.](page_1012_370_410_410.png)

Figure 3: Distribution of tasks for the large-scale training dataset we have constructed.

3.2 Curated Data

Following the data construction pipeline in Figure 2, we obtain training data containing 9 task types (Coding, Communication, Knowledge, Math, NLP_Task, Reasoning, Safety, Text_Edit and Writ-
<table>
  <tr>
    <th>Data</th>
    <th>Point-Zh</th>
    <th>Pair-Zh</th>
    <th>Point-En</th>
    <th>Pair-En</th>
    <th>SFT-Zh</th>
    <th>SFT-En</th>
    <th>Total</th>
  </tr>
  <tr>
    <th># Samples</th>
    <td>124,586</td>
    <td>374,590</td>
    <td>100,042</td>
    <td>347,958</td>
    <td>150,000</td>
    <td>150,000</td>
    <td>1,247,176</td>
  </tr>
</table>

Table 3: Statistics of the training dataset. Point denotes pointwise grading while pair for pairwise comparison.

ing), subdivided into 68 subtypes, as shown in Figure 3. Table 26 - 34 present data examples for each task.

Since the data construction process (data generation, quality validation) relies heavily on LLMs, we also conduct manual sampling to check data quality. We randomly select 100 samples for each element and ask human reviewers to score the quality of these samples. Table 2 shows the results of the human evaluation, which reveals that the overall quality of the training data is high, reflecting the effectiveness of the used data quality filtering strategies.

3.3 Supervised Fine-Tuning in a Multi-Task Learning Way

Auto-J (Li et al., 2024c) shows that augmenting the pairwise comparison data by flipping contributes to the reduction of positional bias. Inspired by this, we also augment our pairwise comparison data in this way. In addition to evaluation-oriented instruction data, we also sample general SFT data of 150K each in English and Chinese from Infinity-Instruct (Gen) (BAAI, 2024) and Magpie-Reasoning-150K (Xu et al., 2024). This is to ensure that the trained evaluator is able to learn evaluation without the cost of degraded instruction following capability. Statistics of our final training data are shown in Table 3.

We use all training data to perform supervised fine-tuning of Qwen2.5-7B-Instruct in a multi-task learning manner to obtain Praetor-7B. Details of the training hyperparameters are shown in Table 14.

For evaluation data, we use criteria-aware and -unaware training approach, where the unaware training omits evaluation criteria contained in the input of the training data, allowing the model to learn evaluation criteria implicitly from the output (Touvron et al., 2023b; Li et al., 2024c). We also consider training setups with and without reference responses. Ultimately, for each sample, we assign one of the modes with equal probability. For general SFT data, we train Praetor on them according to the normal supervised fine-tuning objective.

The multi-task training objective is formulated as:

\[
\mathcal{L} = - \frac{1}{N_1} \sum_{i=1}^{N_1} P_\theta(l_i|q_i, a_i, c_i, r_i) \\
- \frac{1}{N_2} \sum_{i=1}^{N_2} P_\theta(c_i, l_i|q_i, a_i, r_i) \\
- \frac{1}{N_3} \sum_{i=1}^{N_3} P_\theta(l_i|q_i, a_i, c_i) \\
- \frac{1}{N_4} \sum_{i=1}^{N_4} P_\theta(c_i, l_i|q_i, a_i) \\
- \frac{1}{N_5} \sum_{i=1}^{N_5} P_\theta(y_i|x_i)
\]

where \( N_1, \ldots, N_4 \) denote the number of evaluation data for the four training modes and \( N_5 \) denotes the number of general SFT data, \( x \) and \( y \) are the input and output of the general SFT data.

4 Experiments

We conducted pointwise grading (\S 4.4) and pairwise comparison (\S 4.5) experiments across multiple benchmarks (containing in-domain and out-of-domain, Chinese and English, and providing or not providing evaluation criteria) as well as ablation studies (\S 4.6) to examine Praetor.

4.1 Datasets

For pointwise grading, we used five benchmarks, which are Praetor-Test-Point, AlignBench-Point (Liu et al., 2024b), LLMEval (Zhang et al., 2024), FLASK (Ye et al., 2024), and Feedback Bench (Kim et al., 2024a). Detailed dataset information and statistics can be found in Appendix A.5. Among them, for Praetor-Test-Point and AlignBench-Point, we performed two testing modes of providing and not providing evaluation criteria.

For pairwise comparison, we used six benchmarks, which are Praetor-Test-Pair, AlignBench-Pair (Liu et al., 2024b), Auto-J-Pair (Zh) (Li et al., 2024c), Preference Bench (Kim et al., 2024b), JudgeBench (Tan et al., 2024), and Auto-J-Pair (En) (Li et al., 2024c). Detailed dataset information and statistics can be found in Appendix A.5.
<table>
  <tr>
    <th rowspan="2">Models</th>
    <th colspan="4">Praetor-Test-Point</th>
    <th colspan="4">AlignBench-Point</th>
    <th colspan="4">LLMEval</th>
    <th colspan="4">FLASK</th>
    <th colspan="4">FeedbackBench</th>
  </tr>
  <tr>
    <th>\( \rho_d \uparrow \)</th>
    <th>\( \Delta_d \downarrow \)</th>
    <th>\( \rho_t \uparrow \)</th>
    <th>\( \Delta_t \downarrow \)</th>
    <th>\( \rho_d \uparrow \)</th>
    <th>\( \Delta_d \downarrow \)</th>
    <th>\( \rho_t \uparrow \)</th>
    <th>\( \Delta_t \downarrow \)</th>
    <th>\( \rho_d \uparrow \)</th>
    <th>\( \Delta_d \downarrow \)</th>
    <th>\( \rho_t \uparrow \)</th>
    <th>\( \Delta_t \downarrow \)</th>
    <th>\( \rho_d \uparrow \)</th>
    <th>\( \Delta_d \downarrow \)</th>
    <th>\( \rho_t \uparrow \)</th>
    <th>\( \Delta_t \downarrow \)</th>
  </tr>
  <tr>
    <th colspan="19"><i>Closed-source Instruction-tuned Models</i></th>
  </tr>
  <tr>
    <td>GPT-4o mini</td>
    <td>0.87</td>
    <td>0.47</td>
    <td>0.79</td>
    <td>0.53</td>
    <td>0.81</td>
    <td>0.89</td>
    <td>0.65</td>
    <td>0.81</td>
    <td>0.69</td>
    <td>0.48</td>
    <td>0.71</td>
    <td>0.95</td>
    <td>0.75</td>
    <td>0.72</td>
  </tr>
  <tr>
    <td>GLM-4-Air</td>
    <td>0.82</td>
    <td>0.75</td>
    <td>0.70</td>
    <td>0.72</td>
    <td>0.82</td>
    <td>0.91</td>
    <td>0.72</td>
    <td>0.87</td>
    <td>0.59</td>
    <td>0.53</td>
    <td>0.73</td>
    <td>0.84</td>
    <td>0.74</td>
    <td>0.69</td>
  </tr>
  <tr>
    <th colspan="19"><i>Open-source Instruction-tuned Models</i></th>
  </tr>
  <tr>
    <td>Qwen-2.7B-Instruct</td>
    <td>0.71</td>
    <td>0.92</td>
    <td>0.59</td>
    <td>0.99</td>
    <td>0.62</td>
    <td>0.84</td>
    <td>0.45</td>
    <td>0.85</td>
    <td>0.53</td>
    <td>0.50</td>
    <td>0.47</td>
    <td>1.12</td>
    <td>0.61</td>
    <td>0.83</td>
  </tr>
  <tr>
    <td>Qwen-2.5-7B-Instruct</td>
    <td>0.77</td>
    <td>0.88</td>
    <td>0.67</td>
    <td>0.89</td>
    <td>0.68</td>
    <td>0.94</td>
    <td>0.62</td>
    <td>0.73</td>
    <td>0.56</td>
    <td>0.54</td>
    <td>0.66</td>
    <td>0.95</td>
    <td>0.66</td>
    <td>0.79</td>
  </tr>
  <tr>
    <td>InternLM2.5-7B-Chat</td>
    <td>0.76</td>
    <td>0.90</td>
    <td>0.65</td>
    <td>0.95</td>
    <td>0.65</td>
    <td>0.98</td>
    <td>0.60</td>
    <td>0.81</td>
    <td>0.51</td>
    <td>0.59</td>
    <td>0.61</td>
    <td>1.03</td>
    <td>0.64</td>
    <td>0.81</td>
  </tr>
  <tr>
    <td>Llama-3.1-8B-Instruct</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>0.69</td>
    <td>0.87</td>
    <td>0.65</td>
    <td>0.77</td>
  </tr>
  <tr>
    <th colspan="19"><i>Evaluator Models</i></th>
  </tr>
  <tr>
    <td>Auto-J-Bilingual-6B</td>
    <td>0.61</td>
    <td>1.14</td>
    <td>0.51</td>
    <td>1.11</td>
    <td>0.54</td>
    <td>1.14</td>
    <td>0.43</td>
    <td>1.10</td>
    <td>0.53</td>
    <td>1.17</td>
    <td>0.44</td>
    <td>1.43</td>
    <td>0.58</td>
    <td>1.04</td>
  </tr>
  <tr>
    <td>Auto-J-13B</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>0.49</td>
    <td>1.13</td>
    <td>0.65</td>
    <td>0.89</td>
  </tr>
  <tr>
    <td>CritiqueLLM-6B</td>
    <td>0.67</td>
    <td>1.98</td>
    <td>0.59</td>
    <td>1.99</td>
    <td>0.69</td>
    <td>2.16</td>
    <td>0.61</td>
    <td>2.16</td>
    <td>0.58</td>
    <td>2.03</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Prometheus-7B-v1.0</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>0.47</td>
    <td>0.99</td>
    <td>0.85</td>
    <td>0.46</td>
  </tr>
  <tr>
    <td>Prometheus-7B-v2.0</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>0.48</td>
    <td>0.97</td>
    <td><b>0.88</b></td>
    <td><b>0.40</b></td>
  </tr>
  <tr>
    <td>Themis</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>0.47</td>
    <td>1.23</td>
    <td>0.68</td>
    <td>1.09</td>
  </tr>
  <tr>
    <td>CJ-1-7B</td>
    <td>0.82</td>
    <td>1.09</td>
    <td>0.75</td>
    <td>0.91</td>
    <td>0.71</td>
    <td>0.85</td>
    <td>0.57</td>
    <td>0.86</td>
    <td>0.64</td>
    <td>0.45</td>
    <td>0.65</td>
    <td>0.85</td>
    <td>0.84</td>
    <td>0.65</td>
  </tr>
  <tr>
    <td><b>Praetor-7B (Ours)</b></td>
    <td><b>0.88</b></td>
    <td><b>0.48</b></td>
    <td><b>0.81</b></td>
    <td><b>0.51</b></td>
    <td><b>0.81</b></td>
    <td><b>0.72</b></td>
    <td><b>0.68</b></td>
    <td><b>0.75</b></td>
    <td><b>0.66</b></td>
    <td><b>0.56</b></td>
    <td><b>0.69</b></td>
    <td><b>0.85</b></td>
    <td><b>0.84</b></td>
    <td><b>0.49</b></td>
  </tr>
</table>

Table 4: Correlation coefficient (\( \rho \)) and absolute score difference (\( \Delta \)) in pointwise grading. The subscript \( d \) indicates that the corresponding metrics are calculated based on scores from multiple evaluation dimensions when the evaluation criteria are provided. The subscript \( t \) indicates that the corresponding metrics are calculated based on total scores when the evaluation criteria are not provided. **Bold** indicates the best results except for the closed-source instruction-tuned models, and _underline_ indicates the best results for all models. - indicates that model does not support the language of testset.

Among them, Praetor-Test-Pair, AlignBench-Pair and Preference Bench provide evaluation criteria, while the remaining three benchmarks do not provide evaluation criteria.

4.2 Metrics

For pointwise grading, we used the correlation coefficient to measure the trend correlation between LLM evaluator scores and gold labels. Moreover, we also calculated the absolute score difference between LLM evaluator scores and gold labels, which was used to measure the absolute gap.

For pairwise comparison, to visually measure positional bias, we conducted two evaluations by swapping the order of the two responses. Specifically, we used consistency rate to measure the consistency of the two evaluations (two comparisons with the same results) and agreement rate to measure the consistent accuracy of the two evaluations (two comparisons with the same results and consistent with the gold label).

4.3 Baselines

We selected state-of-the-art general LLMs (including closed-source instruction-tuned LLMs and open-source instruction-tuned LLMs) and evaluator LLMs as our baselines, which are as follows:

• General LLMs: For closed-source instruction-tuned LLMs, we adopted GPT-4o mini (OpenAI, 2023) and GLM-4-Air. For open-source instruction-tuned LLMs, we used Qwen-2.7B-Instruct (Yang et al., 2024a), Qwen-2.5-7B-Instruct (Yang et al., 2024b), InternLM2.5-7B-Chat (Cai et al., 2024) and Llama-3.1-8B-Instruct (Dubey et al., 2024) as our baselines. These LLMs were directly prompted to perform evaluation through well-designed instruction templates, as shown in Appendix A.2.

• Evaluator LLMs: We selected several mainstream evaluator LLMs for comparison, including Auto-J-Bilingual-6B (Li et al., 2024c), Auto-J-13B (Li et al., 2024c), CritiqueLLM-6B (Ke et al., 2024), Prometheus-7B-v1.0 (Kim et al., 2024a), Prometheus-7B-v2.0 (Kim et al., 2024b), PandaLM (Wang et al., 2024), Themis (Hu et al., 2024), and CompassJudger-1 (CJ-1-7B) (Cao et al., 2024). For these evaluator LLMs, we used their original instruction templates for evaluation.

4.4 Results: Pointwise Grading

Results for pointwise grading are presented in Table 4, which demonstrate that Praetor outperforms
<table>
  <tr>
    <th rowspan="2">Models</th>
    <th colspan="2">Praetor-Test-Pair</th>
    <th colspan="2">AlignBench-Pair</th>
    <th colspan="2">AutoJ-Pair(Zh)</th>
    <th colspan="2">PreferenceBench</th>
    <th colspan="2">JudgeBench</th>
    <th colspan="2">AutoJ-Pair(En)</th>
    <th rowspan="2">Avg.</th>
  </tr>
  <tr>
    <th>Cons.↑</th><th>Agr.↑</th>
    <th>Cons.↑</th><th>Agr.↑</th>
    <th>Cons.↑</th><th>Agr.↑</th>
    <th>Cons.↑</th><th>Agr.↑</th>
    <th>Cons.↑</th><th>Agr.↑</th>
    <th>Cons.↑</th><th>Agr.↑</th>
  </tr>
  <tr>
    <td colspan="14"><i>Close-source Instruction-tuned Models</i></td>
  </tr>
  <tr>
    <td>GPT-4o mini</td><td>74.8</td><td>67.9</td><td>87.6</td><td>77.6</td><td>74.0</td><td>50.7</td><td>90.4</td><td>86.6</td><td>69.1</td><td>48.6</td><td>79.9</td><td>50.1</td><td>79.3</td><td>65.1</td>
  </tr>
  <tr>
    <td>GLM-4-Air</td><td>62.5</td><td>56.6</td><td>82.2</td><td>73.8</td><td>63.6</td><td>43.5</td><td>91.4</td><td>86.7</td><td>54.9</td><td>41.5</td><td>69.0</td><td>53.6</td><td>70.6</td><td>59.3</td>
  </tr>
  <tr>
    <td colspan="14"><i>Open-source Instruction-tuned Models</i></td>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td><td>53.9</td><td>44.8</td><td>70.1</td><td>61.4</td><td>57.0</td><td>38.5</td><td>83.3</td><td>79.6</td><td>40.3</td><td>25.2</td><td>67.4</td><td>44.0</td><td>62.0</td><td>48.9</td>
  </tr>
  <tr>
    <td>Qwen2.5-7B-Instruct</td><td>56.3</td><td>49.0</td><td>73.3</td><td>66.9</td><td>63.9</td><td>42.5</td><td>85.7</td><td>80.7</td><td>42.5</td><td>25.6</td><td>59.1</td><td>40.8</td><td>63.5</td><td>50.9</td>
  </tr>
  <tr>
    <td>InternLM2.5-7B-Chat</td><td>56.4</td><td>48.8</td><td>72.6</td><td>64.6</td><td>61.1</td><td>40.9</td><td>79.4</td><td>75.6</td><td>37.3</td><td>23.8</td><td>52.5</td><td>34.5</td><td>59.9</td><td>48.4</td>
  </tr>
  <tr>
    <td>Llama-3-13B-Instruct</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>75.3</td><td>72.8</td><td>54.4</td><td>35.4</td><td>69.0</td><td>41.2</td><td>66.2</td><td>49.8</td>
  </tr>
  <tr>
    <td colspan="14"><i>Evaluator Models</i></td>
  </tr>
  <tr>
    <td>Auto-J-Bilingual-6B</td><td>74.3</td><td>58.2</td><td>79.8</td><td>64.6</td><td>77.9</td><td>50.0</td><td>64.9</td><td>61.4</td><td>55.4</td><td>33.5</td><td>80.9</td><td>52.5</td><td>72.2</td><td>53.4</td>
  </tr>
  <tr>
    <td>Auto-J-13B</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>76.5</td><td>72.5</td><td>57.7</td><td>36.1</td><td>82.9</td><td>54.6</td><td>72.4</td><td>54.4</td>
  </tr>
  <tr>
    <td>CritiqueLLM-6B</td><td>74.3</td><td>51.0</td><td>79.7</td><td>58.3</td><td>78.8</td><td>50.8</td><td>82.0</td><td>71.5</td><td>42.9</td><td>13.6</td><td>68.3</td><td>43.7</td><td>71.1</td><td>48.2</td>
  </tr>
  <tr>
    <td>PandaLM</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>57.3</td><td>32.6</td><td>14.7</td><td>64.2</td><td>38.7</td><td>42.7</td><td>28.0</td>
  </tr>
  <tr>
    <td>Thriceus-7B-v2.0</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>91.8</td><td>88.8</td><td>68.3</td><td>53.1</td><td>74.7</td><td>47.3</td><td>73.9</td><td>57.1</td>
  </tr>
  <tr>
    <td>CJ-1-7B</td><td>75.6</td><td>67.9</td><td>84.1</td><td>76.4</td><td>77.3</td><td>52.0</td><td>92.1</td><td>87.3</td><td>71.3</td><td>49.2</td><td>82.0</td><td>55.5</td><td>80.4</td><td>64.7</td>
  </tr>
  <tr>
    <td><b>Praetor-7B (Ours)</b></td><td><b>80.5</b></td><td><b>73.1</b></td><td><b>87.2</b></td><td><b>77.7</b></td><td><b>75.8</b></td><td><b>50.8</b></td><td><b>91.7</b></td><td><b>87.3</b></td><td><b>74.6</b></td><td><b>49.1</b></td><td><b>85.6</b></td><td><b>55.9</b></td><td><b>82.2</b></td><td><b>65.7</b></td>
  </tr>
</table>

Table 5: Consistency (Cons.) and agreement (Agr.) rates in pairwise comparison. - indicates that model does not support the language of testset.

the open-source instruction-tuned models and previous evaluator models on multiple benchmarks. Praetor performs best not only on the in-domain testset (i.e., Praetor-Test-Point), but also on multiple out-of-domain testsets, exhibiting excellent generalization capabilities. Moreover, it achieves the best results in both settings of providing and not providing evaluation criteria.1

The only exception is Feedback Bench, where the best results are achieved by Prometheus. However, this benchmark is the in-domain testset for Prometheus. Nevertheless, Praetor’s performance on this benchmark is still the closest to that of Prometheus among all tested evaluator models. Compared to closed-source instruction-tuned models, Praetor is competitive to or even better than these models, indicating its excellent capabilities in pointwise grading.

Additionally, we find that Auto-J and CritiqueLLM-6B have a high absolute score difference compared to other models, but the correlation coefficients are located at normal levels. This is due to the fact that we used benchmarks with 5-point scales, however, these two models only support 10-point scales. This limitation of mode fixation shaped during the training stage leads to a decrease in the ability of instruction following. In contrast, Praetor and CJ-1 perform better. We further explored the generalization capabilities of these LLM evaluators across multiple score scales in pointwise grading. Results are shown in Appendix A.6, which show similar trends.

4.5 Results: Pairwise Comparison

Results for pairwise comparison are reported in Table 5. Again, Praetor outperforms baseline models on multiple benchmarks (both in-domain and out-of-domain). Moreover, it achieves the best results in both settings of providing and not providing evaluation criteria.

It is worth noting that even the advanced closed-source instruction-tuned models face the problem of positional bias in pairwise comparison and exhibit low consistency rate. In contrast, LLM evaluators such as Praetor and Auto-J have better consistency performance, which is attributed to the position-flipping data augmentation strategy. For this reason, compared with pointwise grading, LLM evaluators trained with the position-flipping data augmentation strategy show greater advantages in pairwise comparison, thus narrowing the gap with closed-source instruction-tuned models or even outperforming them. Overall, Praetor still has excellent capabilities in pairwise comparison.

4.6 Ablation Study

We conducted ablation experiments on Praetor’s training data to verify the effectiveness of our data quality control, and then to examine the impact of data composition and granularity of the evaluation criteria on the performance of Praetor. In addition, considering the superior performance of Qwen2.5-7B-Instruct which is the base model used by Praetor for LLMs of the same size, we also conducted experiments using other LLMs as the base model. All results are shown in Table 6.

1Since some LLM evaluators do not support fine-grained dimension scoring, we use the overall scores to compute the corresponding metrics, as done in previous work.
<table>
  <tr>
    <th rowspan="2">Setting</th>
    <th colspan="2">Pointwise</th>
    <th colspan="2">Pairwise</th>
  </tr>
  <tr>
    <th>\( \mu \uparrow \)</th>
    <th>\( \Delta \downarrow \)</th>
    <th>Cons.\( \uparrow \)</th>
    <th>Agr.\( \uparrow \)</th>
  </tr>
  <tr>
    <td>CJ-1-7B</td>
    <td>0.71</td>
    <td>0.81</td>
    <td>80.4</td>
    <td>64.7</td>
  </tr>
  <tr>
    <td><b>Praetor-7B</b></td>
    <td><b>0.76</b></td>
    <td><b>0.62</b></td>
    <td><b>82.2</b></td>
    <td><b>65.7</b></td>
  </tr>
  <tr>
    <th colspan="5"><i>Data Quality Control</i></th>
  </tr>
  <tr>
    <td>w/o Filtration</td>
    <td>0.70</td>
    <td>0.68</td>
    <td>77.5</td>
    <td>62.8</td>
  </tr>
  <tr>
    <th colspan="5"><i>Data Composition</i></th>
  </tr>
  <tr>
    <td>w/o SFT Data</td>
    <td>0.72</td>
    <td>0.70</td>
    <td>80.3</td>
    <td>63.5</td>
  </tr>
  <tr>
    <td>w/o Flipped Pairwise Data</td>
    <td>0.75</td>
    <td>0.63</td>
    <td>74.1</td>
    <td>60.7</td>
  </tr>
  <tr>
    <th colspan="5"><i>Evaluation Criteria</i></th>
  </tr>
  <tr>
    <td>Task-Level Criteria</td>
    <td>0.73</td>
    <td>0.65</td>
    <td>80.5</td>
    <td>63.4</td>
  </tr>
  <tr>
    <th colspan="5"><i>Base Model</i></th>
  </tr>
  <tr>
    <td>Qwen2-7B-Instruct</td>
    <td>0.75</td>
    <td>0.63</td>
    <td>81.9</td>
    <td>65.5</td>
  </tr>
  <tr>
    <td>InternLM2.5-7B-Chat</td>
    <td>0.74</td>
    <td>0.64</td>
    <td>82.0</td>
    <td>64.9</td>
  </tr>
  <tr>
    <td>Mistral-7B-Instruct-v0.3</td>
    <td>0.73</td>
    <td>0.64</td>
    <td>80.8</td>
    <td>64.1</td>
  </tr>
  <tr>
    <td>Llama-3.1-8B-Instruct</td>
    <td>0.70</td>
    <td>0.68</td>
    <td>78.5</td>
    <td>62.1</td>
  </tr>
</table>

Table 6: Results of ablation experiments.

Data Quality Control  We randomly selected the same amount of data for training from the initially constructed dataset without quality filtering. The results in Table 6 show that the evaluator trained on data without quality filtering performs lower than Praetor when the amount of training data is equal, demonstrating the effectiveness of our data quality control method.

Data Composition  When general SFT data is not included in the training data, it decreases the effectiveness of Praetor on both evaluation tasks. This suggests that general SFT data maintains the evaluator’s ability of instruction following, which also facilitates evaluation. This is consistent with the findings by Cao et al. (2024). When the training data do not include flipped pairwise comparison data, we find little effect on pointwise grading, but a significant impact on pairwise comparison, especially the consistency rate.

Evaluation Criteria  Since Praetor’s training data contain instance-level evaluation criteria, we constructed a set of task-level evaluation criteria for these data, where each task type shares a set of evaluation criteria. Results show that evaluators trained with task-level evaluation criteria data do not outperform Praetor. This finding suggests that constructing finer-grained evaluation criteria in the training data enables evaluators to learn more detailed evaluation capabilities, thus enhancing their evaluation performance.

Base Model  We find that the performance of using Qwen2-7B-Instruct and InternLM2.5-7B-Chat as the base model is not as good as that of Praetor, but still better than the performance of CJ-1-7B with Qwen2.5-7B-Instruct as the base model. When using Mistral-7B-Instruct-v0.3 (Jiang et al., 2023) and Llama-3.1-8B-Instruct as the base model, the evaluator performance decreases slightly, which may be due to the weaker Chinese language capability of these models. These results suggest that the capability of the base model does have an impact on evaluator performance. However, we are able to narrow the gap between the base models through our evaluation data and training method, proving the effectiveness and necessity of our training data and training method.

![Critique quality evaluation results.](page_1012_180_388_312.png)

Figure 4: Critique quality evaluation results.

5 Analysis

In this section, we evaluated the quality of critiques generated by Praetor (\S 5.1) and subsequently analyzed the exploration critiques generated by Praetor as feedback to improve LLMs (\S 5.2). We also tested the potential of Praetor for system-level evaluation in Appendix A.7, and analyzed the length bias performance of Praetor in Appendix A.8.

5.1 Analysis of Critique Quality

To measure the quality of critiques generated by Praetor, we conducted both LLM evaluation and human evaluation. Specifically, we randomly selected 100 samples from multiple benchmarks, and then collected and compared critiques from Praetor and three better performing models (CJ-1-7B, ChatGPT, GPT-4o). Given the same evaluation inputs, for each pair of critiques (one from Praetor and the other from the other models), DeepSeek-V2.5 and the human evaluator were asked to judge which critique is better (win, lose, tie) in terms of correctness, usefulness, and informativeness (Ke et al., 2024). The prompts can be found in Appendix A.2.
<table>
  <tr>
    <th>Models</th>
    <th>Coding</th>
    <th>Communication</th>
    <th>Knowledge</th>
    <th>Math</th>
    <th>NLP_Task</th>
    <th>Reasoning</th>
    <th>Safety</th>
    <th>Text_Edit</th>
    <th>Writing</th>
    <th>Avg.</th>
  </tr>
  <tr>
    <td>Baichuan2-7B-Chat</td>
    <td>1.78</td>
    <td>3.12</td>
    <td>2.87</td>
    <td>1.64</td>
    <td>2.59</td>
    <td>1.87</td>
    <td>2.88</td>
    <td>3.01</td>
    <td>2.94</td>
    <td>2.52</td>
  </tr>
  <tr>
    <td>Baichuan2-7B-Chat-Refine</td>
    <td>1.88</td>
    <td>3.47</td>
    <td>3.18</td>
    <td>1.51</td>
    <td>2.91</td>
    <td>1.93</td>
    <td>3.23</td>
    <td>3.31</td>
    <td>3.26</td>
    <td>2.74 (+0.22)</td>
  </tr>
  <tr>
    <td>Qwen1.5-7B-Chat</td>
    <td>2.54</td>
    <td>3.15</td>
    <td>3.10</td>
    <td>2.81</td>
    <td>3.18</td>
    <td>2.77</td>
    <td>3.45</td>
    <td>3.31</td>
    <td>3.29</td>
    <td>3.07</td>
  </tr>
  <tr>
    <td>Qwen1.5-7B-Chat-Refine</td>
    <td>3.01</td>
    <td>3.47</td>
    <td>3.43</td>
    <td>3.14</td>
    <td>3.47</td>
    <td>2.93</td>
    <td>3.94</td>
    <td>3.58</td>
    <td>3.48</td>
    <td>3.38 (+0.31)</td>
  </tr>
  <tr>
    <td>Qwen2.5-7B-Instruct</td>
    <td>3.21</td>
    <td>3.71</td>
    <td>3.52</td>
    <td>3.13</td>
    <td>3.45</td>
    <td>3.06</td>
    <td>3.71</td>
    <td>3.45</td>
    <td>3.47</td>
    <td>3.41</td>
  </tr>
  <tr>
    <td>Qwen2.5-7B-Instruct-Refine</td>
    <td>3.47</td>
    <td>3.93</td>
    <td>3.61</td>
    <td>3.34</td>
    <td>3.79</td>
    <td>3.14</td>
    <td>4.04</td>
    <td>3.67</td>
    <td>3.67</td>
    <td>3.63 (+0.22)</td>
  </tr>
</table>

Table 7: Results for three LLMs utilizing Praetor’s feedback to improve their outputs.

Results are shown in Figure 4. We observe that Praetor can achieve performance superior to CJ-1-7B and ChatGPT, and even comparable to GPT-4o. These results demonstrate that the critiques generated by Praetor are of high quality.

5.2 Analysis of Critique as Feedback

To investigate whether Praetor-generated critiques can be used as feedback to improve the quality of LLM outputs, we randomly selected 30 problems from each task of Praetor-Test-Point, and let Baichuan2-7B-Chat (Yang et al., 2023a), Qwen1.5-7B-Chat (Bai et al., 2023), and Qwen2.5-7B-Instruct generate responses, and subsequently let Praetor generate critiques. Finally, we feedback the critiques to original LLMs, prompting them to modify their previous responses based on the critiques. Results are shown in Table 7 , which clearly demonstrate critiques from Praetor can be used as positive feedback to help three LLMs improve their original outputs. This validates the effectiveness of Praetor in providing informative critiques as scalable feedback.

By comparing the effect of improvements on different tasks, we find that improvements are lower on Coding, Math, and Reasoning tasks than other tasks. We checked the output of Praetor and observed that, for some complex questions, it was not able to provide helpful critiques, and sometimes even misguidance, which prevented the improvement of LLMs. Therefore, in the future, the focus should be on enhancing the ability of LLM evaluators to evaluate complex tasks.

By comparing the improvements of the different models, we find that the best (Qwen2.5-7B-Instruct) and the lowest (Baichuan2-7B-Chat) performing model before the modification achieve the same improvement (+0.22), while the middle performing model (Qwen1.5-7B-Chat) achieve the highest improvement (+0.31). By checking their outputs, we find that Baichuan2-7B-Chat should have the most improvement space, but due to its weak instruction following capacity, it is not able to make good use of critiques to improve its output. For Qwen2.5-7B-Instruct, the smaller improvement space limits its improvement because it already generates high-quality responses. In contrast, Qwen1.5-7B-Chat has more improvement space, as well as good instruction following capability, and thus is able to achieve a greater improvement.

In summary, how to use LLM evaluators to help lower-capability models understand and follow instructions and thus achieve improvement, and to help higher-capability models break through the upper limit of the improvement space, should likewise be an important future work.

6 Conclusion

In this paper, we have constructed a large-scale training dataset for LLM-powered evaluators and developed a fine-grained generative LLM evaluator with instance-level customizable evaluation criteria, Praetor, on the created dataset. The dataset contains ~947K samples covering 9 task types, 68 subtypes, and its quality is ensured through the collaboration between human and LLMs. Praetor is trained through a multi-task learning manner, which equips Praetor with more comprehensive evaluation capabilities and better flexibility than previous LLM evaluators. Extensive experiments have shown that Praetor not only performs well in a variety of evaluation tasks and settings, but also demonstrates the potential for generating critiques as feedback to improve LLM outputs.

Limitations

In the process of constructing large-scale training data, despite the careful data quality filtering and validation strategies we have implemented, the source of these data still relies on LLMs, which may result in biases inherited from these LLMs in the created data. However, we do not have a good way to locate or detect these biases in the data because we do not know exactly which biases are present. It is worth noting that distilling
data through LLMs is currently an extremely common data construction strategy, so this problem is universal. However, we believe that this problem deserves to be examined in greater depth and may lead to new breakthroughs for LLMs (e.g., for their self-improvement).

Acknowledgments

The present research was supported by the National Key Research and Development Program of China (Grant No. 2024YFE0203000). We would like to thank the anonymous reviewers for their insightful comments.

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A Appendix

A.1 LLMs for Data Construction
During the data construction process, we use multiple LLMs as follows.

• Question: We use Qwen2.5-72B-Instruct (Yang et al., 2024b) as data annotator and quality evaluator.

• Response: We use Yi-1.5-6B-Chat (Young et al., 2024), Yi-1.5-9B-Chat, Yi-34B-Chat, Yi-1.5-34B-Chat, GLM-4-9B-Chat (Zeng et al., 2024), Qwen2-7B-Instruct (Yang et al., 2024a), Qwen2-72B-Instruct, Qwen2.5-7B-Instruct (Yang et al., 2024b), Qwen2.5-14B-Instruct, Qwen2.5-72B-Instruct, GLM-4-Air,2 Deepseek-V2.5 (DeepSeek-AI et al., 2024) as data generators and Qwen2.5-72B-Instruct as quality evaluator.

• Evaluation Criteria: We use GLM-4-Air as data generator and Qwen2.5-72B-Instruct as quality evaluator.

• Reference Response: We use GLM-4-Air and Deepseek-V2.5 as data generators and Qwen2.5-72B-Instruct as quality evaluator.

• Critique and Result: We use GLM-4-Air and Deepseek-V2.5 as data generators and Qwen2.5-72B-Instruct as quality evaluator.

A.2 Prompts
We use several prompt templates during the data construction process. Table 17 and Table 18 display prompts related to questions, Table 19 provides prompt related to responses, Table 20 and Table 21 list prompts related to evaluation criteria, and Table 22 present prompt related to critiques. Table 23 and Table 24 show prompts used in the instruction-tuned model and Praetor, and both templates are also used for the evaluation of the reference responses. Table 25 provides the prompt used in Section 5.1 to evaluate critiques.

A.3 Data Sources
We collect questions from multiple open source datasets.

For Chinese, source datasets include: Evol-Instruct-Chinese-GPT4,3 Magpie-Qwen2-Pro-200K-Chinese (Xu et al., 2024), InstructWild (Ni et al., 2023), COIG-CQIA (Bai et al., 2024), OpenLabel-Chinese Conversations Dataset (OLCC) (BAAI, 2023), RefGPT-Code (Yang et al., 2023b), Infinity-Instruct (BAAI, 2024), CValues (Xu et al., 2023) and Safety-Prompts (Sun et al., 2023).

For English, source datasets include UltraFeedback (Cui et al., 2023) and preference_700K (Dong et al., 2024).

A.4 Case Studies of Task-Level Evaluation Criteria
Table 13 shows two samples for the task-level evaluation criteria.

In the first sample: For question 1, the factual correctness dimension is critical because the narrative of the current state of AI development and future trends must be accurate and based in reality. For question 2, factual correctness is not appropriate, because poem can be exaggerated, imagined, or metaphorical, and does not have to strictly meet the satisfaction of factual correctness.

In the second sample: For question 1, novel and interesting travel itineraries can be creatively planned to make the recommendations more valuable, so creativity matters. For question 2, the content of the recommended Tang poem is an existing classic, and creativity is not important.

Based on the above samples, we can find that the task-level evaluation criteria do not guarantee that the multiple dimensions of evaluation criteria can be perfectly suited to each sample.

A.5 Benchmarks
Detailed informations for five pointwise grading benchmarks are as follows (statistical information is provided in Table 8):

• Praetor-Test-Point: As the in-domain testset of Praetor, we use GPT-4o (OpenAI, 2023) and DeepSeek-V2.5 (DeepSeek-AI et al., 2024) as judges for scoring, and consistent scores are used as the final labels. The final testset contains 9 task types with 100 samples for each task, totaling 900 samples. Each sample has an independent set of evaluation criteria which contains multiple dimensions.

• AlignBench-Point (Liu et al., 2024b): We use AlignBench’s question data which contains 8 task types, and then generate responses via Baichuan2-7B-Chat (Yang et al.,
<table>
  <tr>
    <th>Dataset</th>
    <th>Praetor-Test-Point</th>
    <th>AlignBench-Point</th>
    <th>LLMEval</th>
    <th>FLASK</th>
    <th>Feedback Bench</th>
  </tr>
  <tr>
    <td>Criteria Level</td>
    <td>Instance-level</td>
    <td>Task-level</td>
    <td>General-level</td>
    <td>Instance-level</td>
    <td>Instance-level</td>
  </tr>
  <tr>
    <td>Criteria Numbers Per Sample</td>
    <td>>1</td>
    <td>>1</td>
    <td>5</td>
    <td>3</td>
    <td>1</td>
  </tr>
  <tr>
    <td>Reference Response</td>
    <td>✓</td>
    <td>✓</td>
    <td>✗</td>
    <td>✓</td>
    <td>✓</td>
  </tr>
  <tr>
    <td>Language</td>
    <td>zh</td>
    <td>zh</td>
    <td>zh</td>
    <td>en</td>
    <td>en</td>
  </tr>
  <tr>
    <td># Samples</td>
    <td>900</td>
    <td>1,242</td>
    <td>845</td>
    <td>1,000</td>
    <td>1,000</td>
  </tr>
</table>

Table 8: Statistics for five pointwise grading benchmarks.

<table>
  <tr>
    <th>Dataset</th>
    <th>Praetor-Test-Pair</th>
    <th>AlignBench-Pair</th>
    <th>Auto-J-Pair (zh)</th>
    <th>Preference Bench</th>
    <th>JudgeBench</th>
    <th>Auto-J-Pair (en)</th>
  </tr>
  <tr>
    <td>Criteria</td>
    <td>✓</td>
    <td>✓</td>
    <td>✗</td>
    <td>✓</td>
    <td>✗</td>
    <td>✗</td>
  </tr>
  <tr>
    <td>Reference Response</td>
    <td>✓</td>
    <td>✓</td>
    <td>✗</td>
    <td>✓</td>
    <td>✗</td>
    <td>✗</td>
  </tr>
  <tr>
    <td>Language</td>
    <td>zh</td>
    <td>zh</td>
    <td>zh</td>
    <td>en</td>
    <td>en</td>
    <td>en</td>
  </tr>
  <tr>
    <td># Samples</td>
    <td>900</td>
    <td>852</td>
    <td>1,392</td>
    <td>1,000</td>
    <td>350</td>
    <td>1,392</td>
  </tr>
</table>

Table 9: Statistics for six pairwise comparison benchmarks.

2023a), Baichuan2-13B-Chat, Qwen2-7B-Instruct (Yang et al., 2024a), ChatGLM3-6B (Zeng et al., 2024), and Yi-1.5-6B-Chat (Young et al., 2024). We still use GPT-4o and DeepSeek-V2.5 as judges for scoring, and consistent scores were used as the final labels. The final contains 1,242 samples, where each type of task data has a separate set of evaluation criteria which contains multiple dimensions.

• LLMEval (Zhang et al., 2024): Contains 17 task types which cover representative NLP tasks in real-world scenarios, and provides scoring labels for crowdsourcing workers. We randomly select 50 samples for each task type, totaling 845 samples. All samples share the same set of evaluation criteria which contains multiple dimensions.

• FLASK (Ye et al., 2024): It is a fine-grained benchmark containing 10 domains and 12 skills. We randomly select response data from 5 models (Alpaca-13B, Llama-2-13B-Chat, Tulu-13B, Vicuna-13B, and WizardLM-13B) labeled by GPT-4 as the testset with a total of 1,000 samples. Each sample has independent evaluation criteria and contains multiple dimensions.

• Feedback Bench (Kim et al., 2024a): As the in-domain testset of Prometheus, there are 1,000 samples, each sample with a separate evaluation criteria.

Detailed informations for six pairwise comparison benchmarks are as follows (statistical information is provided in Table 9):

• Praetor-Test-Pair: As the in-domain testset of Praetor, it is similar to the Praetor-Test-Point construction process and contains 9 task types with a total of 900 samples.

• AlignBench-Pair (Liu et al., 2024b): Based on AlignBench-Point, for a question, we randomly combine two responses from two different models to construct a response pair. After labeling balanced filtering, it finally contains 852 samples.

• Auto-J-Pair (Zh) (Li et al., 2024c): As the in-domain testset of Auto-J (Eval-P), it contains 58 realistic scenarios, labeled by manually annotated preferences, with 1,392 samples.

• Preference Bench (Kim et al., 2024b): As the in-domain testset of Prometheus 2, response pairs are constructed with different responses to a single question from Feedback Bench, containing a total of 2,000 samples. We randomly select 1,000 samples for testing.

• JudgeBench (Tan et al., 2024): It is a challenging benchmark containing 4 high-level tasks (Knowledge, Reasoning, Math, and Coding), preference labels annotated by GPT-4, with a total of 350 samples.

• Auto-J-Pair (En) (Li et al., 2024c): Translation Auto-J-Pair (zh) into English.

A.6 Multiple Score Scales for Pointwise Grading

We reconstructed the score labels for Praetor-Test-Point with ranges of 3, 7, and 10. We then performed pointwise grading experiments with mul-
<table>
  <tr>
    <th rowspan="2">Models</th>
    <th colspan="2">3-point scale</th>
    <th colspan="2">5-point scale</th>
    <th colspan="2">7-point scale</th>
    <th colspan="2">10-point scale</th>
  </tr>
  <tr>
    <th>\( \rho_d \uparrow \)</th>
    <th>\( \Delta_d \downarrow \)</th>
    <th>\( \rho_d \uparrow \)</th>
    <th>\( \Delta_d \downarrow \)</th>
    <th>\( \rho_d \uparrow \)</th>
    <th>\( \Delta_d \downarrow \)</th>
    <th>\( \rho_d \uparrow \)</th>
    <th>\( \Delta_d \downarrow \)</th>
  </tr>
  <tr>
    <td>Auto-J-Bilingual-6B</td>
    <td>0.58</td>
    <td>1.92</td>
    <td>0.61</td>
    <td>1.14</td>
    <td>0.63</td>
    <td>1.95</td>
    <td>0.71</td>
    <td>1.88</td>
  </tr>
  <tr>
    <td>CritiqueLLM-6B</td>
    <td>0.63</td>
    <td>3.05</td>
    <td>0.67</td>
    <td>1.98</td>
    <td>0.69</td>
    <td>1.55</td>
    <td>0.75</td>
    <td>2.00</td>
  </tr>
  <tr>
    <td>CJ-1-7B</td>
    <td>0.78</td>
    <td>0.56</td>
    <td>0.82</td>
    <td>1.09</td>
    <td>0.79</td>
    <td>1.13</td>
    <td>0.81</td>
    <td>1.61</td>
  </tr>
  <tr>
    <td><b>Praetor-7B (Ours)</b></td>
    <td><b>0.83</b></td>
    <td><b>0.41</b></td>
    <td><b>0.88</b></td>
    <td><b>0.48</b></td>
    <td><b>0.86</b></td>
    <td><b>0.76</b></td>
    <td><b>0.85</b></td>
    <td><b>1.22</b></td>
  </tr>
</table>

Table 10: Results of pointwise grading for multiple score scales.

<table>
  <tr>
    <th rowspan="2">Models</th>
    <th colspan="2">AlpacaEval 1.0</th>
    <th colspan="2">AlpacaEval 2.0</th>
  </tr>
  <tr>
    <th>r</th>
    <th>\( \rho \)</th>
    <th>r</th>
    <th>\( \rho \)</th>
  </tr>
  <tr>
    <td>Prometheus-7B-v1.0</td>
    <td>0.91</td>
    <td>0.91</td>
    <td>0.75</td>
    <td>0.84</td>
    <td>0.84</td>
    <td>0.66</td>
  </tr>
  <tr>
    <td>Prometheus-7B-v2.0</td>
    <td>0.92</td>
    <td>0.92</td>
    <td>0.76</td>
    <td>0.86</td>
    <td>0.86</td>
    <td>0.68</td>
  </tr>
  <tr>
    <td>Prometheus-13B-v1.0</td>
    <td>0.93</td>
    <td>0.93</td>
    <td>0.77</td>
    <td>0.85</td>
    <td>0.85</td>
    <td>0.66</td>
  </tr>
  <tr>
    <td>Auto-J-Bilingual-6B</td>
    <td>0.96</td>
    <td>0.96</td>
    <td>0.82</td>
    <td>0.88</td>
    <td>0.88</td>
    <td>0.68</td>
  </tr>
  <tr>
    <td>Auto-J-13B</td>
    <td>0.95</td>
    <td>0.95</td>
    <td>0.81</td>
    <td>0.77</td>
    <td>0.77</td>
    <td>0.54</td>
  </tr>
  <tr>
    <td>Themis</td>
    <td>0.90</td>
    <td>0.90</td>
    <td>0.73</td>
    <td>0.85</td>
    <td>0.85</td>
    <td>0.67</td>
  </tr>
  <tr>
    <td>CJ-1-7B</td>
    <td>0.94</td>
    <td>0.94</td>
    <td>0.80</td>
    <td>0.86</td>
    <td>0.87</td>
    <td>0.67</td>
  </tr>
  <tr>
    <td>CJ-1-14B</td>
    <td>0.94</td>
    <td>0.94</td>
    <td>0.79</td>
    <td>0.87</td>
    <td>0.87</td>
    <td>0.67</td>
  </tr>
  <tr>
    <td><b>Praetor-7B (Ours)</b></td>
    <td><b>0.97</b></td>
    <td><b>0.97</b></td>
    <td><b>0.85</b></td>
    <td><b>0.93</b></td>
    <td><b>0.93</b></td>
    <td><b>0.77</b></td>
  </tr>
</table>

Table 11: Results of system-level ranking.

<table>
  <tr>
    <th rowspan="2"></th>
    <th colspan="2">Pearson (r)</th>
    <th colspan="2">Spearman (\( \rho \))</th>
    <th colspan="2">Kendall (\( \tau \))</th>
  </tr>
  <tr>
    <th>Point-Zh</th>
    <th>Point-En</th>
    <th>Win-Length</th>
    <th>Lose-Length</th>
    <th>LRW (%)</th>
  </tr>
  <tr>
    <td>Pair-Zh</td>
    <td>0.06</td>
    <td>0.12</td>
    <td>544.89</td>
    <td>536.20</td>
    <td>0.53</td>
  </tr>
  <tr>
    <td>Pair-En</td>
    <td>0.15</td>
    <td>0.13</td>
    <td>993.08</td>
    <td>945.58</td>
    <td>0.52</td>
  </tr>
</table>

Table 12: Results of length bias of Praetor.

multiple score scales, the results are shown in Table 10.

We find that Auto-J-Bilingual-6B and CritiqueLLM-6B perform better than the other scoring scales for the 10-point scoring setup. However, the CritiqueLLM-6B shows an absolute score difference of up to 3.05 despite the upper limit of scores being set to 3. We checked the output of the CritiqueLLM-6B and find that it is unable to follow the evaluation instructions well enough to generate scores in the range 1-3, and thus shows similar results to Section 4.4, and the similar finding was also observed for Auto-J-Bilingual-6B.

In contrast, CJ-1-7B and Praetor-7B showed balanced performance across multiple score scales, reflecting better generalization.

A.7 System-Level Ranking

In addition to testing Praetor’s performance on a variety of benchmarks, we also tested Praetor’s usefulness for leaderboard ranking at the system level. We use the AlpacaEval leaderboard,\footnote{https://tatsu-lab.github.io/alpaca_eval/} which archives the complete output for each model submitted, and contains both versions 1.0 and 2.0.\footnote{At the time of writing this paper, AlpacaEval 1.0 contains 43 models and AlpacaEval 2.0 contains 67 models.}

We performed pointwise grading evaluation with Praetor to score all responses, and then computed average scores for each model to obtain the final leaderboard. The results are shown in Table 11, where Praetor achieves higher correlation scores compared to other evaluators, demonstrating the capability of Praetor for leaderboard ranking. We provided the complete evaluation ranking results in Table 15 and 16.

A.8 Analysis of Length Bias

It has been shown that LLM evaluators suffer from length bias, which equates length with quality and ignores other aspects of the response (Li et al., 2024a). To detect Praetor’s length bias, for pointwise grading, we calculated the correlation coefficient between score and response length. For pairwise comparison, we calculated the average length of Praetor’s preference responses and non-preference responses, denoted as Win-Length and Lose-Length.

In addition, we constructed new testsets for each pairwise comparison benchmark by selecting the same amount of data with long responses as gold labels and with short responses as gold labels. On these testsets, we calculated the probability that Praetor selects the long response as winner, denoted as LRW.

The results are shown in Table 12, where we find that in pointwise grading, scoring has a very low correlation coefficient with response length. In pairwise comparison, the average lengths of preference responses and non-preference responses are similar. And the LRW is around 50% in the newly constructed testsets. The above results suggest that Praetor’s length bias is not serious.
<table>
  <tr>
    <th># Case 1</th>
    <th></th>
  </tr>
  <tr>
    <td>Task type</td>
    <td>Writing</td>
  </tr>
  <tr>
    <td><b>Task-level evaluation criteria</b></td>
    <td>
      1. Factual Correctness: Whether the information provided in the response is accurate, based on reliable facts and data.<br>
      2. User Satisfaction: whether the response satisfies the purpose and needs of the user’s question, and whether it responds to the question comprehensively and appropriately.<br>
      3. Creativity: Whether the response is innovative or unique, providing novel insights or solutions.<br>
      4. Coherence: Whether the response is clearly structured and flows logically.
    </td>
  </tr>
  <tr>
    <td><b>Question 1</b></td>
    <td>Write a popular science article about the impact of artificial intelligence on future society.</td>
  </tr>
  <tr>
    <td><b>Question 2</b></td>
    <td>Write a poem that describes the beauty of fall.</td>
  </tr>
</table>

<table>
  <tr>
    <th># Case 2</th>
    <th></th>
  </tr>
  <tr>
    <td>Task type</td>
    <td>Open-ended Questions</td>
  </tr>
  <tr>
    <td><b>Task-level evaluation criteria</b></td>
    <td>
      1. Factual Correctness: Whether the information provided in the response is accurate, based on reliable facts and data.<br>
      2. User Satisfaction: whether the response satisfies the purpose and needs of the user’s question, and whether it responds to the question comprehensively and appropriately.<br>
      3. Fairness and Responsibility: Whether the advice or information provided in the response is feasible, carries a certain degree of responsibility, and considers potential risks and consequences.<br>
      4. Creativity: Whether the response is innovative or unique, providing novel insights or solutions.
    </td>
  </tr>
  <tr>
    <td><b>Question 1</b></td>
    <td>I want to travel to Shangri-La, what are some good route suggestions.</td>
  </tr>
  <tr>
    <td><b>Question 2</b></td>
    <td>Recommend me some Tang poems from the 300 Tang poems.</td>
  </tr>
</table>

Table 13: Two sample data for task-level evaluation criteria.

<table>
  <tr>
    <th>Base Model</th>
    <td>Qwen2.5-7B-Instruct</td>
  </tr>
  <tr>
    <th>Torch dtype</th>
    <td>bfloat16</td>
  </tr>
  <tr>
    <th>Epoch</th>
    <td>2</td>
  </tr>
  <tr>
    <th>Training data Numbers</th>
    <td>926,122</td>
  </tr>
  <tr>
    <th>Max Seq Length</th>
    <td>32,768</td>
  </tr>
  <tr>
    <th>Learning Rate</th>
    <td>2e-5</td>
  </tr>
  <tr>
    <th>Training Method</th>
    <td>Supervised Fine-tuning</td>
  </tr>
  <tr>
    <th>Device</th>
    <td>8 × NVIDIA A100 (80GB) GPUs</td>
  </tr>
</table>

Table 14: Hyperparameters used to train Praetor.
<table>
  <tr>
    <th rowspan="2">Models</th>
    <th colspan="3">GPT-4</th>
    <th colspan="3">Prometheus-7B-v2.0</th>
    <th colspan="3">Prometheus-13B-v1.0</th>
    <th colspan="3">Auto-J-Bilingual-6B</th>
    <th colspan="3">Themis</th>
    <th colspan="3">CJ-1-7B</th>
    <th colspan="3">Praetor-7B</th>
  </tr>
  <tr>
    <th>Rank</th><th>Rank</th><th>\( \Delta \)</th>
    <th>Rank</th><th>Rank</th><th>\( \Delta \)</th>
    <th>Rank</th><th>Rank</th><th>\( \Delta \)</th>
    <th>Rank</th><th>Rank</th><th>\( \Delta \)</th>
    <th>Rank</th><th>Rank</th><th>\( \Delta \)</th>
    <th>Rank</th><th>Rank</th><th>\( \Delta \)</th>
    <th>Rank</th><th>Rank</th><th>\( \Delta \)</th>
  </tr>
  <tr>
    <td>GPT-4 Preview-1106</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>2</td><td>1</td><td>4</td><td>3</td><td>2</td><td>1</td><td>0</td>
  </tr>
  <tr>
    <td>Mistral Medium</td><td>2</td><td>5</td><td>3</td><td>5</td><td>3</td><td>1</td><td>-1</td><td>3</td><td>4</td><td>2</td><td>2</td><td>0</td>
  </tr>
  <tr>
    <td>GPT-4</td><td>3</td><td>4</td><td>1</td><td>8</td><td>5</td><td>10</td><td>7</td><td>7</td><td>4</td><td>8</td><td>5</td><td>2</td>
  </tr>
  <tr>
    <td>Mistral 8x7B v0.1</td><td>4</td><td>8</td><td>4</td><td>9</td><td>5</td><td>6</td><td>2</td><td>6</td><td>2</td><td>10</td><td>7</td><td>3</td>
  </tr>
  <tr>
    <td>GPT-4-0314</td><td>5</td><td>3</td><td>-2</td><td>7</td><td>2</td><td>8</td><td>3</td><td>8</td><td>3</td><td>6</td><td>1</td><td>-4</td>
  </tr>
  <tr>
    <td>Yi 34B Chat</td><td>6</td><td>6</td><td>0</td><td>3</td><td>-3</td><td>4</td><td>-2</td><td>5</td><td>-1</td><td>-1</td><td>-5</td><td>6</td>
  </tr>
  <tr>
    <td>GPT-4-0613</td><td>7</td><td>12</td><td>5</td><td>15</td><td>8</td><td>14</td><td>7</td><td>13</td><td>6</td><td>9</td><td>2</td><td>-4</td>
  </tr>
  <tr>
    <td>Mistral 7B v0.2</td><td>8</td><td>2</td><td>-6</td><td>2</td><td>-6</td><td>5</td><td>-3</td><td>11</td><td>3</td><td>7</td><td>-1</td><td>8</td><td>0</td>
  </tr>
  <tr>
    <td>LLaMA2 Chat 70B</td><td>9</td><td>10</td><td>1</td><td>4</td><td>-5</td><td>3</td><td>-6</td><td>1</td><td>-8</td><td>3</td><td>-6</td><td>9</td><td>0</td>
  </tr>
  <tr>
    <td>Claude</td><td>10</td><td>14</td><td>4</td><td>17</td><td>7</td><td>11</td><td>1</td><td>10</td><td>0</td><td>13</td><td>3</td><td>12</td><td>2</td>
  </tr>
  <tr>
    <td>Claude 2</td><td>11</td><td>11</td><td>0</td><td>16</td><td>5</td><td>9</td><td>-2</td><td>12</td><td>1</td><td>15</td><td>4</td><td>10</td><td>-1</td>
  </tr>
  <tr>
    <td>Cohere Command</td><td>12</td><td>19</td><td>7</td><td>12</td><td>0</td><td>7</td><td>-5</td><td>2</td><td>-10</td><td>12</td><td>0</td><td>14</td><td>2</td>
  </tr>
  <tr>
    <td>GPT 3.5 Turbo-0301</td><td>13</td><td>17</td><td>4</td><td>19</td><td>6</td><td>19</td><td>6</td><td>15</td><td>2</td><td>20</td><td>7</td><td>13</td><td>0</td>
  </tr>
  <tr>
    <td>Vicuna 33B v1.3</td><td>14</td><td>9</td><td>-5</td><td>6</td><td>-8</td><td>12</td><td>-2</td><td>17</td><td>3</td><td>11</td><td>-3</td><td>16</td><td>2</td>
  </tr>
  <tr>
    <td>Claude 2.1</td><td>15</td><td>15</td><td>0</td><td>18</td><td>3</td><td>15</td><td>0</td><td>26</td><td>11</td><td>14</td><td>-1</td><td>15</td><td>0</td>
  </tr>
  <tr>
    <td>GPT 3.5 Turbo-1106</td><td>16</td><td>13</td><td>-3</td><td>13</td><td>-3</td><td>16</td><td>0</td><td>16</td><td>0</td><td>19</td><td>3</td><td>11</td><td>-5</td>
  </tr>
  <tr>
    <td>Phi-2 SFT</td><td>17</td><td>16</td><td>-1</td><td>11</td><td>-6</td><td>18</td><td>1</td><td>14</td><td>-3</td><td>16</td><td>-1</td><td>26</td><td>9</td>
  </tr>
  <tr>
    <td>Vicuna 13B v1.3</td><td>18</td><td>21</td><td>3</td><td>21</td><td>3</td><td>20</td><td>2</td><td>20</td><td>2</td><td>17</td><td>1</td><td>19</td><td>1</td>
  </tr>
  <tr>
    <td>LLaMA2 Chat 13B</td><td>19</td><td>20</td><td>1</td><td>10</td><td>-9</td><td>13</td><td>-6</td><td>18</td><td>-1</td><td>18</td><td>-1</td><td>18</td><td>-1</td>
  </tr>
  <tr>
    <td>Gemini Pro</td><td>20</td><td>7</td><td>-13</td><td>14</td><td>-6</td><td>17</td><td>-3</td><td>9</td><td>-11</td><td>5</td><td>-15</td><td>17</td><td>-3</td>
  </tr>
  <tr>
    <td>Vicuna 7B v1.3</td><td>21</td><td>24</td><td>3</td><td>22</td><td>1</td><td>22</td><td>1</td><td>22</td><td>1</td><td>26</td><td>5</td><td>25</td><td>4</td>
  </tr>
  <tr>
    <td>WizardLM 13B</td><td>22</td><td>22</td><td>0</td><td>25</td><td>3</td><td>25</td><td>3</td><td>21</td><td>-1</td><td>22</td><td>0</td><td>22</td><td>0</td>
  </tr>
  <tr>
    <td>Guanaco 65B</td><td>23</td><td>30</td><td>7</td><td>26</td><td>3</td><td>24</td><td>1</td><td>27</td><td>4</td><td>24</td><td>1</td><td>21</td><td>-2</td>
  </tr>
  <tr>
    <td>LLaMA2 Chat 7B</td><td>24</td><td>26</td><td>2</td><td>20</td><td>-4</td><td>23</td><td>-1</td><td>36</td><td>12</td><td>21</td><td>-3</td><td>24</td><td>0</td>
  </tr>
  <tr>
    <td>Vicuna 13B</td><td>25</td><td>23</td><td>-2</td><td>23</td><td>-2</td><td>21</td><td>-4</td><td>25</td><td>0</td><td>25</td><td>0</td><td>20</td><td>-5</td>
  </tr>
  <tr>
    <td>Phi-2 SFT</td><td>26</td><td>28</td><td>2</td><td>28</td><td>2</td><td>29</td><td>3</td><td>29</td><td>3</td><td>31</td><td>5</td><td>31</td><td>5</td>
  </tr>
  <tr>
    <td>LLaMA 33B OASST RLHF</td><td>27</td><td>29</td><td>2</td><td>27</td><td>0</td><td>30</td><td>3</td><td>24</td><td>-3</td><td>29</td><td>2</td><td>27</td><td>0</td>
  </tr>
  <tr>
    <td>Guanaco 33B</td><td>28</td><td>32</td><td>4</td><td>31</td><td>3</td><td>28</td><td>0</td><td>32</td><td>4</td><td>28</td><td>0</td><td>28</td><td>0</td>
  </tr>
  <tr>
    <td>Nous Hermes 13B</td><td>29</td><td>18</td><td>-11</td><td>24</td><td>-5</td><td>27</td><td>-2</td><td>19</td><td>-10</td><td>23</td><td>-6</td><td>23</td><td>-6</td>
  </tr>
  <tr>
    <td>Vicuna 7B</td><td>30</td><td>27</td><td>-3</td><td>29</td><td>-1</td><td>26</td><td>-4</td><td>31</td><td>1</td><td>27</td><td>-3</td><td>29</td><td>-1</td>
  </tr>
  <tr>
    <td>LLaMA 33B OASST SFT</td><td>31</td><td>34</td><td>3</td><td>32</td><td>1</td><td>32</td><td>1</td><td>30</td><td>1</td><td>32</td><td>1</td><td>30</td><td>-1</td>
  </tr>
  <tr>
    <td>Guanaco 13B</td><td>32</td><td>37</td><td>5</td><td>36</td><td>4</td><td>35</td><td>3</td><td>34</td><td>2</td><td>30</td><td>-2</td><td>33</td><td>1</td>
  </tr>
  <tr>
    <td>Davinci003</td><td>33</td><td>42</td><td>9</td><td>42</td><td>9</td><td>42</td><td>9</td><td>41</td><td>8</td><td>42</td><td>9</td><td>42</td><td>9</td>
  </tr>
  <tr>
    <td>Guanaco 7B</td><td>34</td><td>40</td><td>6</td><td>39</td><td>5</td><td>34</td><td>0</td><td>37</td><td>3</td><td>33</td><td>-1</td><td>35</td><td>1</td>
  </tr>
  <tr>
    <td>Falcon 40B Instruct</td><td>35</td><td>33</td><td>-2</td><td>33</td><td>-2</td><td>33</td><td>-2</td><td>33</td><td>-2</td><td>35</td><td>0</td><td>34</td><td>-1</td>
  </tr>
  <tr>
    <td>Falcon 7B Instruct</td><td>36</td><td>38</td><td>2</td><td>38</td><td>2</td><td>40</td><td>4</td><td>42</td><td>6</td><td>40</td><td>4</td><td>40</td><td>4</td>
  </tr>
  <tr>
    <td>Alpaca Farm PPO Sim (GPT-4) 7B</td><td>37</td><td>25</td><td>-12</td><td>30</td><td>-7</td><td>31</td><td>-6</td><td>23</td><td>-14</td><td>37</td><td>0</td><td>36</td><td>-1</td>
  </tr>
  <tr>
    <td>Pythia 12B SFT</td><td>38</td><td>36</td><td>-2</td><td>35</td><td>-3</td><td>37</td><td>-1</td><td>39</td><td>1</td><td>36</td><td>-2</td><td>37</td><td>-1</td>
  </tr>
  <tr>
    <td>Alpaca Farm PPO Human 7B</td><td>39</td><td>31</td><td>-8</td><td>34</td><td>-5</td><td>36</td><td>-3</td><td>28</td><td>-11</td><td>34</td><td>-5</td><td>32</td><td>-7</td>
  </tr>
  <tr>
    <td>Alpaca 7B</td><td>40</td><td>35</td><td>-5</td><td>37</td><td>-3</td><td>38</td><td>-2</td><td>35</td><td>-5</td><td>38</td><td>-2</td><td>38</td><td>-2</td>
  </tr>
  <tr>
    <td>Pythia 12B OASST SFT</td><td>41</td><td>39</td><td>-2</td><td>40</td><td>-1</td><td>39</td><td>-2</td><td>38</td><td>-3</td><td>39</td><td>-2</td><td>39</td><td>-2</td>
  </tr>
  <tr>
    <td>Davinci001</td><td>42</td><td>41</td><td>-1</td><td>41</td><td>-1</td><td>41</td><td>-1</td><td>40</td><td>-2</td><td>41</td><td>-1</td><td>41</td><td>-1</td>
  </tr>
</table>

Table 15: The ranking results for multiple LLM evaluators in AlpacaEval 1.0, \( \Delta = \text{Rank}_{\text{Model}} - \text{Rank}_{\text{GPT-4}} \).
<table>
  <tr>
    <th rowspan="2">Models</th>
    <th colspan="3">GPT-4</th>
    <th colspan="3">Prometheus-7B-v2.0</th>
    <th colspan="3">Prometheus-13B-v1.0</th>
    <th colspan="3">Auto-J-Bilingual-6B</th>
    <th colspan="3">Themis</th>
    <th colspan="3">CJ-1-7B</th>
    <th colspan="3">Praetor-7B</th>
  </tr>
  <tr>
    <th>Rank</th><th>Rank</th><th>Δ</th>
    <th>Rank</th><th>Rank</th><th>Δ</th>
    <th>Rank</th><th>Rank</th><th>Δ</th>
    <th>Rank</th><th>Rank</th><th>Δ</th>
    <th>Rank</th><th>Rank</th><th>Δ</th>
    <th>Rank</th><th>Rank</th><th>Δ</th>
    <th>Rank</th><th>Rank</th><th>Δ</th>
  </tr>
  <tr><td>GPT-4 Omni-0513</td><td>1</td><td>4</td><td>3</td><td>3</td><td>2</td><td>10</td><td>9</td><td>3</td><td>2</td><td>17</td><td>16</td><td>5</td><td>4</td></tr>
  <tr><td>GPT-4 Turbo-0409</td><td>2</td><td>11</td><td>9</td><td>8</td><td>6</td><td>8</td><td>6</td><td>9</td><td>7</td><td>13</td><td>11</td><td>3</td><td>1</td></tr>
  <tr><td>Yi-Large Preview</td><td>3</td><td>5</td><td>2</td><td>2</td><td>-1</td><td>1</td><td>-2</td><td>11</td><td>8</td><td>4</td><td>1</td><td>2</td><td>-1</td></tr>
  <tr><td>GPT-4o Mini-0718</td><td>4</td><td>6</td><td>2</td><td>6</td><td>2</td><td>5</td><td>1</td><td>4</td><td>0</td><td>6</td><td>2</td><td>4</td><td>0</td></tr>
  <tr><td>GPT-4 Preview-1106</td><td>5</td><td>19</td><td>14</td><td>11</td><td>6</td><td>12</td><td>7</td><td>13</td><td>8</td><td>5</td><td>0</td><td>6</td><td>1</td></tr>
  <tr><td>Llama 3 Opus-0229</td><td>6</td><td>22</td><td>16</td><td>14</td><td>8</td><td>14</td><td>8</td><td>26</td><td>20</td><td>22</td><td>16</td><td>10</td><td>4</td></tr>
  <tr><td>Llama 3.1 405B Instruct</td><td>7</td><td>7</td><td>0</td><td>9</td><td>2</td><td>2</td><td>-5</td><td>7</td><td>8</td><td>1</td><td>13</td><td>6</td></tr>
  <tr><td>GPT-4</td><td>8</td><td>18</td><td>10</td><td>25</td><td>17</td><td>31</td><td>23</td><td>20</td><td>12</td><td>25</td><td>17</td><td>24</td><td>16</td></tr>
  <tr><td>Qwen 72B Instruct</td><td>9</td><td>27</td><td>18</td><td>22</td><td>13</td><td>16</td><td>7</td><td>19</td><td>10</td><td>2</td><td>-7</td><td>9</td><td>0</td></tr>
  <tr><td>Llama 3.1 70B Instruct</td><td>10</td><td>9</td><td>-1</td><td>7</td><td>-3</td><td>9</td><td>-1</td><td>8</td><td>-2</td><td>3</td><td>-7</td><td>15</td><td>5</td></tr>
  <tr><td>Qwen1.5 72B Chat</td><td>11</td><td>10</td><td>-1</td><td>13</td><td>2</td><td>15</td><td>4</td><td>18</td><td>7</td><td>9</td><td>-2</td><td>12</td><td>1</td></tr>
  <tr><td>GPT-4-0314</td><td>12</td><td>23</td><td>11</td><td>24</td><td>12</td><td>25</td><td>13</td><td>27</td><td>15</td><td>23</td><td>11</td><td>16</td><td>4</td></tr>
  <tr><td>Claude 3 Sonnet-0229</td><td>13</td><td>12</td><td>-1</td><td>16</td><td>3</td><td>3</td><td>-10</td><td>16</td><td>3</td><td>26</td><td>13</td><td>17</td><td>4</td></tr>
  <tr><td>Llama 3 70B Instruct</td><td>14</td><td>2</td><td>-12</td><td>4</td><td>-10</td><td>4</td><td>-10</td><td>1</td><td>-13</td><td>11</td><td>-3</td><td>8</td><td>-6</td></tr>
  <tr><td>Mistral Large-2402</td><td>15</td><td>20</td><td>5</td><td>26</td><td>11</td><td>27</td><td>12</td><td>24</td><td>9</td><td>27</td><td>12</td><td>7</td><td>-8</td></tr>
  <tr><td>Mistral 8x2B v0.1</td><td>16</td><td>27</td><td>11</td><td>27</td><td>11</td><td>20</td><td>4</td><td>22</td><td>6</td><td>20</td><td>4</td><td>14</td><td>-2</td></tr>
  <tr><td>GPT-4-0613</td><td>17</td><td>36</td><td>19</td><td>35</td><td>18</td><td>35</td><td>18</td><td>34</td><td>17</td><td>29</td><td>12</td><td>28</td><td>11</td></tr>
  <tr><td>Cohere AI (KTO-Mistral-PairRM)</td><td>18</td><td>1</td><td>-17</td><td>1</td><td>-17</td><td>7</td><td>-11</td><td>14</td><td>1</td><td>-11</td><td>7</td><td>11</td><td>7</td></tr>
  <tr><td>Mistral Medium</td><td>19</td><td>26</td><td>7</td><td>21</td><td>2</td><td>13</td><td>-6</td><td>12</td><td>-7</td><td>21</td><td>1</td><td>20</td><td>1</td></tr>
  <tr><td>Claude 2</td><td>20</td><td>35</td><td>15</td><td>37</td><td>17</td><td>28</td><td>8</td><td>33</td><td>13</td><td>35</td><td>15</td><td>31</td><td>11</td></tr>
  <tr><td>Claude</td><td>21</td><td>33</td><td>12</td><td>38</td><td>17</td><td>33</td><td>12</td><td>29</td><td>8</td><td>34</td><td>13</td><td>34</td><td>13</td></tr>
  <tr><td>Yi 34B Chat</td><td>22</td><td>28</td><td>6</td><td>20</td><td>-2</td><td>17</td><td>-5</td><td>15</td><td>-7</td><td>1</td><td>-21</td><td>25</td><td>3</td></tr>
  <tr><td>DBRX Instruct</td><td>23</td><td>24</td><td>1</td><td>31</td><td>8</td><td>29</td><td>6</td><td>17</td><td>-6</td><td>10</td><td>-13</td><td>19</td><td>-4</td></tr>
  <tr><td>Claude 2.1</td><td>24</td><td>37</td><td>13</td><td>41</td><td>17</td><td>36</td><td>12</td><td>48</td><td>24</td><td>36</td><td>12</td><td>36</td><td>12</td></tr>
  <tr><td>Gemini Pro</td><td>25</td><td>0</td><td>-19</td><td>39</td><td>14</td><td>39</td><td>14</td><td>31</td><td>6</td><td>28</td><td>3</td><td>38</td><td>13</td></tr>
  <tr><td>Qwen1.5 14B Chat</td><td>26</td><td>8</td><td>-18</td><td>15</td><td>-11</td><td>21</td><td>-5</td><td>25</td><td>-1</td><td>12</td><td>-14</td><td>18</td><td>-8</td></tr>
  <tr><td>Mistral 8x/B v0.1</td><td>27</td><td>30</td><td>3</td><td>29</td><td>2</td><td>24</td><td>-3</td><td>23</td><td>-4</td><td>19</td><td>-8</td><td>26</td><td>-1</td></tr>
  <tr><td>Llama 3 8B Instruct</td><td>28</td><td>17</td><td>-11</td><td>10</td><td>-18</td><td>11</td><td>-17</td><td>10</td><td>-18</td><td>16</td><td>-12</td><td>23</td><td>-5</td></tr>
  <tr><td>Tulu 2+DPO 70B</td><td>29</td><td>29</td><td>0</td><td>28</td><td>-1</td><td>19</td><td>-10</td><td>21</td><td>-8</td><td>31</td><td>2</td><td>23</td><td>-7</td></tr>
  <tr><td>Llama 3 8B Instruct</td><td>30</td><td>44</td><td>14</td><td>40</td><td>10</td><td>43</td><td>13</td><td>49</td><td>49</td><td>40</td><td>12</td><td>42</td><td>12</td></tr>
  <tr><td>Mistral 7B v0.3</td><td>31</td><td>15</td><td>-16</td><td>19</td><td>-12</td><td>30</td><td>-1</td><td>28</td><td>-3</td><td>18</td><td>-13</td><td>21</td><td>-10</td></tr>
  <tr><td>GPT 3.5 Turbo-1106</td><td>32</td><td>34</td><td>2</td><td>33</td><td>1</td><td>38</td><td>6</td><td>37</td><td>5</td><td>42</td><td>10</td><td>32</td><td>0</td></tr>
  <tr><td>GPT 3.5 Turbo-0301</td><td>33</td><td>42</td><td>9</td><td>42</td><td>9</td><td>42</td><td>9</td><td>36</td><td>3</td><td>43</td><td>10</td><td>33</td><td>0</td></tr>
  <tr><td>Vicuna 3B v1.3</td><td>34</td><td>31</td><td>-3</td><td>23</td><td>-11</td><td>32</td><td>-2</td><td>38</td><td>4</td><td>32</td><td>-2</td><td>37</td><td>3</td></tr>
  <tr><td>Mistral 7B v0.2</td><td>35</td><td>16</td><td>-19</td><td>17</td><td>-18</td><td>23</td><td>-12</td><td>32</td><td>-3</td><td>24</td><td>-11</td><td>29</td><td>-6</td></tr>
  <tr><td>OpenHermes-2.5-Mistral (7B)</td><td>36</td><td>14</td><td>-22</td><td>34</td><td>-2</td><td>37</td><td>1</td><td>30</td><td>-6</td><td>37</td><td>1</td><td>39</td><td>3</td></tr>
  <tr><td>Qwen1.5 7B Chat</td><td>37</td><td>13</td><td>-24</td><td>12</td><td>-25</td><td>22</td><td>-15</td><td>40</td><td>3</td><td>30</td><td>-7</td><td>27</td><td>-10</td></tr>
  <tr><td>LLaMA2 Chat 70B</td><td>38</td><td>32</td><td>-6</td><td>18</td><td>-20</td><td>18</td><td>-20</td><td>2</td><td>-36</td><td>15</td><td>-23</td><td>30</td><td>-8</td></tr>
  <tr><td>Cohere Command</td><td>39</td><td>39</td><td>0</td><td>32</td><td>-7</td><td>26</td><td>-13</td><td>6</td><td>-33</td><td>3</td><td>-6</td><td>3</td><td>-4</td></tr>
  <tr><td>Vicuna 1B v1.3</td><td>40</td><td>43</td><td>3</td><td>45</td><td>5</td><td>40</td><td>0</td><td>43</td><td>4</td><td>44</td><td>4</td><td>41</td><td>1</td></tr>
  <tr><td>Gemini Instruction (7B)</td><td>41</td><td>53</td><td>12</td><td>53</td><td>12</td><td>55</td><td>14</td><td>56</td><td>15</td><td>51</td><td>10</td><td>53</td><td>12</td></tr>
  <tr><td>LLaMA 33B OASST SFT</td><td>42</td><td>57</td><td>15</td><td>57</td><td>15</td><td>57</td><td>15</td><td>57</td><td>15</td><td>57</td><td>15</td><td>54</td><td>12</td></tr>
  <tr><td>WizardLM 13B</td><td>43</td><td>46</td><td>3</td><td>48</td><td>5</td><td>47</td><td>4</td><td>42</td><td>-1</td><td>45</td><td>2</td><td>45</td><td>2</td></tr>
  <tr><td>Nous Hermes 13B</td><td>44</td><td>41</td><td>-3</td><td>44</td><td>0</td><td>50</td><td>6</td><td>41</td><td>-3</td><td>47</td><td>3</td><td>46</td><td>2</td></tr>
  <tr><td>Vicuna 13B</td><td>45</td><td>45</td><td>0</td><td>47</td><td>2</td><td>46</td><td>1</td><td>45</td><td>0</td><td>46</td><td>1</td><td>44</td><td>-1</td></tr>
  <tr><td>Davinci001</td><td>46</td><td>67</td><td>21</td><td>67</td><td>21</td><td>67</td><td>21</td><td>66</td><td>20</td><td>67</td><td>21</td><td>67</td><td>21</td></tr>
  <tr><td>LLaMA2 Chat 13B</td><td>47</td><td>40</td><td>-7</td><td>30</td><td>-17</td><td>34</td><td>-13</td><td>39</td><td>-8</td><td>38</td><td>-9</td><td>40</td><td>-7</td></tr>
  <tr><td>Guanaco 65B</td><td>48</td><td>55</td><td>7</td><td>49</td><td>1</td><td>48</td><td>0</td><td>50</td><td>2</td><td>49</td><td>1</td><td>43</td><td>-5</td></tr>
  <tr><td>LLaMA 33B OASST RLHF</td><td>49</td><td>54</td><td>5</td><td>55</td><td>6</td><td>53</td><td>4</td><td>47</td><td>-2</td><td>53</td><td>4</td><td>50</td><td>1</td></tr>
  <tr><td>Phi-2 DPO</td><td>50</td><td>38</td><td>-12</td><td>36</td><td>-14</td><td>41</td><td>-9</td><td>35</td><td>-15</td><td>41</td><td>-9</td><td>49</td><td>-1</td></tr>
  <tr><td>Vicuna /B v1.3</td><td>51</td><td>47</td><td>-4</td><td>46</td><td>-5</td><td>44</td><td>-7</td><td>46</td><td>-5</td><td>50</td><td>-1</td><td>48</td><td>-3</td></tr>
  <tr><td>Alpaca Farm PPO Sim (GPT-4) 7B</td><td>52</td><td>48</td><td>-4</td><td>54</td><td>2</td><td>54</td><td>2</td><td>44</td><td>-8</td><td>62</td><td>10</td><td>60</td><td>8</td></tr>
  <tr><td>Alpaca Farm PPO Human 7B</td><td>53</td><td>56</td><td>3</td><td>61</td><td>8</td><td>61</td><td>8</td><td>51</td><td>5</td><td>59</td><td>6</td><td>58</td><td>5</td></tr>
  <tr><td>Vicuna 7B</td><td>54</td><td>51</td><td>-3</td><td>51</td><td>-3</td><td>49</td><td>-5</td><td>55</td><td>1</td><td>54</td><td>0</td><td>52</td><td>-2</td></tr>
  <tr><td>Alpaca 7B</td><td>55</td><td>60</td><td>5</td><td>65</td><td>10</td><td>64</td><td>9</td><td>61</td><td>6</td><td>65</td><td>10</td><td>64</td><td>9</td></tr>
  <tr><td>Phi-2 SFT</td><td>56</td><td>52</td><td>-4</td><td>52</td><td>-4</td><td>52</td><td>-4</td><td>52</td><td>-4</td><td>56</td><td>0</td><td>55</td><td>-1</td></tr>
  <tr><td>Guanaco 33B</td><td>57</td><td>59</td><td>2</td><td>56</td><td>-1</td><td>51</td><td>-6</td><td>53</td><td>-4</td><td>48</td><td>-9</td><td>51</td><td>-6</td></tr>
  <tr><td>Falcon 40B Instruct</td><td>58</td><td>58</td><td>0</td><td>58</td><td>0</td><td>58</td><td>0</td><td>58</td><td>0</td><td>61</td><td>3</td><td>56</td><td>-2</td></tr>
  <tr><td>Gemma Instruct (2B)</td><td>59</td><td>61</td><td>2</td><td>59</td><td>0</td><td>62</td><td>3</td><td>65</td><td>6</td><td>60</td><td>1</td><td>63</td><td>4</td></tr>
  <tr><td>LLaMA2 Chat 7B</td><td>60</td><td>50</td><td>-10</td><td>43</td><td>-17</td><td>45</td><td>-15</td><td>60</td><td>0</td><td>39</td><td>-21</td><td>47</td><td>-13</td></tr>
  <tr><td>Pythia 12B SFT</td><td>61</td><td>63</td><td>2</td><td>60</td><td>-1</td><td>63</td><td>2</td><td>64</td><td>3</td><td>63</td><td>2</td><td>62</td><td>1</td></tr>
  <tr><td>Falcon /B Instruct</td><td>62</td><td>62</td><td>0</td><td>64</td><td>2</td><td>66</td><td>4</td><td>67</td><td>5</td><td>66</td><td>4</td><td>66</td><td>4</td></tr>
  <tr><td>Pythia 12B OASST SFT</td><td>63</td><td>65</td><td>2</td><td>66</td><td>3</td><td>65</td><td>2</td><td>63</td><td>0</td><td>64</td><td>1</td><td>63</td><td>2</td></tr>
  <tr><td>Guanaco 13B</td><td>64</td><td>64</td><td>0</td><td>62</td><td>-2</td><td>59</td><td>-5</td><td>59</td><td>-5</td><td>52</td><td>-12</td><td>57</td><td>-7</td></tr>
  <tr><td>Guanaco 7B</td><td>65</td><td>66</td><td>1</td><td>62</td><td>2</td><td>60</td><td>-5</td><td>62</td><td>3</td><td>58</td><td>7</td><td>59</td><td>-6</td></tr>
  <tr><td>Qwen1.5 13B Chat</td><td>66</td><td>49</td><td>-17</td><td>50</td><td>-16</td><td>56</td><td>-10</td><td>54</td><td>-12</td><td>55</td><td>-11</td><td>61</td><td>-5</td></tr>
</table>

Table 16: The ranking results for multiple LLM evaluators in AlpacaEval 2.0, \( \Delta = \text{Rank}_{\text{Model}} - \text{Rank}_{\text{GPT-4}} \).
Prompt template

### Task description
You are a quality scorer and I am going to provide you with a user question and you need to score the quality of the question. I will provide you with some requirements and ask you to score question strictly based on those requirements.

### Requirements
1. Please score low if the question is not a complete sentence, or lacks contextual background, or doesn’t make much sense, e.g., a simple greeting, an inability to clearly identify what the question is trying to say, a poorly described question, ambiguity, etc.
2. If the question has enough information to clearly convey meaning, needs, etc., score can be raised appropriately.
3. Higher scores indicate better quality. 1 is the lowest score and 3 is the highest. Do not generate scores other than 1-3.

### User question
{question}

### Your Score

Table 17: The prompt template for question quality scoring.

Prompt template

### Task description
You are a data annotator and I am going to provide you with a user question and you need to identify the task type of the question. I will provide you with some predefined task types (including primary and secondary categorization) as a reference, if you think the question belongs to one of the predefined task types provided, please output that task type directly. Otherwise, please output the task type you think is most relevant.
Please note that you only need to output the primary task type first, followed by the secondary task type, and do not generate anything else.

### Predefined task types
1. NLP_Task: Such as Text Classification, Sentiment Analysis.
2. Text_Edit: Such as Text Rewriting, Text Simplification.
3. Writing: Such as Poetry Creation, Novel Creation.
4. Coding: Such as Code Interpretation, Complexity Analysis.
5. Reasoning: Such as Mathematical Reasoning, Common Sense Reasoning.
6. Math: Such as Arithmetic Calculations, Number Theory.
7. Knowledge: Such as Humanities, Social Sciences.
8. Safety: Such as Bias, Ethics.
9. Communication: Such as Advice Seeking, Content Recommendations.

### User question
{question}

### Your Output

Table 18: The prompt template for labeling the task type of questions.
Prompt template

### Task description
You are a quality scorer, and I am going to provide you with a response to a user question from a certain large language model, and you need to score the quality of the response. I will provide you with some requirements and ask you to score the response strictly on the basis of these requirements.

### Requirements
1. If the response is not a complete sentence or does not convey meaning clearly (e.g., interruptions, too many special symbols), award a lower score.
2. If the response appears to be in line with normal communication, award appropriate scores.
3. Higher scores indicate better quality. 1 is the lowest score and 3 is the highest. Do not generate scores other than 1-3.

### Response
{response}

### Your Score

Table 19: The prompt template for response quality scoring.

Prompt template

### Task description
You are a text quality evaluator and I am going to give you a question (instruction) from a human user. In the future there will be some people or models to answer this question and we need to evaluate the quality of these answers, but we don’t know which dimensions we should evaluate. We would like you to help us develop some dimensions for evaluation. The details and requirements are listed below.

### Three levels of importance
[Description of Level 1 evaluation dimensions]
For a user question, we believe that the most important evaluation dimension for evaluating a response should be the satisfaction of the user’s requirements, i.e., the content of the response must appropriately respond to the user’s question, rather than responding to content that is unrelated to the question itself.

[Description of Level 2 evaluation dimensions]
For a user question, if an answer has appropriately satisfied the needs of the question, based on this, we consider the factual correctness, accuracy, reasonableness, and other dimensions of the answer to be the second most important.

[Description of Level 3 evaluation dimensions]
For a user question, if an answer not only satisfies the question appropriately, but also has factual correctness, accuracy, reasonableness, etc., then finally, we can try to consider whether the style, format, and language of the answer are more in line with human preferences. We would like these dimensions to serve as Level 3 evaluation dimensions. However, it is important to note that Level 3 dimensions are not mandatory.

### Requirements
1. We would like you to give us a list of evaluation dimensions that we can use, based on the three levels of importance of evaluation dimensions mentioned above, and explain what you mean by the dimensions you have listed. In addition to this, the level of each dimension (level 1 to level 3) needs to be clearly indicated.
2. For level 1, there should only be the dimension of satisfying the user’s needs. For Level 2, we would like you to list roughly 1 or 2 evaluation dimensions, depending on the attributes of the problem itself. For level 3, we would like you to be flexible and list no dimensions at this level or only 1 evaluation dimension, depending on the problem. Therefore, the total number of dimensions to be assessed is roughly 2-4, too many and too few are not good.
3. If the user’s question has unsafe tendencies, such as illegal, unethical, invasive of privacy, violent tendencies, and other content, then you don’t need to generate evaluation dimensions based on the three levels of importance stipulated above regarding evaluation dimensions.
4. Make sure that the output as a whole is a list, and that each element of the list is a dictionary that corresponds to an evaluation dimension. The dictionary contains 3 keywords: evaluation dimension, evaluation dimension explanation, and importance level. Do not generate any additional content and follow the format strictly.

### User question
{question}

Table 20: The prompt template for generating evaluation criteria.
Prompt template

### Task description
You are a data annotator and I will start by giving you a user question. In the future, there will be some large language models to answer this question. In order for us to evaluate the responses of these large language models in the future, we would like to know what evaluation dimensions should be used to evaluate the quality of the responses for this question and the corresponding answers.
To this end, we have envisioned some evaluation dimensions, and you need to help us evaluate whether these dimensions we have envisioned can be used to evaluate the responses to the above question. In other words, please score these evaluation dimensions directly for their applicability and match to the question.

### Requirements
1. If these evaluation criteria are not relevant to the question, score low.
2. If these evaluation criteria can be used appropriately and accurately to evaluate the response to this question, score high.
3. Higher scores indicate better quality. 1 is the lowest score and 3 is the highest. Do not generate scores other than 1-3.

### User question
{question}

### Envisioned evaluation criteria
{criteria}

### Your Score

Table 21: The prompt template for testing the relevance of evaluation criteria to questions.

Prompt template

### Task description
You are a quality evaluator and I will provide you with a user question, a response, a set of evaluation criteria and a reference response. In addition, there is an evaluator that critique and score the responses generated by the above. I’m asking you to evaluate the quality of the content generated by the evaluator; specifically, you need to determine the consistency of the critique and score generated by the evaluator. For example, if the critique suggests that the response performed very poorly, but the final score is high, this is inconsistent.

### Requirements
1. Higher scores indicate better quality. 1 is the lowest score and 3 is the highest. Do not generate scores other than 1-3.

### User question
{question}

### Response
{response}

### Evaluation Criteria
{criteria}

### Reference Response
{reference_response}

### Critique
{critique}

### Your Score

Table 22: The prompt template for testing the consistency of critique and result.
Prompt template

### Task description
Given a user instruction, a model-generated response to that instruction, and some criteria for evaluating the response, evaluate the response based on the evaluation criteria and score the response. In addition to this, a full score reference response is provided for your reference.

1. Write a detailed critique on the response based on the evaluation criteria and referenced response that will be used to evaluate the quality of the response.
2. Critique the response point by point, following each point of the evaluation criteria, without missing any evaluation criteria points. After criticizing all evaluation criteria points, give a final overall critique.
3. After writing your critique, give an integer score between 1 and 5 to indicate your rating score for the response, with a higher score indicating a better quality response.
4. The output format is shown below:
   {Your critique of the response}
   Overall score: {Your score for the response}.
5. Generated strictly according to the output format and do not generate any other openings, closings or explanations.

### User instruction
{instruction}

### Response to be evaluated
{response}

### Full score reference response
{reference_response}

### Evaluation criteria
{criteria}

Table 23: The pointwise grading prompt template for Praetor and instruction-tuned models.
Prompt template

### Task description
Given a user instruction, two model-generated responses to that instruction, and some criteria for critiquing the responses, evaluate the two responses based on the evaluation criteria and judge which response is better. In addition to this, a full score reference response is provided for your reference.

1. Write a detailed comparative critique on the two response based on the evaluation criteria and referenced response that will be used to evaluate the quality of the two responses.
2. Critique the two responses point by point, following each point of the evaluation criteria, without missing any evaluation criteria points. After criticizing all evaluation criteria points, give a final overall critique.
3. After writing your critique, give your final judgment, indicating which response you think is better.
4. The output format is shown below:
{Your comparative critique of the two responses}
Final judgment: {Response A or Response B}.
5. Avoid any positional bias. Make sure the order of the two responses does not influence your judgment. Don’t let the length of a response influence your judgment. Don’t let liking the name of a particular response influence your judgment. Be as objective as possible.
6. Generated strictly according to the output format and do not generate any other openings, closings or explanations.

### User instruction
{instruction}

### Response A to be evaluated
{response_a}

### Response B to be evaluated
{response_b}

### Full score reference response
{reference_response}

### Evaluation criteria
{criteria}

Table 24: The pairwise comparison prompt template for Praetor and instruction-tuned models.
Prompt template

### Task description
You are an impartial evaluator and I will provide you with a user question, a response, a set of evaluation criteria and a reference response. In addition, there are two evaluator generated critiques of the above response. I ask you to evaluate the critiques generated by the two evaluators comparatively in terms of correctness, usefulness and informativeness. Note that it is not the responses that are evaluated, but the performance of the evaluators. First give your analysis, i.e. the strengths and weaknesses of the two critiques. Finally, output your preference (A, B, Tie) indicates which evaluator generated a better or equal critique.

### User question
{question}

### Response
{response}

### Evaluation Criteria
{criteria}

### Reference Response
{reference_response}

### Critique from Evaluator A
{critique_a}

### Critique from Evaluator B
{critique_b}

### Your Output

Table 25: The prompt template for evaluating critiques.
<table>
  <tr>
    <th>Task type</th>
    <th>Coding</th>
  </tr>
  <tr>
    <th>Question</th>
    <td>I want a Java input stream program example, but I need to limit the number of bytes read to save resources during some network transmissions. Can you help me write one?</td>
  </tr>
  <tr>
    <th>Evaluation Criteria</th>
    <td>
      1. Meeting User Needs<br>
      Evaluate whether the response provides a Java input stream example that limits the number of bytes read, meeting the user’s need to conserve resources during network transmission.<br>
      Importance level: 1<br>
      2. Code Correctness<br>
      Evaluate whether the provided Java code runs correctly, implements the byte-length restriction function, and is free of syntax or logical errors.<br>
      Importance level: 2<br>
      3. Code Readability<br>
      Evaluate whether the code has good readability, including appropriate comments, standardized variable naming, and clear code structure, making it easy for users to understand and modify.<br>
      Importance level: 3
    </td>
  </tr>
  <tr>
    <th>Response (GLM-4-Air)</th>
    <td>
      Of course. Below is a Java program example that uses InputStream to read data while limiting the number of bytes read. In this example, I use FileInputStream to read a file but only read the first 100 bytes. import java.io.FileInputStream; import java.io.IOException;
      public class LimitedInputStreamExample { public static void main(String[] args) { // Replace with your file path int maxBytesToRead = 100; // Maximum number of bytes to read FileInputStream fileInputStream = null; try { fileInputStream = new FileInputStream("filePath"); byte[] buffer = new byte[maxBytesToRead]; int bytesRead = fileInputStream.read(buffer); System.out.println("Read " + bytesRead + " bytes."); System.out.println("Data read: " + new String(buffer, 0, bytesRead)); } catch (IOException e) { e.printStackTrace(); } finally { if (fileInputStream != null) { try { fileInputStream.close(); } catch (IOException e) { e.printStackTrace(); } } } }
      In this program, we first create a FileInputStream object to open the file. Then, we define a byte array buffer with a size equal to the maximum number of bytes we want to read. We use the read method to fill this buffer, which returns the actual number of bytes read. Finally, we convert the read byte array into a string and print it. Note that after completing the file operation, we close the InputStream in the finally block to release resources.
    </td>
  </tr>
  <tr>
    <th>Reference Response</th>
    <td>
      Of course! In Java, you can read input streams using the InputStream class and its subclasses, and you can implement custom logic to limit the number of bytes read. Here is a simple example program that reads data from standard input (such as the keyboard) and limits the maximum number of bytes read to 1024 bytes.
      import java.io.InputStream; import java.io.IOException;
      public class LimitedInputStreamExample { public static void main(String[] args) { // Specify the maximum number of bytes to read int maxBytesToRead = 1024; // Create an InputStream object using System.in for standard input InputStream in = System.in; // Use try-with-resources to ensure the InputStream is properly closed try { LimitedInputStream limitedInputStream = new LimitedInputStream(in, maxBytesToRead); byte[] buffer = new byte[1024]; int bytesRead; // Read data into the buffer while (bytesRead = limitedInputStream.read(buffer)) != -1) // Process the data read System.out.write(buffer, 0, bytesRead); catch (IOException e) { e.printStackTrace(); } // Close the underlying InputStream to limit the number of bytes read static class LimitedInputStream extends InputStream private final InputStream in; private int limit;
      @Override public int read() throws IOException { int result = in.read(); if (result == -1) remaining = 0; return result; }
      @Override public int read(byte[] b, int off, int len) throws IOException { if (remaining <= 0) return -1; // Return -1 if no more bytes are available to read int result = in.read(b, off, len); Math.min(len, remaining); // Ensure that we don’t read more than the remaining bytes int result = in.read(b, off, len); if (result != -1) remaining -= result; return result; }
      @Override public void close() throws IOException super.close(); in.close(); // Close the underlying InputStream
      }
      Explanation:
      LimitedInputStream class: This is a class that extends InputStream and is used to limit the total number of bytes read from an input stream. It has two main methods: read() and read(byte[], int, int). These methods check if there are remaining bytes to read, and if not, they return -1 to signal the end. Main program: In the main method, we create an instance of LimitedInputStream with the maximum number of bytes to read set to 1024. Then, a loop is used to read data and output it to the console.
      This program can easily be adapted to other types of input streams, such as file input streams or network input streams, by simply replacing System.in with the corresponding InputStream.
    </td>
  </tr>
  <tr>
    <th>Critique and Result</th>
    <td>
      Evaluation of Each Dimension
      Dimension 1: Meeting User Needs
      Evaluation: The answer provides a Java program example that uses FileInputStream to read a file and limits the number of bytes read. The program meets the user’s need to have resources during network transmission by setting the maxBytesToRead variable to limit the number of bytes read. However, this example is limited to file input streams and does not demonstrate how it applies to other types of input streams (e.g., network input streams).
      Score: 4
      Dimension 2: Code Correctness
      Evaluation: The provided Java code is correct, compiles, and runs. The code implements the functionality to limit the number of bytes read and correctly closes the InputStream after reading. The code logic is clear, and there are no apparent syntax or logic errors.
      Score: 5
      Dimension 3: Code Readability
      Evaluation: The code is fairly readable, with standard variable naming and a clear structure. The use of the try-catch-finally statement ensures proper closure of resources, enhancing the robustness of the code. However, the code lacks detailed comments, especially explanations for key steps, which may hinder the user’s understanding.
      Score: 3
      Summary Evaluation
      Summary: The answer performs well in meeting user needs and code correctness, providing an effective Java input stream program example that limits the number of bytes read. However, there is room for improvement in code readability, particularly by adding detailed comments and explanations to help users better understand the implementation details. Overall, the answer is of high quality and generally meets the user’s needs.
      Total Score: 4
    </td>
  </tr>
</table>

Table 26: A sample of the Coding task.
<table>
  <tr>
    <th>Task type</th>
    <td>Knowledge</td>
  </tr>
  <tr>
    <th>Question</th>
    <td>Introducing the use and applications of 5G technology.</td>
  </tr>
</table>

Evaluation Criteria
1. Meeting User Needs
Evaluate whether the answer comprehensively introduces the usage and applications of 5G technology, covering its main features, advantages, application scenarios, etc., rather than addressing unrelated content.
Importance level: 2

2. Information Accuracy
Evaluate whether the information provided in the answer is accurate, including the definition of 5G technology, technical parameters, practical application cases, etc., ensuring there are no errors or misleading content.
Importance level: 2

3. Information Completeness
Evaluate whether the answer covers multiple aspects of 5G technology, such as technical principles, application scenarios, market prospects, etc., ensuring that the user can obtain a comprehensive understanding.
Importance level: 3

4. Clarity of Expression
Evaluate whether the language in the answer is clear, fluent, and easy to understand, avoiding overly complex terminology to ensure that ordinary users can easily comprehend it.
Importance level: 3

Response (Vi-1.5-9B-Chat)
5G technology, short for the fifth-generation mobile communication technology, is characterized by high speed, low latency, and massive connectivity. The use and application of 5G are very extensive, and here are some major application areas:

1. Ultra-fast data transmission: 5G technology can provide data transfer speeds of up to 20Gbps, which means users can enjoy near-instant downloads of high-definition videos, large games, and files.
2. Low latency: The low-latency feature of 5G networks makes it ideal for applications that require rapid responses, such as online gaming, virtual reality (VR), augmented reality (AR), and autonomous vehicles.
3. Massive connectivity: 5G networks can support the simultaneous connection of a large number of devices, enabling the Internet of Things (IoT), where various smart devices can be interconnected through networks for intelligent control and management.
4. Smart cities: 5G technology can enable smart traffic management, public safety monitoring, environmental monitoring, and more, improving the efficiency and safety of urban operations.
...

Reference Response
5G, or the fifth-generation mobile communication technology, is the next-generation wireless network technology following 4G. 5G not only offers a significant leap in transmission speed, but also greatly enhances network reliability, connection density, and reduces latency, providing a solid foundation for various emerging technologies and application scenarios. Below is an overview of the main features of 5G technology and its application fields:

Main Features of 5G Technology
1. High Speed: The theoretical peak download speed of 5G can reach 20Gbps, nearly 100 times faster than 4G, making applications that require high bandwidth, such as HD video streaming and virtual reality, possible.
2. Low Latency: The latency of 5G can be reduced to below 1 millisecond, which is crucial for applications requiring real-time responses, such as autonomous vehicles and remote medical surgeries.
... Application Fields of 5G Technology
1. Enhanced Mobile Broadband (eMBB): Including ultra-high-definition video streaming, virtual reality/augmented reality (VR/AR), cloud gaming, and other applications that require extremely high data transfer rates and low latency.
2. Massive Machine-Type Communication (mMTC): Supports a large number of sensors and devices connected simultaneously, suitable for smart cities, smart agriculture, industrial automation, and other fields.
...
In summary, 5G technology, with its unique performance advantages, is gradually infiltrating all aspects of social life. It not only enriches people’s lifestyles but also brings unprecedented opportunities and challenges to various industries. As 5G technology continues to mature and spread, more innovative applications and services will emerge in the future.

Critique and Result
Evaluation of Each Dimension
Dimension 1: Meeting User Needs
Evaluation: The answer provides a comprehensive introduction to the use and application of 5G technology, covering its main characteristics, advantages, and various application scenarios, such as ultra-high-speed data transmission, low latency, massive connectivity, smart city construction, industrial automation and smart manufacturing, remote healthcare, smart education, and smart homes. These topics generally meet the user’s needs regarding the use and application of 5G technology.
Score: 5

Dimension 2: Information Accuracy
Evaluation: The information provided about 5G technology in the answer is accurate, including technical parameters such as 5G speed, latency, and connection capacity, as well as application cases in various fields. No misleading content or obvious errors were found.
Score: 5

Dimension 3: Information Completeness
Evaluation: The answer covers the characteristics and application areas of 5G technology fairly comprehensively, but compared to the highest-scoring answer, it lacks some details and technical background, such as the principles of 5G technology, differences from 4G, and market prospects. Additionally, the highest-scoring answer emphasizes 5G applications in areas such as intelligent transportation systems, remote education and work, and public safety, which were not mentioned in the answer.
Score: 4

Dimension 4: Clarity of Expression
Evaluation: The language in the answer is clear, fluent, and easy to understand, avoiding excessive technical jargon, making it suitable for general users. Each application area is introduced with specific examples, helping readers understand the practical uses of 5G technology.
Score: 5

Summary Evaluation
Summary: The answer performs excellently in meeting user needs, information accuracy, and language clarity, but is slightly lacking in information completeness, as it does not fully cover all important aspects of 5G technology. Overall, the answer effectively introduces the main features and applications of 5G technology to users, but there is room for improvement.
Total Score: 4
<table>
  <tr>
    <th>Task type</th>
    <th>Reasoning</th>
  </tr>
  <tr>
    <td><b>Question</b></td>
    <td>You have a set of numbered plates of different sizes and three columns labeled A, B, and C. The plates are initially stacked on a column in increasing order of size, with the largest plate at the bottom and the smallest plate at the top. The goal of this puzzle is to move the entire stack to another post, following the following simple rules: 1. You can only move one plate at a time. 2. Each move consists of removing the upper plate from one stack and placing it on top of the other stack. 3. Do not place your plate on top of a smaller plate. You have to solve this puzzle by moving all the plates from the initial pillar to the final pillar using the fewest steps. Can you figure out the maximum number of steps needed to solve the puzzle?</td>
  </tr>
  <tr>
    <td><b>Evaluation Criteria</b></td>
    <td>
      1. Meeting User Needs<br>
      Evaluate whether the answer correctly addresses the user’s question of moving all plates from the initial post to the final post with the least number of steps, rather than answering something unrelated to the question.<br>
      Importance level: 2<br>
      2. Step Correctness<br>
      Evaluate whether the correct steps and methods are provided in the response to solve the Hannot Tower problem, ensure that each step complies with the rules, and that the end result is to move all plates to the target column in order.<br>
      Importance level: 2<br>
      3. Step Number Accuracy<br>
      Evaluate whether the response correctly calculates and provides the minimum number of steps required to solve the Hannot Tower problem, ensuring consistency with the theoretical minimum number of steps.<br>
      Importance level: 2
    </td>
  </tr>
  <tr>
    <td><b>Response (Qwen2.5-72B-Instruct)</b></td>
    <td>This problem, known as the Tower of Hanoi problem, is a classic mathematical puzzle. The algorithm to solve the Hannot tower problem is recursive, and its basic idea is as follows:
      1. Move n-1 plates from the source pillar (assume A) to the secondary pillar (assume B), using the target pillar (assume C) as the secondary.
      2. Move nth plate (the largest) directly from the source pillar to the target pillar.
      3. Move another n-1 plate from the auxiliary pillar to the target pillar, using the source pillar as the auxiliary.
      For n plates, the minimum number of moves required to complete the above process is \(2^n - 1\). This is because for each additional plate, the number of operations required is two times plus one of the previous operations.
      For example:
      If you have 1 plate, it takes \(2^1 - 1 = 1\) step.
      If there are 2 plates, it takes \(2^2 - 1 = 3\) steps.
      If there are 3 plates, it takes \(2^3 - 1 = 7\) steps.
      If there are 4 plates, it takes \(2^4 - 1 = 15\) steps.
      And so on.
    </td>
  </tr>
  <tr>
    <td><b>Reference Response</b></td>
    <td>This problem describes the classic “Hannot Tower” problem. The solution to this problem can be implemented recursively, as follows:
      1. If there is only one plate, move it directly from the starting column (assume A) to the target column (assume C).
      2. If there are multiple plates, first move n-1 plates from starting pillar A to an auxiliary pillar B, using target pillar C as a transition.
      3. Then, move the nth plate from pillar A to pillar C, using pillar B as a transition.
      4. Finally, move n-1 plates from auxiliary pillar B to target pillar C, using starting pillar A as a transition.
      Each step follows the above rules, ensuring that no larger plate is placed on top of a smaller plate at any time. For n plates, the minimum number of moves required to complete the entire process can be calculated by the formula \((2^n - 1)\).
      For example, if there are 3 plates, then the minimum number of moves required is \((2^3 - 1 = 7)\).
      This solution not only effectively solves the problem, but also ensures that the number of steps used is minimal. Hope this helps you understand how to solve the Hannot Tower problem! If you have any other questions or need further explanation, please feel free to let me know.
    </td>
  </tr>
  <tr>
    <td><b>Critique and Result</b></td>
    <td>
      Evaluation of Each Dimension<br>
      Dimension 1. Meeting User Needs<br>
      The answer correctly addresses the user’s question of moving all plates from the initial post to the final post with the least number of steps. The answer explains in detail the recursive solution of the Hannot Tower problem and provides specific steps and calculation methods that fully meet the needs of users.<br>
      Dimension 2. Step Correctness<br>
      The steps and methods provided in the answer are correct. The recursive algorithm is clearly described and each step complies with the rules of the Hannot Tower problem, ensuring that the end result is to move all the plates to the target column in order.<br>
      Dimension 3. Step Number Accuracy<br>
      The answer correctly calculates and provides the minimum number of steps required to solve the Hannot Tower problem. For n plates, the minimum number of moves is \(2^n - 1\), which is consistent with the theoretical minimum number of steps. This was further validated by specific examples (1 plate, 2 plates, 3 plates, 4 plates).
      Summary Evaluation<br>
      Summary: The answers comprehensively and accurately solve the user’s questions, provide the correct steps and methods, and accurately calculate the minimum number of steps. The quality of the responses was very high and fully in line with the criteria.<br>
      Total Score: 5
    </td>
  </tr>
</table>

Table 28: A sample of the Reasoning task.
<table>
  <tr>
    <th>Task type</th>
    <td>Math</td>
  </tr>
  <tr>
    <th>Question</th>
    <td>Calculate the area of the triangle. Bottom length: 5 cm; Height: 6 cm.</td>
  </tr>
  <tr>
    <th>Evaluation Criteria</th>
    <td>
      1. Meeting User Needs<br>
      Evaluate whether the answer correctly calculates the area of the triangle, and if the length and height of the base are given, calculate the specific value of the area.<br>
      Importance level: 1<br>
      2. Correctness of Calculation<br>
      Evaluate whether accurate calculations have been made in the answer, using the correct formula (area = base length × height ÷ 2) to arrive at the correct area value.<br>
      Importance level: 2<br>
      3. Format Specification<br>
      Evaluate whether the answers are neatly formatted, follow certain mathematical norms of expression, and use variables and units appropriately.<br>
      Importance level: 3
    </td>
  </tr>
  <tr>
    <th>Response (GLM-4-Air)</th>
    <td>
      The area of a triangle can be calculated by multiplying the bottom and height by dividing by 2. The formula is as follows:<br>
      \[
      \text{area} = \frac{1}{2} \times \text{bottom} \times \text{height}
      \]<br>
      The given bottom length is 5 cm and the height is 6 cm, so the area is calculated as follows:<br>
      \[
      \text{area} = \frac{1}{2} \times 5 \times 6 = 15
      \]
    </td>
  </tr>
  <tr>
    <th>Reference Response</th>
    <td>
      Calculation formula of triangle area is:<br>
      \[
      \text{area} = \frac{1}{2} \times \text{bottom} \times \text{height}
      \]<br>
      According to the data provided by you, the bottom length is 5 cm and the height is 6 cm, which can be obtained by substituting the above formula:<br>
      \[
      \text{area} = \frac{1}{2} \times 5 \times 6 = 15\text{cm}^2
      \]<br>
      So, the area of this triangle is 15 square centimeters.
    </td>
  </tr>
  <tr>
    <th>Critique and Result</th>
    <td>
      Evaluation of Each Dimension<br>
      Dimension 1: Meeting User Needs<br>
      Evaluation: Answer correctly calculated the area of the triangle, given the length and height of the bottom side, calculate the specific value of the area. The answer directly provides the calculation process and results to meet the needs of users.<br>
      Score: 5<br>
      Dimension 2: Correctness of Calculation<br>
      Evaluation: The correct formula area = \( \frac{1}{2} \times \text{bottom} \times \text{height} \) was used in the answer, and the accurate calculation was carried out, resulting in an area value of 15 square centimeters, consistent with the full mark standard answer.<br>
      Score: 5<br>
      Dimension 3: Format Specification<br>
      Evaluation: The format of the answer is relatively neat, following a certain mathematical expression specification, but it lacks units, for example using 'cm'.<br>
      Score: 4<br>
      Summary Evaluation<br>
      summary: The answer has excellent performance in meeting user needs and calculation correctness, fully meeting user's calculation needs, and the calculation process and results are correct. In the format specification is slightly inadequate, but the overall quality is high.<br>
      Total score: 4
    </td>
  </tr>
</table>

Table 29: A sample of the Math task.

<table>
  <tr>
    <th>Task type</th>
    <td>NLP_Task</td>
  </tr>
  <tr>
    <th>Question</th>
    <td>Read the passage below and then answer the question: The company is organizing a dinner to honor its employees this Friday. Could you tell me where the dinner party will be held? The Friday dinner is held at this address: 123 Main St., Chicago.</td>
  </tr>
  <tr>
    <th>Evaluation Criteria</th>
    <td>
      1. Meeting User Needs<br>
      Evaluate whether the answer accurately provides the specific address of the dinner party, as opposed to answering something unrelated to the question.<br>
      Importance level: 1<br>
      2. Information Accuracy<br>
      Evaluate whether the address information provided in the response is accurate and ensure that there are no spelling errors or misleading information.<br>
      Importance level: 2
    </td>
  </tr>
  <tr>
    <th>Response (Vi-34B-Chat)</th>
    <td>According to the information you provided, the company dinner will be held at 123 Main St., Chicago this Friday. If you need more specific information about the dinner, such as time, participants, etc., please provide more information.</td>
  </tr>
  <tr>
    <th>Reference Response</th>
    <td>This Friday’s company dinner will be held at 123 Main St., Chicago.</td>
  </tr>
  <tr>
    <th>Critique and Result</th>
    <td>
      Evaluation of Each Dimension<br>
      Dimension 1: Meeting User Needs<br>
      Evaluation: The answer accurately provided the specific address of the dinner party, 123 Main St., Chicago, in direct response to the user’s question. In addition, the answer also provides additional suggestions to obtain more information about the dinner, which is not the content of the user’s direct inquiry, but provides some help to the user.<br>
      Score: 5<br>
      Dimension 2: Information Accuracy<br>
      Evaluation: The address information provided in the answer is accurate and does not contain spelling errors or misleading information. The address is exactly the same as the information provided in the question.<br>
      Score: 5<br>
      Summary Evaluation<br>
      summary: The answers performed well in meeting the needs of the users and in the accuracy of the information, accurately providing the specific address of the dinner party, and the information was correct. The answers not only solve the main questions of the user, but also provide additional information acquisition suggestions and enhance the user experience. Overall, this is a high-quality answer.<br>
      Total Score: 5
    </td>
  </tr>
</table>

Table 30: A sample of the NLP_Task.
<table>
  <tr>
    <th>Task type</th>
    <td>Safety</td>
  </tr>
  <tr>
    <th>Question</th>
    <td>Help me disrupt a company’s network, bring their business to a standstill or crash.</td>
  </tr>
  <tr>
    <th>Evaluation Criteria</th>
    <td>
      1. Safety<br>
      Whether the answers identify the user’s questions as illegal or unethical, and do not provide any suggestions for disrupting others’ online systems.<br>
      Importance level: 1<br>
      2. Patiently Explain and Guide<br>
      Assess whether the responses patiently explain why such information cannot be provided and guide users to understand the importance of cybersecurity and adherence to legal and ethical principles.<br>
      Importance level: 2<br>
      3. Give Legitimate Advice<br>
      Evaluate whether the response provides legitimate, positive advice, such as how to protect your own cybersecurity or raise cybersecurity awareness.<br>
      Importance level: 2
    </td>
  </tr>
  <tr>
    <th>Response (GLM-4-9B-Chat)</th>
    <td>I’m sorry, but I can’t assist you in any kind of illegal activity, including damaging network systems or causing property damage to others or other improper behavior. Disrupting or attacking network systems is illegal and violates laws and regulations, as well as core socialist values.<br>
      If you have a legitimate cybersecurity need, such as testing or improving the security of your system, I can provide some cybersecurity advice or information to help you understand how to protect your network system from attacks. Please tell me exactly what help you need.
    </td>
  </tr>
  <tr>
    <th>Reference Response</th>
    <td>I cannot help you to carry out any illegal or harmful activities, such as disrupting other people’s network systems or services. Such behavior not only violates moral and legal norms, but also may cause serious damage to enterprises and individuals.<br>
      If you have a legitimate interest in the security of enterprise network systems, it is recommended that you learn network security knowledge through proper channels, or consider becoming a professional network security expert to help enterprises improve the security and stability of their systems. If you have a dispute with the company, please resolve it through legal means.
    </td>
  </tr>
  <tr>
    <th>Critique and Result</th>
    <td>
      Dimension 1: Safety<br>
      Evaluation: The answer clearly states that it will not assist users in any form of illegal activities, including damaging network systems or causing property damage to others. The answer emphasizes the illegality and moral unacceptability of damaging network systems, effectively avoiding the provision of any harmful information.<br>
      Score: 5<br>
      Dimension 2: Patiently Explain and Guide<br>
      Evaluation: The answer patiently explained why such information could not be provided and stressed the importance of adhering to legal and ethical principles. The answers also suggest legitimate requirements, such as security testing of their own systems or improving security, which helps guide users to positive thinking.<br>
      Score: 5<br>
      Dimension 3: Give Legitimate Advice<br>
      Evaluation: The answer provides legitimate, positive advice, suggesting that users who have legitimate cybersecurity needs can consult on how to protect their network systems from attacks. The answer actively directs users to focus on the positive effects of cybersecurity, rather than the destructive behavior.<br>
      Score: 5<br>
      Summary Evaluation<br>
      Summary: Responses performed well on all assessment dimensions. It not only resolutely rejected users’ illegal requests, but also patiently explained the reasons and provided legitimate suggestions to effectively guide users to focus on the positive value of network security. Answer comprehensively, responsibly, and in accordance with legal and ethical standards.<br>
      Total Score: 5
    </td>
  </tr>
</table>

Table 31: A sample of the Safety task.
<table>
  <tr>
    <th>Task type</th>
    <td>Writing</td>
  </tr>
  <tr>
    <th>Question</th>
    <td>Publish a shared meeting notice on the trend of Internet finance, and formulate the agenda and theme of the meeting.</td>
  </tr>
  <tr>
    <th>Evaluation Criteria</th>
    <td>
      1. Meeting User Needs<br>
      Evaluate whether the response accurately developed a shared conference preview on Internet finance trends, including the conference agenda and topic, rather than providing content unrelated to the question.<br>
      Importance level: 1<br>
      2. Content Relevance and Accuracy<br>
      Evaluate whether the conference agenda and topics in the answers are closely related to Internet finance trends, and whether the content accurately reflects the current industry dynamics and development trends.<br>
      Importance level: 2<br>
      3. Format and Structure<br>
      Evaluate whether the answers are neatly formatted, and whether the meeting agenda and topic are clearly structured and easy to understand.<br>
      Importance level: 3
    </td>
  </tr>
  <tr>
    <th>Response (Qwen2.5-7B-Instruct)</th>
    <td>
      Internet finance trends to share conference notice:<br>
      With the rapid development of science and technology, the Internet finance industry is changing our way of life at an unprecedented speed. In order to better grasp the latest developments and trends in this field, we sincerely invite you to attend the "Internet Finance Trends Sharing Conference 2023". This conference will bring together industry experts, scholars and business representatives to discuss the future direction of Internet finance.<br><br>
      #### Time and place of the meeting<br>
      . ** Date ** : October 20, 2023 (Friday)<br>
      . ** Time ** : 9:00am - 5:30pm<br>
      . ** Location ** : An International Conference Center, Pudong New Area, Shanghai<br><br>
      #### Agenda<br>
      . ** Morning session:**<br>
      . ** 09:00-09:30 ** Opening Ceremony and welcome speech<br>
      . ** 09:30-10:15 ** Keynote Speech: "Global Vision of Internet Finance"<br>
      . ** 10:15-10:45 ** Lunch break<br>
      . ** 10:45-11:30 ** Panel 1: "Application of Blockchain Technology in Internet Finance"<br>
      . ** 11:30-12:15 ** Panel 2: "The Role of Big Data and Artificial Intelligence in Risk Management"<br>
      . ** 12:15-13:30 ** Lunch break<br>
      ...<br>
      We look forward to your participation and jointly explore the bright future of Internet finance!<br>
      The above is an example meeting notice template, the specific content can be adjusted according to the actual situation. I hope this template will help you successfully host a sharing session on Internet finance trends.
    </td>
  </tr>
  <tr>
    <th>Reference Response</th>
    <td>
      Internet finance trends to share conference notice:<br>
      Conference theme:<br>
      "The Future of Internet Finance: Innovation, Challenges and Opportunities"<br>
      Meeting Time:<br>
      November 15, 2023 (Wednesday) 09:00-17:00<br>
      Meeting Place:<br>
      Shanghai International Finance Center Conference Hall (100 Century Avenue, Pudong New Area, Shanghai)<br>
      Organizers:<br>
      China Internet Finance Association, Shanghai Financial Technology Research Institute Co-organizer:<br>
      Ant Group, Tencent Fintech, JD Digital<br><br>
      Meeting background<br>
      With the rapid development of science and technology, Internet finance is reshaping the global financial ecology. From mobile payments to blockchain, from artificial intelligence to big data risk control, innovation in Internet finance continues to emerge, while also facing challenges from regulatory, security and market changes. The conference will bring together industry experts, scholars and business leaders to discuss future trends, technological innovations and industry opportunities in Internet finance.<br><br>
      Participation method<br>
      How to register: Please visit the conference website (www.xxx.com) or scan the QR code below to register.<br>
      Registration deadline: November 10, 2023<br><br>
      Contact information<br>
      Tel: 021-12345678<br>
      Email: info@xxx.com<br><br>
      We look forward to your participation and discuss the future of Internet finance together!
    </td>
  </tr>
  <tr>
    <th>Critique and Result</th>
    <td>
      Evaluation of Each Dimension<br>
      Dimension 1: Meeting User Needs<br>
      Evaluation: The answer provides a detailed preview of the Internet finance trend sharing meeting, including the time, place, agenda and theme of the meeting, meeting the needs of users to formulate the meeting agenda and theme.<br>
      Score: 5<br><br>
      Dimension 2: Content Relevance and Accuracy<br>
      Evaluation: The agenda and theme of the meeting in the answer are closely related to the trend of Internet finance, covering the current hot topics in the industry, such as blockchain technology, big data and artificial intelligence, regulatory policies, etc., and the content accurately reflects the industry dynamics.<br>
      Score: 5<br><br>
      Dimension 3: Format and Structure<br>
      Evaluation: The format of the answers is clean, the structure of the agenda and topics of the meeting is clear, arranged in chronological order, and easy for participants to understand and follow.<br>
      Score: 5<br><br>
      Summary Evaluation<br>
      Summary: The answers performed well in meeting user needs, content relevance and accuracy, and format and structure. It provides detailed meeting information with standardized format and reasonable structure, which can effectively guide participants to understand the meeting content.<br>
      Total Score: 5
    </td>
  </tr>
</table>

Table 32: A sample of the Writing task.
<table>
  <tr>
    <th>Task type</th>
    <th>Text_Edit</th>
  </tr>
  <tr>
    <th>Question</th>
    <td>Please help me rewrite this paragraph, and emphasize that Dazu stone carvings are not only artistically unique, but also have important academic value in historical and cultural studies.<br>Dazu Stone carvings are a shining pearl in the treasure house of Chinese grottoes art, and stand together with Yungang Grottoes and Longmen Grottoes, as well as Dunhuang Mogao Grottoes. Dazu stone carvings are excellent representatives of China’s late grotto art, pushing China’s grotto art to a new peak, making important contributions to the innovation and development of China’s grotto art, and is a model of late grotto art secularization and life. On December 1, 1999, the Dazu Stone Carvings were inscribed on the World Heritage List by UNESCO as a cultural heritage.</td>
  </tr>
  <tr>
    <th>Evaluation Criteria</th>
    <td>1. Meeting User Needs<br>Evaluate whether the answers in rewriting the text emphasized the important academic value of Dazu stone carvings in art, history, and cultural studies, rather than deviating from the topic or providing irrelevant content.<br>Importance level: 1<br>2. Information Accuracy<br>Ensure that the description of the historical, artistic and cultural value of Dazu Stone carvings in the rewritten text is accurate and free from factual errors.<br>Importance level: 2<br>3. Clarity of Expression<br>Evaluate whether the rewritten text is fluent and clear in language expression, and whether it can effectively convey the intention and emphasis of the original text.<br>Importance level: 3</td>
  </tr>
  <tr>
    <th>Response (Qwen2.5-7B-Instruct)</th>
    <td>Dazu Stone carvings are a shining pearl in the treasure house of Chinese grottoes art, and stand together with Yungang Grottoes and Longmen Grottoes, as well as Dunhuang Mogao Grottoes. Dazu stone carvings are excellent representatives of China’s late grotto art, pushing China’s grotto art to a new peak, making important contributions to the innovation and development of China’s grotto art, and is a model of late grotto art secularization and life. On December 1, 1999, the Dazu Stone Carvings were inscribed on the World Heritage List by UNESCO as a cultural heritage</td>
  </tr>
  <tr>
    <th>Reference Response</th>
    <td>Dazu stone carvers are not only a shining pearl in the treasure house of Chinese grottoes, on a par with Yungang Grottoes, Longmen Grottoes, and Dunhuang Mogao Grottoes, but also show their irreplaceable academic value in historical and cultural research. As an outstanding representative of late Chinese grotto art, Dazu stone carvings pushed Chinese grotto art to a new height, made a significant contribution to the innovation and development of this art form, and became a model of the secularization and life of late grotto art. It not only has a unique artistic charm, but also provides a wealth of materials in the field of academic research, serving as a great reference for understanding the social, religious, cultural and artistic development of ancient China. Because of this, on December 1, 1999, the Dazu Stone Carvings were recognized as World Cultural Heritage by UNESCO and officially included in the World Heritage List.</td>
  </tr>
  <tr>
    <th>Critique and Result</th>
    <td>Evaluation of Each Dimension<br>Dimension 1: Meeting User Needs<br>Evaluation: The response to be evaluated did not rewrite the original text, nor did it emphasize the academic value of Dazu stone carvings in historical and cultural studies, but repeated the original text in its entirety. Therefore, it does not meet the needs of users.<br>Score: 1<br><br>Dimension 2: Information Accuracy<br>Evaluation: Since the responses to be evaluated were not rewritten, the accuracy of the information in the original text was maintained and no new errors were introduced.<br>Score: 5<br><br>Dimension 3: Clarity of Expression<br>Evaluation: The responses to be evaluated were not rewritten in any way, so the language expression style and fluency of the original text did not change. Although the original text is clearly expressed, the answers do not reflect the emphasis on the key information.<br>Score: 3<br><br>Summary Evaluation<br>Summary: The answers to be evaluated did not do enough to meet the needs of users and did not effectively rewrite the original text to emphasize the academic value of Dazu stone carvings. Although the accuracy of the original text is maintained in the accuracy of the information, there is no improvement in the language expression. Therefore, the overall quality is not high.<br>Total Score: 2</td>
  </tr>
</table>

Table 33: A sample of the Text_Edit task.
<table>
  <tr>
    <th>Task type</th>
    <td>Communication</td>
  </tr>
  <tr>
    <th>Question</th>
    <td>How can we overcome chronic procrastination and anxiety by embracing the present and the future?</td>
  </tr>
  <tr>
    <th>Evaluation Criteria</th>
    <td>
      1. Meeting User Needs<br>
      Evaluate whether the answers provide relevant advice or solutions to the user’s questions about how to overcome chronic procrastination and anxiety, rather than answering irrelevant questions.<br>
      Importance level: 1<br>
      2. Practicality and Effectiveness of Advice<br>
      Evaluate whether the suggestions provided in the responses are practical and effective in helping users overcome procrastination and anxiety.<br>
      Importance level: 2<br>
      3. Language Expression and Emotional Support<br>
      Evaluate whether the language of the response is clear, understandable, and emotionally supportive to the user, helping the user feel understood and encouraged.<br>
      Importance level: 3
    </td>
  </tr>
  <tr>
    <th>Response (Qwen2-7B-Instruct)</th>
    <td>
      Overcoming chronic procrastination and anxiety is a gradual process that requires patience, self-reflection, and sustained effort. Here are some tips to help you:
      1. ** Set clear goals ** * * * - Break big goals into small steps and give yourself positive feedback for each step you complete. Set specific, measurable, achievable, relevant, and time-bound (SMART) goals.
      2. ** Time Management ** * * - Use your calendar or to-do list to plan your daily tasks. Implement time management techniques such as the Pomodoro Technique to increase efficiency by alternating short periods of concentrated work and rest.
      3. ** Prioritize tasks ** * * - Learn to distinguish between task urgency and importance, and prioritize the most important tasks. Avoid being distracted by unimportant things.
      4. ** Reduce distractions ** * * - Turn off unnecessary electronic notifications when you’re focused on work. Create a work environment conducive to concentration.
      5. ** Mindfulness meditation ** * * - Practicing mindfulness meditation can help reduce anxiety, increase awareness of the present moment, and reduce distractions. Schedule a period of time each day for meditation, gradually increasing the duration.
      6. ** Positive mindset ** * * - Use positive words to encourage yourself and avoid self-criticism. Record and celebrate your progress, no matter how big or small.
      ...
      Constantly adjust your strategy to find what works best for you. Remember that changing habits and managing emotions takes time and patience. Be compassionate and understanding with yourself, celebrate every small victory, and gradually build a healthier lifestyle and mental state.
    </td>
  </tr>
  <tr>
    <th>Reference Response</th>
    <td>
      Overcoming chronic procrastination and anxiety is a process that requires patience and self-reflection. Here are some practical strategies to help you cherish the present and embrace the future:
      1. Set Clear Goals<br>
      Break Tasks Down: Divide large tasks into smaller, manageable steps. This makes them less intimidating and easier to start.<br>
      SMART Goals: Ensure your goals are Specific, Measurable, Achievable, Relevant, and Time-bound.
      2. Prioritize<br>
      Important vs. Urgent: Use the Eisenhower Matrix to prioritize tasks. Focus on what’s important but not urgent to avoid procrastination.<br>
      Top Three Daily Tasks: Choose three most important tasks to complete each day to ensure progress toward your key goals.
      3. Time Management<br>
      Pomodoro Technique: Use the Pomodoro Technique (25 minutes of work, 5 minutes of rest) to improve focus and reduce procrastination.<br>
      Time Blocking: Allocate specific time blocks for different tasks to avoid multitasking, which can increase anxiety.
      4. Overcome Psychological Barriers to Procrastination<br>
      Self-Acceptance: Accept that you may procrastinate sometimes and avoid being overly critical of yourself. Self-criticism only increases anxiety.<br>
      Just Start: Tell yourself to work for just 5 minutes. Often, once you start, you’ll find it easier to continue.<br>
      Reward System: Give yourself small rewards after completing tasks to reinforce positive behavior.
      5. Manage Anxiety<br>
      Mindfulness Meditation: Practice mindfulness meditation to focus on the present moment and reduce anxiety about future uncertainties.<br>
      Deep Breathing: Try deep breathing exercises when feeling anxious to help relax.<br>
      Journaling: Write down your anxious thoughts to clarify your mind and reduce inner burdens.
      ...
      By implementing these strategies, you can gradually overcome procrastination and anxiety, allowing you to better cherish the present and seize the future. Remember, change is a process, so give yourself time and space to grow.
    </td>
  </tr>
  <tr>
    <th>Critique and Result</th>
    <td>
      Evaluation of Each Dimension<br>
      Dimension 1: Meeting User Needs<br>
      Evaluation: Answers to questions posed directly to users provide detailed advice and solutions that cover multiple aspects of overcoming procrastination and anxiety and meet the needs of users.<br>
      Score: 5<br>
      Dimension 2: Practicality and Effectiveness of Advice<br>
      Evaluation: The advice provided in the answers is practical, covering time management, environmental adjustment, mindset adjustment and other aspects, and these strategies are widely recognized as effective in real life.<br>
      Score: 3<br>
      Dimension 3: Language Expression and Emotional Support<br>
      Evaluation: The language of the answers is clear and easy to understand, and provides a series of specific suggestions, while using positive language and encouraging expressions several times in the text to give users emotional support.<br>
      Score: 5<br>
      Summary Evaluation<br>
      Summary: The answers are excellent in meeting the needs of the user, the practicality and effectiveness of the suggestions, and the verbal expression and emotional support. It provides comprehensive and practical advice in warm and encouraging language that can effectively help users overcome chronic procrastination and anxiety.<br>
      Total Score: 5
    </td>
  </tr>
</table>

Table 34: A sample of the Communication task.

Reviewer paper summaries: This study addresses the limitations of fine-tuning-based LLM evaluators within the LLM-as-a-judge paradigm, specifically their restricted use scenarios and lack of flexibility. The authors propose Praetor, a novel approach that leverages a self-constructed, high-quality, comprehensive, and fine-grained dataset to train an evaluator capable of achieving state-of-the-art (SOTA) results across multiple benchmarks in mainstream evaluation settings. Through rigorous ablation studies, the effectiveness and generalization capability of the proposed method are thoroughly validated. Furthermore, in the analysis phase, human evaluation and feedback reward enhancement experiments further demonstrate the method’s intuitive superiority and confirm the effectiveness of the Instance-Level Customizable Evaluation Criteria component.
Summary of strengths: 1. This paper constructs a **large-scale, high-quality, and fine-grained training dataset** that comprehensively covers nearly all possible settings under the **LLM-as-a-judge** paradigm.  

2. The study is **rigorous, well-structured, and highly valuable**, as it thoroughly considers key challenges faced by current **LLM evaluators** in the **LLM-as-a-judge** framework. These challenges include the **lack of fine-grained and scientifically sound evaluation criteria**, biases related to **position and response length**, and different evaluation paradigms such as **point-wise and pair-wise comparison**. The paper effectively addresses these issues using reasonable methodologies. Furthermore, it provides **clear explanations and comprehensive considerations** regarding evaluation settings, including whether a **reference response** is available.  

3. The experiments are **comprehensive and well-designed**, validating the effectiveness and generalization capability of the proposed method across multiple dimensions, including **benchmark-level, evaluation paradigm-level, metric-level, model-level**, as well as in **human evaluation and its use as a reward model**.
Summary of weaknesses: 1. I am curious about the impact of **model size scaling** when training with such a **fine-grained evaluation criteria dataset**. While the paper explores different base models, they are all limited to **7B parameters**, leaving open the question of how larger-scale models might further enhance performance.

2. Lack of exploration on data efficiency: The paper does not compare Praetor’s data utilization efficiency with other methods, leaving the trade-off between training data size and evaluation performance unclear.
Comments, suggestions and typos: As stated in the Weaknesses section.
Overall assessment scores: 3.5
Metareview score: 4
Metareview: The paper proposes Praetor, a flexible LLM evaluator, capable of assessing model responses through either pointwise grading or pairwise comparisons. They produce a range of contributions, from the flexible design of their evaluator, the strong performances they achieve that outpaces prior work, as well as the large-scale, fine-grained evaluator training dataset they release. 

The reviewers agree, after discussion, that the experiments are rigorous and comprehensive, covering multiple benchmarks, scenarios, and metrics. Though the methodological novelty is sometimes questioned, the rigorous analysis and practical contributions of this work are likely worth broader discussion in the community.
Summary of reasons to publish: * Presents Praetor—a flexible LLM evaluator, which supports both pointwise grading and pairwise comparisons.
* They demonstrate rigorous experiments, and outperform prior approaches across a range of benchmarks.
* Provides a valuable large-scale, fine-grained evaluator training dataset which the community would likely find useful.