SaMer: A Scenario-aware Multi-dimensional Evaluator for Large Language Models
Authors: Kehua Feng
Keyan Ding
Jing Yu
Yiwen Qu
Zhiwen Chen
chengfei lv
Gang Yu
Qiang Zhang
Huajun Chen
Venue: ICLR 2025 Poster (2025)
Keywords: Fine-grained Evaluation
Adaptive Multi-dimensional Evaluator
Large Language Models
Primary area: datasets and benchmarks
TL;DR: We propose a scenario-aware multi-dimensional evaluator designed to provide both overall and fine-grained assessments of LLM-generated responses.

Abstract: Evaluating the response quality of large language models (LLMs) for open-ended questions poses a significant challenge, especially given the subjectivity and multi-dimensionality of "quality" in natural language generation. Existing LLM evaluators often neglect that different scenarios require distinct evaluation criteria. In this work, we propose **SaMer**, a scenario-aware multi-dimensional evaluator designed to provide both overall and fine-grained assessments of LLM-generated responses. Unlike fixed-dimension evaluation approaches, SaMer adapts to different scenarios by automatically identifying and prioritizing relevant evaluation dimensions tailored to the given query. To achieve this, we construct a large-scale fine-grained preference dataset spanning multiple real-world scenarios, each with distinct evaluation dimensions. We then leverage a text embedding model combined with three specialized heads to predict the appropriate evaluation dimensions and corresponding scores, as well as the respective weights that contribute to the overall score. The resulting model offers fine-grained and interpretable evaluations and shows robust adaptability across diverse scenarios. Extensive experiments on eight single rating and pairwise comparison datasets demonstrate that SaMer outperforms existing baselines in a variety of evaluation tasks, showcasing its robustness, versatility, and generalizability.

Published as a conference paper at ICLR 2025
SAMER: A SCENARIO-AWARE MULTI-DIMENSIONAL EVALUATOR FOR LARGE LANGUAGE MODELS
Kehua Feng .2. Keyan D : Jing Yu?.3, Yiwen Qu Zhiwen Chen", Chengfei Lv
Gang Yu Qiang Zhang2.tt Huajun Chen 1.2f
'College of Computer Science and Technology. Zhejiang University
ZZJU Hunngoouu Global Scientific and Technological Innovation Center, Zhejiang University $Polytechnic Institute, Zhejjang University
*ZJU-UIUC Institute, Zhejiang University
AAlibaba Group
fkehuafeng, dingkeyan, qiang. zhang cs, huajunsir] ju edu.
ABSTRACT
Evaluating the response quality of large language models (LLMs) for open-ended questions poses significant challenge, especially given the subjectivity and multi- dimensionality of "quality" in natural language generation. Existing LLM evalu- ators often neglect that different scenarios require distinct evaluation criteria. this work, we propose SaMer, scenario-aware multi-dimensional evaluator de- signed to provide both overall and fine-grained assessments of LLM-generated responses. Unlike fixed-dimension evaluation approaches, SaMer adapts to dif- ferent scenarios by automatically identifying and prioritizing relevant evaluation dimensions tailored the given query. To achieve this, construct large-scale ane-grained preference dataset spanning multiple real-world scenarios, each with distinct evaluation dimensions. We then leverage text embedding model com- bined with three specialized heads to predict the appropriate evaluation dimen- sions and corresponding scores, as well as the respective weights that contribute to the overall score. The resulting model offers fine-grained and interpretable eval- uations and shows robust adaptability across diverse scenarios. Extensive exper- iments eight single rating and pairwise comparison datasets demonstrate that SaMer outperforms existing baselines a variety of evaluation tasks, showcasing its robustness, versatility, and generalizability.
INTRODUCTION
The rapid development of large language models (LLMs) has significantly enhanced their ability to generate human-like responses to open-ended questions. However, assessing the quality of these responses automatically remains critical challenge in the field of natural language processing. The complexity arises from the inherent subjectivity of "quality" in text generation, which often depends on multiple factors such relevance, coherence, factual accuracy, and fluency. Conventional au- tomated evaluation metries (e.g., BLEU (Papineni et al., 2002) and ROUGE (Lin, 2004)) often fail to account for human perception of natural language in terms of its flexibility and complexity in conveying rich yet equivalent semantic information (Zheng et al., 2023b).
In recent years, the utilization of LLMs as judges (Zheng et al., 2023b) has gained significant at- tention, with cutting-edge LLMs being employed generative evaluators to provide judgments on text quality, including single rating and pairwise comparison. Previous investigations (Zhou al., 2023; Fu et al., 2023; Zheng et al., 2023b; Gilardi et al., 2023) suggest that proprictary LLMs can effectively mimic expert evaluations when appropriately prompted; however, they are costly and pose reproducibility issues. As alternative, fine-tuning an open-source LLM to create specialized evaluators offers a cost-effective approach (Wang et al., 2023b), which has even outperformed pro- prietary LLMs in certain evaluation tasks. Nevertheless, their flexibility, robustness, and versatility
"Equal contribution. Corresponding authors.

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Published as a conference paper at ICLR 2025
(b)
Helpfulness
Helpfulness
Query: What impect Al medical diognostics?
Query: What impart of Al medical diagnostio??
Coherence
Coherence
R Response:
Response:
Correctness
Correctness
Harmlesness
Query:
What are the errors
Query:
What are the errors following code!!
Clarity
the following
Harmlessness
Innovarlvenes
Response: .......
Respouse: .......
Overall
Completeness
Overall
2.4
Figure 1: Comparison between (a) the conventional multi-dimeensinal evaluation method and (b) the proposed scenario-aware multi-dimensional evaluation approach. The conventional method uses fixed evaluation dimensions for all queries, assigning scores to attributes like helpfulness, coherence, correctness, and harmlessness, which may not reflect the context-speciff needs of each query. In contrast, our scenario-aware approach dynamically identifies relevant evaluation dimensions based on the query context. It then assigns scores and weights each dimension according its impor- tance for the scenario, allowing for more adaptive and context-sensitive overall assessment of the LLM's response quality.
still lag behind, limiting their ability to handle diverse real-world scenarios. Moreover, LLM as a judge often exhibits cognitive biases towards aspects such as response length, option order, and entity preference (Huang et al., 2024; Park et al., 2024).
On the other hand, most LLM--bssed evaluators (Wang al., 2023b; Li et al., 2023; Kim et al., 2023; Wang et al., 2023a; Kim et al., 2024) only provide an overall score or ranking, even if multi- dimensional evaluations are provided, they are limited to a few coarse-graing gaarsee and fixed dimensions such as harmlessness, honesty, and helpfulness. These approaches neglect the need for adaptive fine- gree-gra evaluations, especially when dealing with different types of open-ended questions that require varying dimensions for quality assessment. For example, the assessment of creative writ- ing should focus on originality, innovation, and coherence, while technical questions might need evaluations around practicality, accuracy, and clarity. This gap calls for more nuanced evaluation framework that can identify and prioritize the appropriate dimensions for each query and assess them accordingly, providing a comprehensive and interpretable evaluation LLM responses across diverse scenarios.
To address these challenges, we propose SaMer, a scenario-aware multi-dimensional evaluator that not only quantifies overall quality but also provides fine-gr fine-grained evaluation of LLM- L..M--eeertted re- sponses across diverse scenarios. In contrast existing approaches (Wang al., 2024a; Wettig et al., 2024) that rely on a limited set of predetermined evaluation dimensions for all queries, our approach employs a flexible framework tailored to accommodate a variety assessment criteria and adaptively identifies the appropriate evaluation dimensions based on different question types (as illustrated in Figure 1). Our approach begins by constructing large-scale pairwise preference dataset covering dozens of scenarios, each associated with distinct evaluation dimensions. We then integrate a text embedding model with three specialized heads: one for predicting which dimensions required for the given query (i.e., the dimension predictor), another for scoring the response quality of these dimensions (i.e., the dimension scorer), and the third for weighting the contribution of these dimensions the overall score (i.e., the dimension weighter). These components are jointly trained using a combination of multi-label classification loss and pairwise rank loss to ensure that the model not only learns discern relevant dimensions but also effectively ranks responses based on their quality in those dimensions. The resulting model shows the following ad advantages:
Flexibility in Evaluation Mode:
SaMer provides overall and fine- grained assessments of L.LM-generate responses, supporting single rating and pairwise comparison.
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Adaptability across Diverse Scenarios: SaMer dynamically identifies the necessary eval- uation dimensions according to different question types, allowing for flexible adaptation to diverse evaluation scenarios.
Fine-grained and Interpretable Evaluations: SaMer provides detailed weights and scores for all dimensions, enabling us to discern the dominant factors the evaluation and understand how each dimension contributes to the overall quality.
To validate the effectiveness of model, conduct extensive experiments on three single rat- ing benchmarks and five pairwise comparison benchmarks. Our results demonstrate that SaMer achieves robust performance and strong generalizability, outperforming existing baselines in multi- ple evaluation tasks.
RELATED WORK
While human judgment widely recognized the gold standard for evaluating the quality of re- sponses generated by LLMs to open-ended questions, its limitations lie in being both costly and time-consuming. Inspired by the strong instruction-followinn capabilities of advanced proprietary LLMs, many works (Zhou al., 2023; Zheng et al., 2023b; Gilardi al., 2023; Zeng et al., 2023) have explored the use language models as judges to mimic human evaluators, providing in-depth assessments. Recent research has focused open-source LLM evaluators, aiming to reduce liance on proprietary models. PandaLM (Wang et al., 2023b) is trained on LLaMA with instruction tuning, enabling it to conduct pairwise comparisons and provide results along with brief explana- tions. AUTO-J (Li et al., 2023) trained on 58 real-world scenarios and diverse evaluation protocols, produces well-structured natural language feedback and corresponding scores based on user queries and LLM-generated responses. Prometheus (Kim et al., 2023) emphasizes the importance of refer- ence answers, being trained on data incorporating over IK different scoring rubrics, thus allowing for adaptation to custom evaluation criteria. Prometheus2 (Kim et al., 2024) further enhances this ca- pability through weight merging, enabling the model to support both direct assessment and pairwise ranking evaluation protocols simultaneously.
However, these models often struggle deliver precise quantitative scores, typically functioning as task-specific classifiers (Huang et al., 2024). Furthermore, they tend to offer only an overall score, neglecting the need for multi-dimensional quantitative assessments across various question types. Existing studies have highlighted the importance of multi-dimensional evaluation (Li et al., 2024b) and argued that different scenarios require distinct dimensions or metrics to be considered (Li al., 2024a). In this paper, to the best of our knowledge, develop the first scenario-aware multi-dimensional evaluator. This model establishes distinct evaluation dimensions customized to specific scenarios and assigns scores each dimension.
3 DATASET CONSTRUCTION
Current pairwise preference datasets for training LLM evaluators, such as HelpSteer (Wang et al., 2023c), UltraFeedback (Cui al., 2023), and Preference Collection (Kim al., 2024), have started to incorporate culti-dimensional preference labels and fine-grained evaluation criteria. However, their dimensions remain simplistic and lack scenario-specific customization. To address this, we construct fine-grained preference dataset spanning diverse scenarios with comprehensive set of evaluation dimensions, enabling more tailored assessments across various scenarios. Figure illustrates the construction of our dataset, including the scenario and dimension definition, pairwise preference data collection, and fine-graine -raiind preference annotation.
3.1 SCENARIO AND DIMENSION DEFINITION
Scenario We defined 36 scenarios from the perspective of human needs, categorizing them into five main types based on Maslow's 's hierarchy: safety, social, cognitive, aesthetic, and self- actualization needs. A detailed description of scenarios is provided in Table A1.
Dimension We designed a total 42 evaluation dimensions, drawing upon previous studies (Li al., 2023; 2024a; Sharma et al., 2023). Based the scenario definitions and descriptions, we in-
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(a)
(b)
Classifer
Respaue
Response
Public Datasets
Capalitiv
Arsthetle
Noeds
Needs
Acsuasy
Maslow's
Bictallyy based on rollble inforaation
Aernalication
"Aosamay" ("aralpas": "pndfommna" '2"), "Complenmess ("tnalysis "prefermnoe" 'A"),
Nexds
Nerds
Rafeey
Diber
Comphtmass
regcases zover aggets
#1:
B
-1:
<
0: Tie
Unused
Sceealo--pecific Dimensions
Fine-grained Preference Label
Figure 2: Construction of the fine- tne-graiied preference dataset. (a) The scenarios in our dataset. (b)
The pipeline of data collection and annotation.
vited three graduate students with rich experience in natural language processing to pick the required evaluation dimensions for each scenario and assign scenario-specific definitions to each dimension. The comprehensive list of dimensions corresponding the scenarios is provided Table A2.
3.2 PAIRWISE PREFERENCE DATA COLLECTION
To collect data across wide range of scenarios, gathered large volume of publicly avail- able preference data from multiple sources, including Chatbot Arena Conversations (55K) (Zheng 2023bb, Synthetic GPT-J (Havrilla, 2023), Stanford (Eth-((Etay et al., 2022), HelpSteer- 2(Wang et al., 2024b), UltraFeedback (Cui et al., 2023), PKU-SafeRLHF (Ji et al., 2024), and Pref- erence Collection (200K) (Kim et al., 2024). Each dataset contains two model-generated responses from the same instruction (or conversation history), along with overall preference annotations from judges (labeled as win, lose, tie). To enrich scenario data, we also obtained more data from the Lysms Chat (Zheng et al., 2023a) dataset, which contains I million chat instances. To acquire pair- wise responses, we used Qwen-2-7B-In (Yang et al., 2024) (an LLM not included in the dataset) to generate new responses for each instance, and employed GPT-4o for preference annotation.
Next, we categorize the collected data samples based on their respective scenar- ios. Given that large-scale scenario anno- tation by humans proprietary LLMs is costly, we follow the strategy proposed (Li et al., 2023) train scenario clas- sifier based on Llama3-8b (Dubey et al., 2024). Specifically, we utilize the scenario classification data provided in (Li et al., 2023) as part of our training set, modifying the original scenario labels to align with our definitions. For the missing scenarios. we utilized GPT-4o-mini to annotate 33K instructions from Chatbot Arena Conver- sations (Zheng et al., 2023b) and select the data that correspond to these scenarios.
Table 1: Statistics of the constructed fine-grained pref- erence dataset
Latd Ubinttution (Lane Sumplec
1049
Searr Turset Beako Shaass
Syetex CEr
2012
SHF
11220
Hegpleer
203
Gagg PRU-SSEALL
11:0
30655
Cheber Cleaeieessa
Testorac Chllctas
41543
Sccarta Dies Warm,
Tratue Rewiting
Dackkr
Langage Recomnmaaaee
Exlpesee
1119
Hlasay
3111
Verkeakes
takeradia Extactio
4433
Tealc
Sockin Abvip
Saliz Coend boxx Protze
2055
Opet Quarkin
Schiing Mars Sears Proldwwe
1900
The Ther Towskiees
590
Riateg
3E0
Uha Ambraa
3003
Weang Secial Modaa Plo
200
3300
Koywook Easacioo
12wayy Apencidiin
300
Soeking Adiia
Eeputioued Pactiss
Weske Lagil Docenus
2000
Ode:
After labeling scenarios with the classifier, we sampled least 6K instances for each scenario from the original large dataset. To further ensure label accuracy, we employed GPT-4o-mini to verify the scenario labels and filtered out any data deemed inaccurate. Finally, we balanced the number of samples per scenario between 2K and 5K to maintain similar proportions across scenarios, with 335,402 data in total.
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(a)
Demension Prediction Layer
2. Dimension Scoring
Query
Dimensiort Prediction
Token Enbeekingg
Weighting Layer
Decidl Blocke
Decoder Block
score
dim I dim
Response
Scoring Layies
dim k
SaMer
score
Multi-tabel
Be Dimenssion eighting
Overall Scoring
Response
Class. Loss
Wi, My
(M, scorc,)
Embedding Model
Pairwise Rank Loss
Figure 3: Overview of the proposed SaMer. (a) Illustration the evaluation process SaMer, which mainly involves four steps: 1) identifying the appropriate evaluation dimensions, 2) scoring the response quality in those dimensions, 3) weighting the contribution of those dimensions, 4) and calculating overall score through weighted summation.
(b) Model architecture and training. SaMer consists text embedding model and three MLP--based prediction heads (i.e., dimension prediction, scoring, and weighting layers). The training of SaMer involves minimizing the multi- label classification loss as well as multi-dimensional and overall ranking losses.
3.3 FINE-GRAINED PREFERENCE ANNOTATION
Based the scenario labels and scenario-specific dimensions, utilize GPT-4o to perform fine- grained preference annotation. For each sample, considering that is more reliable for LLMs to perform pairwise comparisons rather than single-instance evaluations (Liu al., 2024), we instruct GPT-4o to focus solely on the specified dimension to carefully compare the two model-generated responses and select the better one or declare a tie.
Finally, we constructed a fine- fine-graine grained preference dataset D (Table 1). Each sample in D comprises query 2, a pair of responses "B). and overall preference label pa E {A, B, Tie). Addition- ally, each sample is assigned a scenario label as well as set of multi-dimensional fine-grained preference labels P {Pi, P2, PN.). J, where N, denotes the number of dimensions relevant to the given scenario.
MODEL
In this section, we present the development of SaMer, utilizing the constructed fine-grained pref- erence dataset. Figure 3 shows the model architecture, training, and evaluation process of SaMer. Specifically, the training process involves dimension prediction multi-label classification, multi- dimensional scoring via pairwise preference learning, and overall scoring via multi-dimensional weighted summation.
4.1 DIMENSION PREDICTION VIA MULTI-LABEL. CLASSIFICATION
We first dedicate SaMer to adaptively predict which evaluation dimensions should be considered for each query. To achieve this, train dimension predictor using annotated evaluation dimensions in the fine-grained preference dataset D, which selects the appropriate evaluation dimensions based on different scenarios. Specifically, we utilize frozen pre-trained LLM without its original output projection layer text encoder fe. Given query 2, we feed it into fe and extract the hidden state corresponding to the last token from the final decoder layer, yielding an h-dimensional text embedding vector fz( E Rh We then add dimension prediction layer (a shallow multi-layer perceptron (MIP)) dee E TRAXN on the top of fo. which outputs an N N-dimensional prediction vector & Pairwise Rank-based (Su et al., 2022) loss:
o fax E N The training of this MLP can be viewed as multi-label classification task. To mitigate the potential label imbalance issue, we employ the ZLPR (Zero-bounded Log-sum-exp
Lalp = log
1+ iEP
1+
e
+ log
1+ Σ
d
(1)
J&P

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4.2 MULTI-DIMENSIONAL SCORING VIA PAIRWISE PREFERENCE LEARNING
We then leverage the constructed fine-grained preference dataset to achieve multi-dimensional scor- ing. We introduce scoring layer Os E IR (another MLP) that outputs N-dimmenionaal score vector & = 0g fa( @ r.)) E IRN, based on the text embedding extracted from the concatenation of the query x and the response rs where E {A, B). Here, we adopt a pairwise preference learning approach. Specifically, for each training sample in D, we first use a mask to exclude dimensions not covered by the preference label set P. Then, for each dimension j. when the preference labels for responses and are not tied (p, * 0) on this dimension, we train them using a margin ranking loss (Cao et al., 2007) with a predefined margin 7, a tie occurs (pj = 0), meaning both responses have similar scores on this dimension, apply a regression loss to make their scores as close as possible. Thus, the multi-dimensional preference loss is formulated as
diim Σ
[pyl max (0, -Py (SAj (B,) +7) + (1 (1-1p,)) -Isaa (Bylla]
(2)
where the preference label P3 E {+1, -1, represents that response A is superioriinferior/qquul to response in the j-th dimension.
4.3 OVERALL SCORING VIA MULTI-DIMENSIONAL WEIGHTED SUMMATION
To obtain the overall evaluation score, a straightforward approach is to linearly combine the all of dimensional scores, but this overlooks the fact that the contribution of each dimension varies across different evaluation scenarios. To address this, introduce a weighting layer dg R which produces set of non-negative normalized weights {w og fo(x) E RN w; 0. w; 1} based on the embedding fa(z) E R of the query x. These weights are then multiplied with the multi-dimensional scores Ç to obtain the scalar overall score S for the given x and response i.e.,
wt Ç. To train the weighting layer, we employ the previous preference loss again:
Lo = |pol- max (0, -Po (SA SB) + + (1 - (pol) ||S - SBl12
(3)
where p. E {+1, -1, denotes the overall preference label between responses A and B.
Finally, we jointly optimize the dimension prediction, scoring and weighting layers of SaMer by minimizing the combination of the aforementioned losses:
min
ED (Cripe + > Caim A2Ca),
(4)
where a and X2 are the hyper-parameters that balance the contributions of each loss.
5 EXPERIMENTS
5.1
IMPLEMENTATION OF SAMER
We utilize the Llama-3 8B (Dubey al.,, 2024) the text emhedding model and initialize it with the parameters from a Llama-3 8B Reward Model trained by (Wang et al., 2024a). Three MLP layers, specifically de. d2. and øg. are appended to the embedding model and trained with the loss function specified in Eq. (4), while keeping the embedding frozen. Notably, de undergoes a warm-up pre- training phase using the loss function Laip in Eq. (1), aiming enhance SaMer's scenario-aware multi-dimensional prediction capability.
During training, we set 7 = 0.3 in Eq. (2) and (3), = in Eq. (4). Particularly, the impact of X₁ and A on the performance of SaMer is discussed in the Appendix A4.1. The text embedding dimension 4096, consistent with the hidden size of Llama-3-8B, while the dimension N of the three MLP layers is 42, representing the total number of pre-defined dimensions. To efficiently train the model, we leverage the DeepSpeed libsary(Rasley et al., 2020), Zero Redundancy Optimizer (ZeRO) Stage 2 (Rajbhandari et al., 2020), and FlashAttention2 (Dao, 2023) across 2 NVIDIA GeForce RTX 4090. We adopt AdamW (Loshchilov, 2017) as our optimizer, with A1 = 0.9, B3 = 0.95, and weight decay of 0.1. The peak learning rate is set to 5e-5, with 10% warm-up steps, and a cosine decay to O. We set the batch size to 32 and the maximum sequence length to 8.192. The model is trained for 3 epochs to ensure convergence and optimal performance.

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5.2 BENCHMARKS AND METRICS
To thoroughly evaluate SaMer, we employ three types of benchmarks: overall-level single rating and pairwise comparison, well dimension-level fine-grained comparison.
Single Rating (Overall) is practical evaluation approach as it eliminates the need to prepare a comparison baseline. However, it is inherently challenging since the evaluating language model (LM) must produce scores based its own internal judgment without external references. We have prepared three single rating benchmarks:
Vicuna Bench (Kim et al., 2023): Adapted from Vicuna (Chiang et al., 2023), this bench- mark consists of 80 single-turn chat prompts and 320 total responses generated by four models: WizardLM- 13BB, Vicuna-13B, Llama2-13B-Chhn and GPT-3.5-Turbo-D61 The responses are scored by GPT-4.
FLASK Eval (Ye et al., 2023): A benchmark contains 200 diverse test prompts and 2K responses from Alpaca-7B, Vicuna-13B, Bard, and GPT-3.5-Turbo-0611.. We utilize the aaman--nnntated scores as the reference standard.
Feedback Bench (Kim et al., 2023): The test set of Feedback Collection (Kim et al., 2023) comprising IK scoring rubrics, 200 instructions, and 1K model- godel-generated responses. To accurately gauge the model's scoring capabilities, we exclude the scoring guidelines and reference answers.
Pairwise comparison (Overall) is an effective strategy for evaluating LLMs. By comparing pairs of model-generated responses, it reduces subjectivity in assessment and encourages the identification of finer distinctions. Through pairwise comparisons, aim to explore the alignment between the evaluator and human preferences. We selected five benchmarks for this purpose:
HHH Alignment (Askell et al., 2021): A dataset of 221 paired responses created by An- thropic, designed to evaluate model's alignment and preference accuracy across four sce- narios: Helpfulness, Harmlessness, Honesty, and Other.
LLMBar (Zeng et al., 2023): meta-evaluation benchmark aimed at assessing LLM evalua- tors' ability to distinguish instruction-followee followwnn outputs. It includes one Natural subset and four Adversarial subsets: Neighbor, GPTInst, GPTOut, and Manual. The Natural subset reflects real-world distributions with objective preferences, while the Adversarial subset contains crafted outputs deviating from instructions.
AutoJ Eval: An in-domain test set from (Li et al., 2023), consisting of 58 prompts and 1,392 response pairs, with preferences labeled by human annotators as win, tie, or lose.
AlpacaEval (Dubois et al., 2024): A benchmark reconstructed from the Alpac- Farm (Dubois et al., 2023) dataset, containing 805 test prompts from five data sources. We use a version scored by GPT-4, which includes 13 model responses and 10,463 pairs.
Preference Bench (Kim et al., 2024): An in-domain test set of the Prometheus model, containing 200 prompts and 2,000 response pairs. This dataset is generated by combining responses from five different models in the Feedback Collection (Kim et al., 2023).
Fine-Grained Comparison (Multi-dimensionaa) is a complex evaluation task that requires an evaluator to compare pairs of responses based specified dimension and produce preference judgments that are exclusive to that dimension. The model's effectiveness on this task hinges on its ability to deeply understand the given dimension while remaining entirely uninfluenced by others. To assess this capability, we use concealed test set with 10 samples per scenario derived from the multi-scenario, multi-dimenssinal fine-grained preference data gathered in Section 3.3, which term as MD-Eval that encompasses a total of 360 human-verified samples.
5.3 BASELINES
We employed proprietary LLM baselines, including OpenAl's OPT-44-2024-05--1 (OpenAI, 2024a), GPT-4o-min-2004--77-1 (OpenAI, 2024b), and Anthropic's Claude-3.5-6 3.5-Somnet-2024-066
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Table 2: Evaluation results on three single rating benchmarks. Bold results and underline results indicate the best and the second-best results among the open-source models, respectively. Same as below.
Vicuna Bench
FLASK Eval
Feedback Bench
Evaluator
Pearson
Spearman
Kendall- Tau
Peurson
Spearman
Kendall Tau
Pearson
Spearman
Kendall Tau
GPT-4o-mini
0.456
0.323
0.286
0.493
0.449
0.371
0.752
0.764
0.671
Claudo-3.-Someet
0.489
0,326
0.286
0,420
0.400
0.322
0.726
0.726
0.630
GPT-4o
0.434
0.306
0,263
0,501
0.478
0,389
0.735
0.731
0.636
Llama-2-777 Baamm---BB-Chan Chal
0.021
0,042
0.037
0.092
0.072
0,059
0,492
0.553
0.488
Llama-2 2-13B Chat
0.072
0.014
0.013
0.217
0.133
0,113
0.569
0.538
0.468
Llama:3. 3.8B Inst
0.007
.0.088
0.007
0.091
0.072
0,062
0,464
0,489
0.434
L.amm-3.1188 Inst
0.298
0.188
0.167
0.336
0.269
0,228
0.623
0.660
0.586
Mistral-7B-Inss
0.190
0.117
0.104
0,243
0.179
0,154
0.525
0.531
0.469
AutoJ-13B
0.360
0.364
0.317
0.458
0.384
0.312
0.609
0.605
0.523
Prometheux- 7B
0.413
0.416
0.354
0,246
0.206
0,161
Prometheus 13B
0.268
0.272
0.237
0.354
0.314
0,255
Prometheus2-78
0.267
0,254
0.219
0.335
0.259
0,209
AmooMM-8B
0.446
0.396
0.300
0,359
0.315
0.225
0.749
0.754
0.607
SaMer-SB
0.476
0.458
0.354
0.515
0.468
0.345
0.790
0.783
0.631
20 (Anthropic, 2024), alongside open-source L.M buselines such as Llama-2-7B/13B- (Tou- vron et al., 2023), -lmm-3-8BBIInstruee (Dubey et al., 2024), L.ama-3.1-8B3 1-8B-Instruct (Dubey et al., 2024), and Destral-7B-Instruet-e ically trained for evaluation tasks but exhibit strong instruction-followim capabilities, we used prompts from LLMBar (Zeng et al., 2023) to assess their performance on evaluation tasks. For state- of-the-art LLM evaluator baselines, we adopted state-of-the-ane art models including AutoJ- (Li et al., 2023), Prometheus-7B/13B (Kim et al., 2023), Prometheus2-78 (Kim et al., 2024), and ArMoRM-8B (Wang et al., 2024a). To ensure fair comparison, we used each model's original prompt templates and excluded the reference answer module from the templates of Prometheus se- ries.
(Jiang et al., 2023). Given that these models were not specif-
5.4 EVALUATION RESULTS
To demonstrate our SaMer's adaptability across various evaluation modes, we conducted three sets of experiments: single ratings, pairwise comparisons, as well as multi-dimensional fine-grained comparisons. As experiments focus on investigating the generalizability of evaluators across diverse evaluation scenarios, we have not included the held-in results, such as Prometheus' perfor- mance in Feedback Bench.
In the single rating (Table 2) tasks, SaMer demonstrated significant improvements across three single rating benchmarks, particularly in FLASK Eval. is noteworthy that SaMer exhibits compa- rable performance and even surpasses proprietary models, including GPT-40/4o-mini and Claude- 3.5-Sonnet (detailed discussion is illustrated in the Appendix A4.3). However, one can observe that most models (including SaMer) did not achieve correlation coefficient exceeding 0.5 with the an- notated labels on Vicuna Bench and FLASK. indicating the challenging nature of these benchmarks and the complexity of aligning model evaluations with human ratings.
In the pairwise comparison tasks (Tables and 4), SaMer achieved top performance in out of 15 tasks and ranked second in the remaining 6 tasks among the open-source models. This strong performance can be partly attributed to the robust ArmoRM backbone, which also delivered leading results across multiple tasks. However, we highlight the effectiveness of our strategy, as evidenced by SaMer's notable improvements on Avaac Despite that the proprietary models generally outperform all open-source models in these evaluations, SaMer showcases competitive results by outperforming many open-source baselines and coming close to the proprietary models' perfor- mance in several tasks, highlighting its robust adaptability.
In the fine-grained comparison tasks (Table 5), the results show that dimension-leved accuracy was generally lower than overall accuracy for most evaluators, underscoring the challenge of accurately evaluating responses on specific dimensions. An intriguing observation was the performance drop in Llama-2-1 1-----1-1---- 3BBChat compared to its 7B counterpart, suggesting that increasing model parameters
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Table 3: Evaluation results on four pairwise comparison benchmarks
Preference Bench
HHH Alignment
Autol Eval
AlpacaEval
Evaluator
Help.
Harn.
Hon.
Other
Total Avg.
w/o TIE
w/ TIE
OOD
w/o TIE
w TIB
GPT-4o-mini
89.83
91.38
75.41
93.02
86.88
79.20
$9.34
88.85
82.01
83.54
Claude-3.5 Sonnee
91.53
94.83
88.52
93.02
91.86
76.64
61.68
85.10
73.70
76.02
GPT-4o
89.83
94.83
88.52
93.02
90.95
76.05
58.33
84,10
76.58
78.61
Lluma-2-7B-
59.32
71.43
45,90
53,66
56.12
48,99
35,99
57.04
50.56
46.15
Lluma-2-13B-Chne
71.43
76.47
61.22
71.43
68.79
54.95
40.62
63.91
54.39
50.62
Llama-3-8B-Inst
79.66
80.70
73.77
88.37
80.00
62.51
48.49
75.55
66.90
69.45
Llmm-3.1-8BBInstt
83.05
84.62
78.69
88.10
83.18
71.61
52.70
79.80
74.24
74.38
Mistral-7B-Inst
71.19
81.03
67.21
74.42
73.30
58.74
43.34
63.40
56.08
56.40
AutoJ-13B
70.49
72.09
76.02
77.43
77.72
72.59
Prometheus-7B
50.85
43.10
48.84
49.32
47.20
47.63
79.90
43.72
48.57
Prometheus-135
66.10
48.28
34.43
65.12
52.49
46.71
48.64
76.80
40.03
45.23
Prometheus2-7B
83.05
75.86
63.93
76.74
74.66
74.78
55.72
74.05
75.00
ArmoRM-SB
89.83
93.10
78.69
93.02
88.24
76.74
57.18
86.90
76.74
78.76
SaMer-8B
88.14
89.66
78.69
95.35
87.33
77.72
58.69
83.50
81,38
83.00
Table 4: Evaluation results the LLMBar benchmark
Table 5: Evaluation results the
MD-Eval dataset
Evaluator
GPTInst
GPTOut
Manual
Neighbor
Natural
GPT-4o-mini
83.70
65.96
63.04
67.16
91.00
Evaluator
Dim Acc.
Overall Ace.
Claude-3.-Sounet
88.04
61.70
78.26
92.00
GPT-44-mini
72.99
78.00
GPT-4o
78.26
77.61
Claude
GPT-4o
Llama-2-7B-Chr
48.35
46.81
41.30
43.61
58.00
Jlama-2-13B-C-
33.77
31.82
Llama 2.7B 2-7B-Chat
53.13
53.58
Llmma-3-8B-Ins
39.13
55.32
41.30
21.64
Llama-2-1338 2-1BB-hht
48.47
53:47
Llama-3.1
43.48
55.32
43.48
Llama- 3-8---3--B-Ins-
64,96
66.67
Llama 3. 1.1.8B.. 1-88-Inntt
73.13
71.91
Mistral-7B-Inst
$1.09
46.81
45.65
45.52
76.00
Mistral-7B-Inst
55.70
62.80
Autol-13B
23.91
50.00
26.67
23.48
71.13
Autel 13B
Prometheus-78
15.22
36.17
34.78
17.16
48.00
Prometheus-7B
60.22
38.33
Prometheus- -3ttheeus--BBBB
14.13
46.81
28.26
15.67
$9.00
Prometheus- J3B
64,96
43.67
Prometheus2-77
29.35
58.70
37.78
22.39
77.00
Prometheus2
ArmoRM-8B
77.17
63.83
69.57
67.16
93.00
AmoRM-8B
79.33
SaMer-8B
54.35
65.96
69.57
86.57
84.00
SaMer-8B
75.67
82.33
does not necessarily lead to better fine- tieegrrined evaluation capabilities. contrast, SaMer achieved notable improvements, with + +10.7 increase in dimension-level accuracy and a +15.7 increase in overall accuracy compared to the Lllmm-3-8B-Inns the original backbone of SaMer. Moreover, when compared to proprictary models, SaMer outperformed both GPT-4o-mini and Claude-3.5- Sonnet. This result underscores the strength and effectiveness of our training methodology in en- hancing multi-dimeensonal, scenario-aware evaluations.
5.5 INTERPRETABLE EVALUATIONS
In addition to its robust performance across various benchmarks, SaMer excels in providing inter- pretable evaluations offering detailed dimension-speciif scores and weights. These fine-grained assessments enable deeper understanding of how each dimension contributes to the overall quality, allowing us identify the dominant factors in this evaluation.
SaMer exhibits scenario- aware adaptability, effectively identifying contextually appropriate evalua- tion dimensions for queries that may not have explicit scenario labels. This capability exemplified in Fig. 4, which shows the weights assigned by SaMer across three distinct scenarios: Creative Writ- ing, Math Reasoning, and Writing Legal Document. These weights indicate the relative importance of each dimension within the evaluation process. For Creative Writing, SaMer assigns the greatest importance to the creativity dimension, followed sequentially by logic, relevance, harmlessness, and style, which aligns closely with the essential attributes of creative writing. In contrast, for Math Reasoning, the emphasis shifts towards the reasoning process and outcome, with logic, accuracy, clarity, and step-by-step explanation emerging the predominant dimensions. When evaluating the

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Published as a conference paper at ICLR 2025
Caslive Winling
Math Reasoning
30
Walling Legal Document
10
10
Adoit Uncortbioy
Audrecc Pricedy
Authoristt
Chanto
Clarity
Corerage
Creaiistyy
Diguh
Infermotion Richowre
Fonthility
Insigtt
Logel
Appenta
Objeervity
Orgiality
Profesiian Profesranatium
Rele Relevatiee
Fandiner
Artrestir
Binnatiitty
Profauuna
Bong Fricady
Coberewe
Finalia
Enotion
Step by Step Eapl aapiinaiin
Laneth
Sty
Clou cerertees
Virid
Chuda Roekbbbity
Insin fen Palleeitg
Midhlaiity
Complecoe Frithiolose Painuns Old Revall the Regining Engegemen
Positg
Mulippl
Figure 4: Average dimension weights of SaMer when evaluating scenarios in the MD-Eval dataset, including Creative Writing, Math Reasoning, and Writing Legal Document. The most preferred attribute for each category is highlighted in the corresponding color: Creativity for Creative Writing, Accuracy for Math Reasoning, and Logic for Writing Legal Document.
Writing Legal Document scenario, logic is identified the most critical dimension, followed by considerations of harmlessness and clarity, reflecting the distinct requirements of legal writing.
To demonstrate SaMer's accuracy in dimension prediction, we compared its performance with generative LLM base- lines on the dimension selection task. The full set of 42 dimensions was fixed to ensure consistency across mod- els. with baselines guided by prompts select appropri- ate dimensions for each scenario. The evaluation was on both in-domain (ID, MD-Eval) and out-of-domain (OOD, Auto-J Eval) (Li et al., 2023) datasets. For OOD, 108 unique examples were selected after filtering out overlap- ping scenarios. Precision and Recall were used for ID eval- uation, while OOD evaluation was based on the win rate against SaMer (the proportion of cases where a baseline outperformed SaMer according to human judgment).
Table 6: Performance comparison of SaMer and LLM baselines on the di- mension selection task (ID and OOD).
ID
OOD (S)
Evaluntor
Precision
Recall
Win Rate
GPT-4o
63.42
38.10
48.15
GPT-4o-mini
57.61
37,89
37.65
Mistral-7B-Inst
42.73
23.82
14.42
LJama2-7B-Chal
27.78
37.36
10.84
LIama3-8B-Inst
39.81
36.37
21.61
L.ama3.1-SB-Im
34.96
28.06
SaMer-8B
74.84
72.33
The results in Table 6 show that SaMer outperforms the baselines. In the ID evaluation, SaMer achieves 74.84% Precision and 72.33% Recall, while most baselines fail to exceed 50% Precision and 40% Recall, indicating current LLMs' limitations. Among the baselines, GPT-4o performs rela- tively well with high precision but low recall, suggesting a more selective (and less comprehensive) subset of dimensions. In the OOD evaluation, SaMer surpasses GPT-4o, whose win rate below 50%, showing that SaMer aligns better with human preferences.
6 CONCLUSION
In this work, we propose SaMer, a scenario-aware multi-dimensional evaluator designed to pro- vide fine-grained and interpretable assessments of LLM-generated responses. By dynamically iden- tifying and prioritizing relevant evaluation dimensions for different query scenarios, SaMer en- ables more nuanced and context-sensitive evaluations compared conventional fixed-dimension approaches. Extensive experiments across single rating and pairwise comparison benchmarks val- idate the model's adaptability, showing that SaMer outperforms existing baselines while offering transparent and interpretable assessments through detailed dimension- scores and weights.
Despite its strengths, SaMer has some limitations. First, its performance depends on the quality and diversity of the fine-graines preference dataset, which, while comprehensive, may not cover all possible real-world scenarios. Additionally, the model's capacity to interpret complex and overlap- ping dimensions requires further enhancement. For future work, expanding the dataset to include broader scenarios and refining SaMer to better handle contextually ambiguous queries would im- prove its robustness. Furthermore, SaMer can serve as a reward model for reinforcement learning, guiding LLMs to optimize not just overall quality but also specific dimensions that match human preferences, enhancing response quality in targeted way.
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ACKNOWLEDGEMENTS
This work is supported by Zhejiang Provincial "Jianbing" "Lingyan" Research and Development Program of China (2024C01 135, 2025C01097), National Natural Science Foundation of China (62301480, 62302433, U23A20496), Hangzhou West Lake Pearl Project Leading Innovative Youth Team Project (TD2023017) and Alibaba Group through Alibaba Innovative Research Program.
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A1 SCENARIO AND DIMENSION DEFINITION
In this section, we provide detailed presentation of the scenarios and evaluation dimensions. Table A1 lists the 36 specific scenarios along with their definitions (or descriptions), while Table A2 presents the auman-verified evaluation dimensions required for each scenario. Since the definitions of the dimensions scenario-specific, we have provided dedicated set of evaluation dimension definitions for each scenario in Section A1.1.
A1.1 SCENARIO-SPECIHIC EVALUATION DIMENSION DEFINITIONS
Writing Legal Document
1. Accurecy: The dialogue must provide procise and correct information, casuring that all logal terms, conditions, and clauses correctly
legally sound.
Adnit Uncertainn ntt: The dialogue should acknowledge any uncertainty lack of information, providing guid- ance on where to seck further clarification or legal advice.
Authent of ty: The dinlogue should reflect gemaine legal standards and practices, using appropriate legal terminology and referencing actual logal principles and precedents.
citation: The dialogue should include references relevant laws, statates, regulations, legal cases to support the infor- mation provided, ensaring the document"s 's credibility and legal validity.
Clarity: dialogue must clear understandable, avoiding overly complex language or jargon that could confuse the reader. Logal terms should be explained where necessury.
Complet eneas: s: The dialogue should cover all necessary of the logal document, ensuring that no critical information or sections are omitted.
not harmful misleading, providing accurate legal information 8. Layout: The dialogue should guide the user in structuring the legal document properly, casuring that it follows logical and nccepted format legal texts.
esaness: that does not the user at risk of legal issues.
dialogue must ensure
Logic: The dialogue should follow logical sequence, ensuring legal arguments clauses are presented coherent and rational manner.
10. Muil t tile Aspecta:: The dialegue should consider varions aspects the legal issue hand, providing comprehensive view that addresses different angles and potential implications.
11. Ub jbjectivity The dialogue should maintain objective presenting information and advice based on legal standards and facts rather than personal opinions or biases.
12. Professionaa on The dialogue should exhibit high level of professionalism, reflecting the formal and serious nature of legal document drafting, and ensuring that all advice is sound and reliable.
Seeking Medical Advice
1. Accuracy: The information provided should be modically accurate, based on current medical knowledge and guidelines.
Admit Uncertainty: The diulogue should scknowledge when the information is uncertain or when lurther professional consultation necessary.
3. Authenticity: The advice should be genuine and trustworthy, reflecting real medical knowledge and practioes.
citation: Whenever possible, information should backed by credible sources or references medical
Clarity: The language used should be clear and easy to understand, avoiding medical jargon unless explained.
Completeness: The response should cover all relevant aspects of the medical query, providing a thorough answer.
Coverages The advice should address the full scope the user's question, considering various facets of the medical issue.
8. Peasibility: The recommendations given should be practical and achievable for the user.
Harmlessne essness: information should safe, avoiding any advice could potentially
user.
10. Logie: The advice should follow logical sequence, with well-reasoned explanations and conclusions.
11. Maltiple Aspects: The dialague should consider different perspectives and aspects of the medical issue, such us symp-
potential causes, and treatment options.
12. Objectivityy The advice should be unbiased and based on factual information rather than personal opinions.
Prafessioralls
reflect professional attitde, muintaining respectful and serious ap-
proach to the medical issue.
14. Relevance: The response should be directly relevant the user's 's specific medical query or concern.
15. style: The communication style should be appropriate for medical context, maintaining balance between professionalism and empathy.
16. Timeliness: advice be current, reflecting latest medical guidelines and
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Inquire Safe Experimental Practices
Accuracy: information provided must be factually correct on established scientific peinciples safety standards.
Autthenticity:
The advice and guidelines should be genuine and reflect real world practices and regulations.
Cinatiion: Relevant sources, such as scientific literature, regulatory guidelines, or expert opinions, should be cited to support information provided.
Clarity: The advice should communicated clear and understandable explained.
avoiding technical jargon unless
The response should cover necessary aspects of safe experimental practices, including prepuration, exe- cution, experimeen procedues..
Compler ctoness:
The response should address wide range of safety considerations, including but not limited to chemical, biolog- ical, physical, and environmental hazards.
Coverage:
Peasibility y::: The recommended practioes should be practical and achievable within constraints typical experimental settings.
8. Ra rml s: The information should ensure that no harmfull or dangerous advice given, prioritizing the well-heing of individuals and the enviroument.
reasoning rationale each recommendattion Cl ject jeciiity:::: The information should unbiased based objective evidence personal opinions dotal experiences.
Logic: advice should logical sequence,
11. Profeas ona a am: The tone and content should reflect a high level of expertise and professionllsm, suitable for a scientific technical andience.
12. Relevance: The advice should be directly relevant the specilic of experiment safety concern being inquired about.
Chitchat
response engaging and able capture interest, making conversation enjoyable lively. Audience Friendly: The response considerate of needs foelings, fostering positive inclusive inter- action.
Attractive:
Bei Be Friend! y: The character displays friendly artitude and tone, making the user feel welcomed and comfortable.
Coherence: The response flows naturally and logically, with responses that smoothly connect presious and following statements.
Creativity: The character demonstrates unique and imaginative thinking, contributing original and interesting topics to the sonversation.
Rmo Emojis are appropriately to inappropriate.
expression, adding emotional color without being
7. Emotion: The character expresses genuine and strong emotions, making the conversation more relatable and engaging.
Harml essneas: The response ensures that no content is harmful or offensive, maintaining a safe and respectful environment.
Interactiviit:: The response promates active participation and engagementt, encouraging the user contribute to the conversation.
10. Length: The responses of appropriatc length, providing sufficient detail without being overly long too brief.
11. Logic:
The respunse follows logical sequence, with coherent explanations and sound reasoming.
12. Relevvnce: The responses are directly relevant to the user's statements and the overall topic of the conversation.
13. Style: The character maintains a consistent and approriate style, whether formal or informal, as suited to the user's chitchat
14. Vivid: The response vivid and lively, using concrete and imaginative language to make the conversation visually and
emotionally enguging.
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Roleplay
Attractive: character enguging and to capture user's and interest. dialogue novel interesting, sparking the user's curiosity and desire participate.
2. Audience Fri Friandly: The character considers the user's needs and feelings, providing positive interaction experience. It shows respect and understanding interests.
the user, with information internction methods suitable the level
3. Being sriendly: yy The character displays friendly attitude and tone. The language and behavior are gentle and polite, making feel welcome comfortable.
Coherence: content
dialogue is logically following context, avoiding jumps or hreaks.
connected. Responses naturally link previous and
Czeat evily::ly: The character demonstrates unique ideas and innovative expression. The dialogue includes novel and interest- ing viewpoints or expressions, bringing surprise inspiration
Emo Emojis uppropriately used dialogue enhance the expression. They used fittingly, adding emotional color and fun without being excessive or inappropriate
Emat i on: The character can express genuine and strong emotions. Emotional expressions are sincere and natural, resonating with the
Barmlessness The character's 's words and actions de not harm offense to the user others. It avoids offensive. discrimimatrry, or harmful statements and behaviors, ensuring the dialogue is safe and respectful.
That ructi ion Fowlowing: pechends the user's requirements without defying or rojocting them.
character accurately understands and executes the instructions, accurately com-
10. Interact ivity: yt The character engages in active and effective interaction with the user. It maintains the internctivity of the dialogue through questions, foedback, and other means, promoting user participation.
Logic: The character's with reasonable reasoming processes, avniding contrudictions.
actions are logically sound, clearly expressing viewpoints. Responses are oell-organized,
12. Relevance The character's is responses are closely related to the dialogue topic and the user's needs. Answers directly address 's questions topics, avoiding digressions irrelevant content.
13. Style: The character maintains consistent response style that matches the expected tone and style that lits the user's 'ss's requested character language style.
keeps consistent
14. divid: The character's expressions are vivid and lively, enhuncing the attractiveness of the dialogue. uses comcrete and vivid descriptions and language, making the dialogue more visually and emotionally ongaging.
Writing Social Media Post
Astrecti ve: The post captures the audience's attention with compelling visuals, interesting content, engaging language, making it stand out in a crowded feed.
Audience Friendly: The post is considerate the audience"s preferences, interests, and needs, ensuring resonates well with them.
3. Being Friendly: The tooe of the apprachable and warm, making the audience feel welcomed and valued.
4. Clarity: The message is clear and easy to understand, avoiding ambiguity and confusion.
Coherence: The post flows logically and muintains consistent theme or message throughout.
Ureativity: The pool shows originality and imaginative thinking, offering unique content that dilferentiates from others.
the post more visually Rmojia: a: Emojis used appropriately enhance the message, emotional nuance, ongaging without overwbelming the text.
8. Emotion: The post conveys genuine and strong emations, making it more relatable and engaging for the audience.
Ha rml essneas: s: The content safe and respectful, avoiding any offensive or harmful language or imagery.
10. Length: The post is concise and to the point, providing sufficient information without being overly lengthy or too brief.
11. Logic: The post follows logical structure, with ideas presented in a coherent and rational order.
12. Originslity: The content unique and mere repetition existing posts, offering perspectives information.
13. Style: The post maintains consistent and appropriate style, whether formal informal, tailored the platform and audience.
14. Timelineas: The post is relevant current events, trends, the audience"s immediate interests, making it timely and
topical.
15. Vivid: The post uses vivid descriptive language visuals create strong impression engage the audience's seuses.
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Table AI: Detailed description for each scenario in our dataset.
Safety Noeds
writing legal document
Writing legal documents a formal and precise process that involves drafting, reviowing, and fimilizing legal texts such us contracts, wills, deeds, and legal agreements.
sooking medical advice
Seoking matical advice involves converiaiion where indivihal scoks information or guidince reganding health- sysptous,
related
medical conditions.
inquine safe experimental practices
Inquiring about experimental practices involves seeking information, guidelines, advice selentific or tectmical expeniments a manner that ensures the safety of all paricipaaes, minimizes risks, and adheres to establisbed standards and regulations.
conduct
Social Needs
chischat
Chischat casual, informal converation focused on social interaction ruther than exchanging detailed information or solving problems.
toleplay
Role play dialegue is an interactive simulation where participants assume specific roles to mimic real life scenacios for training. education, entertainmett purposes.
writing social media post
Writing social media post involves crafting message intended shtrinng social platfonss like Ficebook, Twimer, Instigram, etc.
Cagnilive Needs
remsoning
reasoming involves logical and systematic thinking to sulve problems. Solving general problem involves providing an through caasoning, logic, conceptiall understanding, qualitative analysis
solving general exam problem
solving math exam problem
It requires understanding mathematical concepts. applying appropriase methods, and providing clear, step by--ot----pppp expla- nations to unive at accurate solutions.
instractional rownitigg
Instractional rowrkinng involves modifyyngg or oshoncing given test resporse improve its clarity, accuracy, coher- attributes while fathhfully adhering to original instructions or intent.
ence,
code writing
Cide writing involves creating, oditing, and dehigging code to solive problems, implement festares, or automate melks
text tnaslatioon
Text to test translation is the process of converting tex: from one language to another while preserving the meuning, tome, and contect of the ungimall test.
general explanation
general explanation the act of peoviding clear, detailed, and undertandablic explanations on wide raage of topics. aiming inform, clarify, and educate the aufience effectively.
functional writing
Fonctional writing in response context refors to the exchange information instractions clear, procise, and efficient manner to achieve a specific purpose.
Get verification
Verifying fact in dialogue contest involves confirming the securacy and reliabiiit of information presented the conversation.
general anilysis
Analyzing general rype of dialogue focused cramining. interprcting, and understanding various subjects data in non-speciftc monner.
lext summarization
Text summarication the process of condensing a piece of text a shorter version, retaining the most importan: infor- mation and meaning.
calegory identification
Category identi fication involves accurately classilying identifying multiple objects provided by the into peodefined specific categorios.
title generation
Title generation involves creating an appropriate and compelling title given or based on a description a work.
question gemenation
Question genenation is the proces of creating questions bused on given comtent cuntest.
rewding comprchemsion
Reading comprehemsion involves unswering quesiios that can be direcily answered by informution cuntained within the attached passage.
keywords extraction
Keywords extraction involves udemifying extracting the impoctast and relevant keywords from given piece
information extraction
Information extration involves extracting specific categories of information as specified by the user from a given piece of lest.
topic modeling
Topic modeling involves extracting high-level topics themes from given text to identity and summarize the main
subjects discussed.
ranking
Ranking involves sorting set itens based on specified criteria.
data analysis
Data analsis refers to the process of inspecting, cleaming, transferming and modeling data with the goal of discovering useful informution, drawing conclusions, and supporting decision-makinn
Aesthetic Needs
literary uppreciation
Literary appreciation the analysis evalusaion literary woeks, focusing their artistic qualities, themes, and stylisiic elements.
lmngung polish
Language polish refers to the process of refining and enhoneing piece of text to improve its clarity, rewlabiltt, and owerall quality
recommendation
recommendation dialogue conversation where one party suggests products, services, actions another based. on their preferences, needs, interest..
sreative writing
Creative writing is the art of crafting stories, poetry, and other literary works that emphasize imaginative and original expression.
Self- Actusliutzzzzionn Needs
soeking advice
Seeking advice dislogue scenario where one participant requests guidance, recommendatioss or solutions to specific problem or situation.
planning
Planning in dislogue refer to conversations where the primary poul is to organive, striegive, and prepare for future actions events.
hrinsttrminng
Brainstorming collsbeeative and exestive process where participants generate wide mage ideas solutions partisular probiem or challenge.
open question answering
Open question dialogue invulves queries that cannot be answened with a simple "yes" or "no".
value judgement
Value Indgemest is the process of evalutinng or making decisious hased on pensonal beliafs, values, or standards.
Other
an unlabeled scenario, involves engaging in open ended conversations, providing information, answering ques- tions, and assisting with vatious requests bused on user instructions.
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Table A2: The evaluation dimensions for each scenario in our dataset
Safety Noeds
writing legal document
Accumay. Admil Uncertainty Authenticity, Citation, Clarity, Harmlosness, Logic, Multiple Axpects, Objectivity, Pro- fessional, Layout, Completeenss
sooking medical advice
Acemecy, Admit Uncestainy. Authenniciryy Citrrion, Clarity, Completenevs, Coverge, Fesuihility, Harmlesonoss I aggl.
Multiple Asgects, Objeetivity, Professionlism, Relevnaes Style, Timeliness
inquine safe exper imental practices
Accuracy. Authenticity, Ciation, Charity Compleseness, Coverage, Feasibility, Harmlessnes, Logic, Objectivity,
fessioealism, Relevance
Social Needs
chitchat
Audience Friendly, Creatvity, Hammlessness, Logic, Relevance, Amractive, Interactivily. Being Friendly, Coheresce, Emejis, Emotion, Length, Style, Vivid
releplay
Audience Friendly, Creaiivity Hemnlesnnes, Lagic Relevance, Atimetivee, Interactivity, Being Friendly, Ceherence,
Emujos, Emotion. Style, Vavid, Instuction Followng
writing social modia post
Audienne Friendly, Clarity, Creativtyy Hamnlesones, Lagic, Originality Timeliness, Attractive, Reing Friendly, Coher- ence, Eunojis, Emotion, Length Style, Vivid
Cugniiive Needs
reasoning
Accuncy, Authenticity, Clarit, Logic, Objectivity Relevance. Professooal, Step by Step Explanation, Instruction Fol- lowing. Layout Modularity Completeness, Poining Out, Result at the Beginning
solving general exam problem
Acenmcy. Andience Friendly, Autheatiitty Clarity, Fensibiiity Harmlossness, Logic. Objeativity, Relevance, Timeli- ness, Professional, Step by Stop Explaration, Completeness
solving math exam problem
Accumcy. Authenticity, Cluity, Logic. Objectivity, Relevance. Professional, Step by Step Explanation, Instruction Ioll lowing. Layour Medulazity Completeness, Poiming Out, Result at the Beginning
instractional rownitigg
Accmncy Authenticity, Clarity, Crativity, Hanslesiness, Logic, Relevance, Coherenoe, Langth, Style, Iestmiction Fel- lowing. Completenes,, Faihfolness, Pointing Oui
code writing
Accuncy. Clarity, Fensibility Harmlesaness Logic, Professional, Style, Stop by Step Explanation, Code Conrectness Code Readability, Instraction Following. Layour, Modalarity
lest unmslation
Accunay. Authemicity, Clarity, Harmleesness Lugic, Objectivity Prufessiomalism, Relevance, Coherence, Style, In- struction Following. Completeness, Futhhulness
general explanation
Acconay, Admit Uncetainty, Audience Friendly, Authemicity, Ciotion, Clarity, Cuverage, Depih, Harmlesnnes, Logic, Multiple Aspects, Objestivity. Professionllien emm Relevince. Coherenoe, Step by Step Explanation, Instmetionn Following
functional writing
Accumcy. Authentcity, Cintion. Clarity, Coverage Creativtyy Depth, Harmlesnnes, Insight, Logic. Professionalism, Relevanee, Coherznce, Longth, Style, Instraction Following, Leyour, Complateness, Faihfulncss
fact vertfccatinn
Accuracy. Admit Uncertainty. Auchenicity, Ciation, Claits, Hammeesness, Information Richness, Objectivity, Rele- vance, Timeliness, Instruction Following Compleiness, Faihiulness,,
the Beginning
general andlysis
Accuncy. Admit Uncertainty. Andience Friendly, Authemticity Citation, Clarity, Covenge, Creativity, Depch, Fensi- bility, Harmlesness, Information Richness, Insight, Logic, Mubiplle Aspect, Objectivity Originalit, Prufessionlism, Relevenoe, Timelinesa
text summarization
Accuracy, Citatin, Clarity, Harmlessness, Logic. Objectivit, Relevance, Cohrrence, Length, Instraction Following, Completeness, Fuithulneess Resalt Beginning
calegury identification
Accuracy Clarity, Relevance, Completeness, Faihfulness
tirle generation
Andienae Friendly, Clarity, Craiiiily, Relevanoe, Engagement
question generation
Audience Friendly. Creativity Harmlessness, Logic, Relevance, Anractive, Interactivity, Being Friendly, Coherence, Emujis, Emotion, Length, Style, Vivid
reading comprchionsionn
Acemncy. Clarity, Depth, Insight, Completeness
keywords extraction
Accuracy. Clarity, Relevance, Completeness, Fatthfulness
information extraction
Accuracy. Clarity, Relevance, Completeness, Faithfulness
topic modeling
Clarity, Information Richness, Relevance, Coherence, Completeness
ranking
Accurayy Clarny, Relevance, Campleteness, Fattfflnnes
dats analysis
Audience Friendly. Creaivviy, Hamnlesmess, Logic, Relevance, Alimetivve Internctivity, Being Friendly. Cohemmon Fmojis, Fimnion, Lengh, Style, Vivid
Aesthetic Needs
literary uppreciation
Attractive, Being Friendly, Coherence. Coverage, Creativity, Depth, Harmlosaness, Insight, Logic, Multiple Aspects, Originality, Professional, Relevance. Style, Vivid
languag polish
Audienoe Friendly, Creativity, Depth, Hannlesnnss, Logic, Relovance, Altmntive, Professional, Cohlerence, Style, Vivid, Instraction Following, Completeness. Fuihhfuhess, Pointing Out
recommendation
Andience Friendly, Authenricity, Citatioo, Coverage. Depth, Homleeness,, Information Richness, Logic, Objectvvity Kelevanee, Timeliness, Interactivity, Being Friendly, Coberence, Instruction Fallowing
creutive writing
Audionoe Friendly, Creativiyy Depth, Harnlessnes, Informasion Richness, Insighi, Logic, Mulliple Aspects, Origimlity. Relevance, Attractive, Being Friendly, Coherence. Emotion, Style, Vivid, Instraction Following, Facing
Self Auff-AAtuaiizziinn Needs
seeking advice
Accuracy. Admit Unceritiaty Audience Friendly, Authenticity, Citztio, Clarzy, Covenge, Feasibility, Harmlesseess, Logic. Multiple Aspects Relevance, Timeliness. Professiona. Being Friendly. Completeness
planning
Azcuracy Audience Friendly, Clarity, Creativity Feasibility, Hammlessness, Logic, Prufessionalism. Relevance, Timeli- ness, Interactivity, Instraction Following. Modularity Completeness
briinttoriing
Admit Userutray,, Audicace Frieadly Authenticity, Clucity Covenge, Creativity, Depth, Fessibility, Hamdessnsss information Richness. Insight, Logic, Multigle Aspects, Objectivity, Originaiit, Relevance, Timeliness, Attractive, In- teraetivity, Professional
open question answering
Uncertainty, Audience Friendly, Clarity, Creativiyy Depth, Hammlessncss, Information Kichness. Insight, Logic.
Multiple Aspects. Originality Relevance, Deing Friendly, Coherence, Style
value judgement
Admit Uneestainty Audience Friendly, Authentiityy Coverage, Depth, Hommossaees, Insight, Logic, Multiple Aspects, Objeciviity Being Frienily
Other
Audienoe Friendly, Creativity Hammlessness Legic, Relevance, Astractive, Interactivity, Coherence, Emotion, Length
Style, Vivid
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A2 DETAILS OF DATA ANNOTATION
In Section 3.3, we utilized GPT-4o to conduct fine-grained, dimension-level preference annotations. Specifically, using the prompts outlined in Table A3, we instructed GPT-4o to focus solely on the specified dimension, carefully compare the responses generated by two models, and select the better one declare tie. Additionally, required GPT-4o to provide pairwise comparison analysis, detailing the strengths and weaknesses of the two responses within the specified dimension to justify its decision.
Table A3: The prompt for GPT-4o fine- fne-grained preference annotation.
[System Prompt]:
two dialogues with the same instruction, your to determine which dialogue better, fully urilize your capabilities, you will be provided with the secnario of the dialogues and brief introduction to the sccnario, as well as the attributes need to consider. You lirst need to conduct a detailed comparison for each attribute, determining which dialogue is better for each attribute. Finally, please make comprehensive judgment based on all the atiributes determine which dialogue is relatively betler.
are helpful assistant.
[User Prompt]:
Here are two dialogues with the same instraction, To avoid redundancy, we first provide the instruction of the dialogues, followed by the responses to the instruction.
[Instruction Start]:
{prompt)
[Instruction End]
|Response Start]]
fresponse.ar |
[Response End]
IResponse Start]:
(response_b) [Response End]
To fully utilize your capabilities, you will be provided with the scenario the dialogues and a brief introduction to the scenario, as well as the attribates you need to consider.
IScenario Start]:
[scenarin]
[Scenario End]
[Attributes Start]:
{attributes)
[Attributes bnd]
Please first conduct detailed comparison for each attribute dependently (only analyze one attribute, you should ignore other attrihutes when comparing!), I), and then make comprehensive judgment based the importance of each attribute, and guess the most important attributes that human You should extract detailed evidence when analyzing the performance of each dialogue, Your judgment results should only be "I" for dialogue 1 and dialogue If their performances are similar, should "tic". Your output must follow the format below:
attributes determine which dialogue is better. should weight preferred (refer the preference label).
"Name of attribate e 1f: If
"comper isont: *Fical wxtirast detailed evidence, then fullly analysw the per Cormance of LMO dialogues worde",
make comperison, no less "kinner"s "I, or tie. for dielogue 1 or 2. for dialogue 2, bie when their performences are sim Tar" 1b.
stersbute
"Name at attribate 2n
tooparison"s
ratt metwilled evidence, then fully analyse performance altribute Lo make pompet no leas than 50 words",
"iinnrr:; f1, 2, or tie, 1. for dislogue 1 or 2 for dialogue 2, tie when their perfornences are sim:lar"
"Overall"; 14
Poopari isont; apirss dytailly analyse the must important attributes should be considered in this scenacio
(reter the preterence label) and the weights ot all atcribute, tinally comprehensive judgrent on weighted atsributes, gl laae anelyse detail.", "xinner"s "I, or tie. for dielogue or 2 for dialogue 2, tim when their performences are similar"
Overall winner should not be the dialogue that contains obvious deficiencies or defects. Ouput the above format in JSON. Again, the name the attributes you should consider (utrr.buessnumm. You should ensure that output include all the attributes. Do not output any other churacters.
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After collecting fine tne-grained preference annotations for all data, we employed a data pre- screening method inspired by STaR (Zelikman et al., 2022) to preliminarily enhance data quality. Specifically, assumed that reliable pairwise comparison analysis could effectively capture the strengths and weaknesses of the models, enabling GPT-4o to determine the correct overall winner by aggregating evaluations across all dimensions. This process is analogous to deriving accurate answer through a reliable reasoning process (Zelikman et al., 2022).
To improve data quality, compared the overall winners annotated by GPT-4o with the human- verified preference labels in the dataset. We retained 78% of the original data that matched human- verified preferences, thereby enhancing the dataset's alignment with human preferences.
A3 QUALITY CONTROL
A3.1
SCENARIO-SPECIFI EVALUATION DIMENSIONS CONSTRUCTION
In Section 3.1, we mentioned that the evaluation dimensions required for each scenario were deter- mined by three graduate students. To mitigate biases caused by the small sample size, we strictly adhered to the following measures to ensure the reliability these decisions:
Collecting Human Feedback. Before making any decisions, we encouraged the three graduate students to solicit feedback from volunteers by collecting 10 questionnaires for each scenario. These questionnaires aimed to investigate which dimensions humans tend to consider in each specific sce- nario. The graduate students selected dimensions with selection rate exceeding 60% in each sce- nario as candidate dimensions for further discussion.
Referencing Related Works. We referred to related studies, such as Auto-J, which provided in- sights into dimension selection for specific scenarios. While these studies differ from our work in terms of scenario design, they offered valuable inspiration for dimension selection in similar con- texts.
Based on these efforts, the final decisions were collectively made through discussions among three graduate students. Therefore, although some dimensions may have been overlooked, the tained dimensions for each scenario were carefully reviewed and scrutinized to ensure their validity.
A3.2 HUMAN VERIFICATION OF MD-EVAL
We detail the process of constructing MD-Eval as follows:
Data Source MD-Fval data was derived from a subset of the data constructed in Section 3. 3-1.3.3, with 10 samples collected for each scenario, resulting in total of 360 samples (5, 482 dimension- level annotations). It is important to note that while using GPT-4o for data annotation, we ad- ditionally generated pairwise comparison analyses for each dimension to serve references for subsequent human annotators.
Human Verification Process We enlisted three graduate students with foundational NLP research experience to verify the data. Specifically, the participants reviewed GPT-4o's 's analysis for cach dimension to identify any obvious biases and then provided validated preference annotations. We ensured that these participants annotated every dimension in each sample.
Final Annotation Agreement The final preference label for each dimension was determined by the majority vote among participants. We defined the inter-annotator agreement as the average proportion of participants selecting the most chosen preference for each dimension. The agreement among the annotators was calculated to be 87.61%.
Comparison with GPT-4o Annotations We compared the human-validated labels with GPT-4o annotations and found an agreement of 82.49%. This demonstrates that the annotation process described in Section 3.1-3.3 resulted in GPT-4o labels that align closely with human annotations and exhibit a level of agreement comparable to that among human annotators, further supporting the reliability of the training data.
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A4 ADDITIONAL DISCUSSIONS
A4.1 ABLATION STUDY ON A1 AND A2
As presented in Table A4, we explored the impact of multiple combinations of λ and X2 in Eq. (4) by systematically in- creasing and decreasing their values. When λ X2 (e.g.,
Table A4: Impact of X1 and X2 on SaMer performance in single rat- ing and pairwise comparison tasks.
A1 1.5, X 1), we observed a performance improve- ment in the pairwise comparison task but slight degradation in the single rating task. This can be attributed to the fact that in the pairwise comparison setting, SaMer evaluates two re- sponses using the same evaluation dimensions and their as- sociated weights, making dimension-level scoring capabilities particularly critical. Since Å controls the rank loss at the di- mension level. it plays a direct role in this context. Conversely, in the single rating setting, accurately predicting both evaluation dimensions and their weights be- comes essential, and excessively low X2 values hinder the accurate prediction of these weights.
Vicuna Beach (%)
Auto-J Eval (%)
As.
Pearson
w/o TIB
w/ TIE
1.1
47.59
76.15
57.61
0.5. 1
47.15
75,86
57.33
1.5,
47.58
76.25
57.76
1,0.5
47.39
76.35
57,76
1,1.5
47.97
75.37
56.97
When A A (e.g., λ₁ 1, X2 1.5), found that SaMer's performance improved in the single rating task but deteriorated in the pairwise comparison task. This indicates that increasing 22 indeed benefits the prediction of dimension weights, thereby enhancing the accuracy of single rating tasks, further validating our carlier analysis. However, in the case of λ 0.5 and X2 = 1, both single rating and pairwise comparison tasks exhibited poor performance, underscoring the critical importance of dimension-level scoring (i.e., A1).
In summary, considering the respective roles and significance of \ and X2, our Idesign choice of setting \1 = A2 = 1 is both reasonable and effective.
A4.2 INFERENCE EFFICIENCY OF SAMER
We further evaluate the inference efficiency of SaMer compared to other large language models with simi- lar parameter scale (i.e., 7-8B). Experiments were con- ducted using NVIDIA GeForce RTX 4090 GPU, with all model parameters stored in bf16 precision. The in- ference framework employed is based on HuggingFace rans formers Python library (Wolf, 2020).
Table A5: Inference efficiency compar- ison of SaMer and baseline L.MMs (av- erage runtime in seconds per instance).
Single Rating (i)
Pairwise Comparison (L)
Evaluator
Llama2-7B-Chan
0.29
0.23
Llama3-8B-HK -B--3---B-Inttt
0.11
0.28
As shown in Table A5, SaMer exhibits a significantly lower average runtime for processing a single data in- stance compared to the other baselines, highlighting its superior efficiency. This efficiency can be attributed to SaMer's design as a classification model, which requires only single forward pass through MLP layers to generate the final classification result. In contrast, the baselines, being causal language models, involve the generation and analysis of text segments during evaluation, requiring multiple forward passes, which ultimately reduces their inference speed.
Llama3.1-8BD --St-Inst
0.10
0.19
Promethus-7B
0.74
1.26
Promethus2-7B
0.64
0.57
SaMer-8B
0.04
0.10
A4.3 EXPLANATION OF SAMER'S 'S OUTSTANDING PERFORMANCE IN SINGLE RATING
In Table 2, the Pearson correlation of SaMer on the Vicuna Bench (Chiang et al., 2023) is compara- ble to proprictary models, i.c.. GPT-40/4o-minii (OpenAI, 2024a) and Claude-3.5-Sonnee (Anthropic, 2024), while its Spearman and Kendall correlations are significantly higher. This observation sug- gests that, despite GPT-40 performing all annotations for the preference dataset construction (in 3), SaMer outperforms GPT-4o on these metrics. Furthermore, the substantial difference between the Spearman and Kendall correlations and the Pearson correlation warrants further explanation.
We summarize three key factors to explain why SaMer outperforms GPT-4o in single-rating tasks:
1. SaMer employs the preference data for training: It's important emphasize that the data used to train SaMer was not derived from GPT-4o's 's single ratings. Instead, when con- structing the preference data for SaMer's training, we instructed GPT-4o to perform pair-
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wise comparisons, selecting the better response between two options. For LLMs, pairwise comparison is considered a more reliable evaluation method than single rating (Liu et al., 2024). Therefore, it is possible that SaMer, trained preference data, could outperform GPT-4o in single-rating evaluations.
2. Fine-grained evaluation provides an advantage over coarse-grained evaluation: When using GPT-4o for preference annotation, focused single-dimension preferences, al- lowing SaMer to integrate preference information across multiple dimensions to compute aggregated score. In contrast, when evaluating GPT-4o on Vicuna Bench, it was only asked to perform coarse-grand grasse-ggaindddee scoring.
3. Differences in scoring range and score discreteness: In typical LLM judging scenarios on Vicuna Bench, scoring is usually done on a discrete scale of 1-5 (similar to classifi- cation task with five categories). SaMer, however, outputs continuous score within the range (0, 1), making it more sensitive to quality variations.
Additionally, Spearman correlation is rank-based, measuring the consistency of rank order between two variables, while Kendall correlation compares pairwise order consistency between samples. These metrics are better suited for handling continuous, non-repeated values. When duplicate values are present, as often occurs in the Vicuna Bench where GPT-4o frequently assigns the same score (e.g., 232 out of 320 responses received score of 5), the rank information becomes incom- plete, negatively impacting these correlations. contrast, SaMer produces fewer duplicate scores, maintaining better rank-order integrity.
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Reviews:
[{"review_id": "7TsP8ghLsG", "reviewer": "Reviewer_o3px", "summary": "This paper introduces SaMer, a novel scenario-aware multi-dimensional evaluator designed to assess the quality of Large Language Model (LLM) responses to open-ended questions. The work addresses a significant challenge in LLM evaluation: the complex, multifaceted nature of response quality assessment, which varies significantly across different types of queries and use cases. While traditional LLM evaluators typically provide only overall scores or rankings, with some offering limited multidimensional evaluations across fixed dimensions like harmlessness, honesty, and helpfulness, different scenarios require different evaluation criteria. The key innovation of SaMer lies in its ability to provide both overall and fine-grained assessments of LLM-generated responses, with a flexible framework that adaptively identifies and prioritizes relevant evaluation dimensions based on the query type. This approach contrasts with existing methods that rely on predetermined evaluation dimensions. To support this framework, the authors developed a large-scale fine-grained preference dataset encompassing 42 evaluation dimensions across 36 scenarios, with scenarios categorized into five main types based on Maslow's hierarchy. Three graduate students created dimension definitions, and the final dataset contains 135,402 total data points, with 2,000-5,000 samples per scenario.\nThe dataset construction started with the collection of preference data (win/lose/tie labels) from multiple sources, including custom datasets built using LLM-generated annotations. Scenario annotation utilized a custom-trained LLaMA-3-8B classifier on [Li et al., 2023] data, enriched with GPT4-o-mini annotations for target scenarios without previously existing annotations. The sampling process initially gathered 6,000+ instances per scenario, followed by GPT-4o-mini verification of scenario labels and final balancing to 2,000-5,000 samples per scenario. Fine-grained preference annotation was performed using GPT-4o.\nThe model architecture consists of a frozen pre-trained LLaMA-3-8B (pre-trained for rewarding using [Wang et al., 2024]) as the text encoder, with its original output projection layer removed. On top of this encoder, the model employs three specialized MLP heads: a quality dimension prediction layer performing multi-label classification using ZLPR loss, a pairwise preference learning layer using margin ranking loss, and a weighting layer for overall scoring also utilizing margin ranking loss. The training process combines these losses (L_{zlpr}, L_{dim}, and L_{o}) through weight hyperparameters lambda.\nThe evaluation of SaMer was conducted across 9 benchmarks spanning 3 families. For single rating benchmarks, the assessment used Pearson, Spearman, and Kendall-Tau correlations, while preference selection tasks were evaluated using accuracy metrics for pairwise and fine-grained comparisons. The results demonstrate SaMer's superior performance compared to existing baselines, showing strong adaptability across diverse scenarios while providing interpretable, fine-grained evaluations.", "questions": "Please specify the version of the employed proprietary models to ensure reproducibility.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is clear and well-structured, effectively communicating complex ideas through a logical flow and professional presentation.\n- The fine-grained preference dataset represents a valuable contribution to the field.\n- The model is efficient and practical, making it accessible for deployment and fine-tuning.\n- The evaluation is thorough and comprehensive, benchmarking against both proprietary models and open-source alternatives.", "weaknesses": "- From a methodological standpoint, the paper's novelty is limited. While it combines multiple components to create a comprehensive evaluation system, the individual techniques used are standard approaches in the field, and many data resources are derived. The main innovation lies in the application rather than the methodology itself.\n- The hyperparameter analysis lacks depth and transparency. For example, the authors fix lambda values to combine the different losses without exploring or discussing their impact on model performance. Similarly, other hyperparameters are presented with single values, offering no insights into their selection process or their effects on the model's behavior. This absence of ablation studies or sensitivity analyses makes it difficult to understand the robustness of the approach.\n- The use of GPT-4o-based models on this scale for intensive annotation and filtering is resource-intensive and appreciable. However, this heavy reliance can introduce potential biases and raises questions about the overall training dataset quality.\n- There is no discussion of verification steps or quality control through human analysis to ensure the artificial annotations' reliability. This is particularly important given the subjective nature of many evaluation dimensions.\n- The supplementary materials lack data samples, making it impossible for reviewers to assess the quality and characteristics of the dataset.\n- The metrics section (5.2) requires a more detailed explanation.\n- The paper does not clearly provide information about code availability, model checkpoint distribution, or dataset licensing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BkzLD2BgmL", "reviewer": "Reviewer_EYLr", "summary": "This paper proposes an evaluation model (SaMer) that assess the quality of responses based a 42 pre-selected dimensions and gives an overall evaluation by weighting scores on different dimensions. The experimental results show that the proposed model achieves the best efficacy on single rating datasets and the fine-grained comparion data curated by the authors, and it also achieves a comparable performance with ArmoRM on pairwise comparison datasets.", "questions": "- Typos: The term \"Demension\" in Figure 3 should be corrected to \"Dimension.\"\n\n- How is the dimension-level accuracy in Table 5 calculated?\n\n- The data used to train fine-grained preferences is primarily annotated by GPT-4o; however, GPT-4o has issues with confusing different dimensions [2]. How can you ensure that the data generated by GPT-4o is of high quality?\n\n- Could you please provide more information on how you verify the quality of MD-Eval test set (used in Table 5) through human evaluation?\n\n[2] Are LLM-based Evaluators Confusing NLG Quality Criteria? (Hu et al., ACL 2024)", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Compared to past evaluators that assessed overall quality, the fine-grained evaluation method proposed in this paper provides more fine-grained evaluation results. The weights across different dimensions offer a certain level of interpretability.\n\n- Since this model can implicitly determine the required dimensions based solely on the query without task information, this evaluator is task-agnostic and possesses a certain degree of generality.", "weaknesses": "- The evaluator's ability to understand different dimensions is questionable. Firstly, the 42 dimensions are fixed, and the model did not even encounter these dimension definitions during training (nor did the paper provide their specific definitions). Secondly, the experimental results do not demonstrate whether the evaluator can accurately determine which dimensions are needed when encountering out-of-domain data. Compared to similar work like X-Eval [1], which allows users to input personalized dimension definitions, it is hard to say that this model has a clear advantage.\n\n- The paper employs three loss functions but lacks ablation studies to investigate the impact of choosing $\\lambda_1$ and $\\lambda_2$ on model performance, making it difficult for readers to understand the individual contributions of these designs.\n\n\n\n[1] X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects (Liu et al., NAACL 2024)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hk3RICrvWF", "reviewer": "Reviewer_DeNC", "summary": "This paper presents SaMer, a new framework to evaluate LLM responses with more flexibility. Instead of using static criteria for evaluating open-ended generation, SaMer adapts its approach based on the context, whether it's looking at a creative response, answering a factual question, or offering advice. To achieve this, the authors created a large-scale dataset covering a variety of real-life scenarios so SaMer could learn to recognize what matters most in different situations. The framework is set up with three parts: one to pick relevant criteria, one to score based on those criteria, and one to weigh them for an overall score. \n\nExperiments indicate that SaMer performs competitively, achieving improvements over baseline models across single rating, pairwise comparison, and fine-grained dimension-specific benchmarks. It also sheds light on which factors are key in different scenarios. Overall, SaMer brings a more adaptable, context-sensitive way to evaluate LLM responses, making it better suited to assess responses based on what's actually needed in each situation.", "questions": "In Table 2, SaMer's Pearson correlation on the Vicuna Bench is comparable to GPT-4o, but its Spearman and Kendall correlations are significantly higher. Given that GPT-4o performed all preference annotations, why does SaMer outperform GPT-4o on these metrics, and are there any insights into why these correlations differ so much from the Pearson correlation?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The motivation for a more fine-grained, multi-dimensional evaluation of LLM responses is reasonable and relatively under-explored, as current evaluations usually rely on a single overall score.\n2. The authors have built a large-scale preference dataset from existing corpora, covering diverse scenarios and annotating each with corresponding evaluation dimensions.\n3. Experimentally, the model shows performance improvements over baselines across several settings.", "weaknesses": "**Major Issues:**\n\n1. **The choice of evaluation dimensions for each scenario is unclear**: The authors mention that three graduate students annotated the evaluation dimensions for each scenario. However, this task seems highly subjective and challenging. From Table A2, many dimensions appear vague or redundant. For instance, why doesn’t \"data analysis\" include \"Accuracy\"? Why is \"Logic\" missing for \"reading comprehension\"? “Recommendation” seems subjective, so why does it include \"Objectivity\"? \"Accuracy\" and \"Admit Uncertainty\" also seem somewhat contradictory, yet both are included in five scenarios. \"Being Friendly\" and \"Audience Friendly\" seem overlapping. Overall, the annotations lack clarity and intuitiveness. The authors could improve this by defining these dimensions and including the guidelines used for annotation in the paper. Additionally, reporting inter-annotator agreement with discussion would be helpful.\n\n2. **The predicted evaluation dimensions are still pre-defined**: Although SaMer claims to flexibly predict relevant evaluation dimensions per scenario, it actually selects from a set of 42 dimensions defined in previous studies. This limits SaMer's ability to generalize to broader, unforeseen scenarios.\n\n3. **No evaluation of selected evaluation dimensions**: The experiments in this paper focus on overall response preference or specific fixed dimensions. However, one of SaMer's primary contributions is automating the prediction of relevant dimensions for each scenario, yet there is no experiment supporting SaMer’s effectiveness in this. A potential experiment could involve having LLMs generate reasonable evaluation dimensions for given scenarios as a baseline, then comparing those results with SaMer's using human judgment as a metric.\n\n4. **Missing related work**: Multi-dimensional and explainable evaluations for text generation are not new ideas, and it would be helpful for the authors to include comparisons and discussions with prior works such as [1-3].\n\n**Minor Issues:**\n\n1. **Potential unfair comparisons**: The training of SaMer heavily relies on GPT-4o for preference annotation, while some baselines may not have used this newer and potentially stronger teacher model, which could lead to unfair comparisons.\n2. **Lack of efficiency discussion**: Since SaMer first predicts evaluation dimensions before aggregating them into an overall score, discussing runtime and efficiency comparisons would help the community better understand the practical efficiency of this framework.\n\n**References:**\n\n[1] Zhong et al. Towards a Unified Multi-Dimensional Evaluator for Text Generation. EMNLP 2022.\n\n[2] Liu et al. G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment. EMNLP 2023.\n\n[3] Xu et al. INSTRUCTSCORE: Explainable Text Generation Evaluation with Finegrained Feedback. EMNLP 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)