IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering
Authors: Ruosen Li
Ruochen Li
Barry Wang
Xinya Du
Venue: NeurIPS 2024 poster (2024)
Keywords: Interactive Question Answering
Automatic Evaluation
Question Answering
LLM Evaluation
Primary area: natural_language_processing

Abstract: To evaluate Large Language Models (LLMs) for question answering (QA), traditional methods typically focus on directly assessing the immediate responses generated by the models based on the given question and context. In the common use case of humans seeking AI assistant’s help in finding information, these non-interactive evaluations do not account for the dynamic nature of human-model conversations, and interaction-aware evaluations have shown that accurate models are not necessarily preferred by humans Lee et al. Recent works in human-computer interaction (HCI) have employed human evaluators to conduct interactions and evaluations, but they are often prohibitively expensive and time-consuming to scale. In this work, we introduce an automated evaluation framework IQA-EVAL to Interactive Question Answering Evaluations, more specifically, we introduce LLM-based Evaluation Agent (LEA) that can: (1) simulate human behaviors to generate interactions with IQA models; (2) automatically evaluate the generated interactions. Moreover, we propose assigning personas to LEAs to better simulate groups of real human evaluators. We show that: (1) our evaluation framework with GPT-4 (or Claude) as the backbone model achieves a high correlation with human evaluations on the IQA task; (2) assigning personas to LEA to better represent the crowd further significantly improves correlations. Finally, we use our automated metric to evaluate five recent LLMs with over 1000 questions from complex and ambiguous question answering tasks, which would cost $5k if evaluated by humans.

IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering
Ruosen Li', Ruochen Li', Barry Wang"?, and Xinya Du'
'Department of Computer Science, University of Texas at Dallas 'Department of Computer Science, Carnegie Mellon University ruosenn suochen.li, xinya. du ) @ @ut dallaalll edu Pbarrywlss comu.edu
Abstract
To evaluate Large Language Models (LLMs) for question answering (QA), tradi- tional methods typically focus on assessing single-turn responses to given questions. However, this approach doesn't capture the dynamic nature human-Al inter- actions, where humans actively seek information through conversation Recent works in hhman-commpap computer interaction (HCI) have employed human evaluators conduct interactions and evaluations, but they are often prohibitively expensive and time-consuming to scale. We introduce an automatic evaluation framework IQA-EVAL. to achieve Interactive Question Answering Exaluations more specifi- cally, we introduce I.LM- M-M--eddedl- Evaluation Agent (LFAA) that can: (1) simulate human behaviors to generate interactions with IQA models; (2) automatically evaluate the generated interactions. Moreover, we propose assigning personas LEAs to better simulate groups of real human evaluators. We show that: (1) our evaluation framework with GPT-4 (or Claude) as backbone model achieves a high correlation with human evaluations to LEA to better represent the crowd further significantly improves correlations. Finally,
the IQA task; (2) assigning personas
automatic metric to evaluate five recent representative LLMs with over 1000 questions from complex and ambiguous question answering tasks, which comes with substantial cost of $5k if evaluated by humans.
Introduction
The advent of Large Language Models (LLMs) has significantly advanced the field of natural language processing (NLP), enabling systems to perform a wide range of tasks with remarkable proficiency [Zhao et al. 2023 Wei et al. 2022a Yang et al. 2024 Du 2024b Jing et al. 2024 Among these tasks, question answering (QA) has emerged as critical and representative goal- application. demonstrating the potential of LLMs to generate informative responses an assistant (Biancofiore jet al., 2024). Multiple methods have been proposed to enhance the faithfulness and explainability of generated information (Wei et al., 2022b Long 2023 Li and Du 2023 Du 2024a] Beyond developing these methods, rigorous evaluation of generated outputs is also crucial.
Accurate and consistent evaluation helps researchers understand existing LLM's capacities and emerging human-LLM QA interactions [Chang al. 2023 Lin and Chen 2023 Chang et 2023 Traditionally, automatic metrics such as accuracy have been used to evaluate models based on the
"Work done while the Department of Computer Science, Corell University.
Details are in Appendix
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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LLM-based Simulation
(LLMk-based Evaluation Agent (LEA)
IQA process (Export
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Helpfulness:
Helpfulness:
Helpfulness Free Text: The modal has provided
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concise and accurate answers to human questions.
results, However. they are verbose, making them less readable for humans due to excessive repelition.
Figure 1: An example of human-model interactive question answering (IQA) and automatic evaluation (IQA-EVAL... The two interactions occur with two types of personas in humans (or LLM-based evaluation agents): Expert and Clarity-seeker, and are evaluated by humans or agents with corresponding personas. The IQA model only responds to the immediately preceding prompt without further contexts like the question itself (leftmost in the Figure).
quality of their direct answers to specific questions. However, interactions between humans and LLMs grow more complex and nuanced, these traditional metrics often fail to capture the full spectrum of model's capabilities (e.g. helpfulness and fluency), particularly in interactive QA settings [Liu et al. 2016 Deriu et al. [02T]. whereas interactions are crucial for user experience and system effectiveness, yet remains overlooked in traditional evaluation paradigms. Recent works such as [Lee et al. 2023) evaluated human-LLM, LLM interactions, a process that involves human participation and annotation. Although human evaluations for these interactions provide closer approximation to real-world use cases, this approach is significantly costly and time-consuming, Recognizing the need for automatic evaluation, works like G-Eval (Liu al.) 2023b). LLM-Eval (Lin and Chen 2023). FaithScore [Jigg et al.) 2023], and PRD [Li et al.) [024] proposed to automate the assessment of non-interactive LLM responses using LLMs evaluators.
Drawing insights from (a) using LLM for automatic evaluation and (b) literature of LLM-agents research Wang et al. [024 Deshpande et al.) (2023). propose IQA-EVAL framework to auto- evaluate the performance of IQA models (i.e. LLMs) with L..M-based Evaluation Agent (LEA) to simulate and then evaluate interactions. By additionally incorporating personas, our experiments on a well-annotated dataset show that our methods align well with human judgments and provide more comprehensive evaluation of LLMs in interactive settings than traditional metrics. Finally, we benchmark recent LLMs with new complex and ambiguous questions, and demonstrate that the accuracy of answers does not always transfer to the corresponding ranking of models on their numan-model model interactions. See an overview of our system in Figure
capability of achieving good human-
Our contributions as follows:
We propose the first LLM agent-based automatic evaluation framework IQA-EVAL designed specifically to generate and then evaluate interactions in IQA. Our results demonstrate strong correlation with human evaluations.
We propose persona- based LLM evaluation agents to better assess how models adapt to different user preferences and interaction styles.
We experiment with IQA-EVAL framework to benchmark the most recent LLMs on the IQA task, demonstrating the strength and general effectiveness of our framework on fully automated evaluation.
2 Related Work
Evaluating Interactions Traditional methods for human-model dialogue evaluation have often
been centered around single-turn pairwise evaluation [Vinyals and Le 2015 Li et al.) 2016). Some

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methods with multi-turn Likert scores emerge [Venkatesh et al., 2017 Zhang et al. 2018 See al. 2019] but require time-consuming and costly collection of human- model conversations. In response, Ghandeharioun et al. (2019) suggests self-play scenario where dialog system engages in conversation with itself, employing set of proxy measures for evaluation.
Acute-eval Li ct al. 2019] and LLM-Eval [Lin and Chen 2023] have advanced multi-turn evaluation frameworks, reflecting the increasing demand for sophisticated techniques that holistically capture
human-AT interactions. Further studies in specific interaction domains like task completion [Liu
ct al. 2023a]. code generation Yang al. 2023]. and collaborative
wroblem-solving (Lee al.
2023 Huang et al. 2023; Fu et al. 2023] emphasize the need for evaluations that consider both environmental and human elements Wang et al., 2023b]. Our work differs from these methods by introducing an automated approach that emphasizes interaction quality and significantly reduces the reliance on human annotations.
LLM-based Agent for Simulation Recently, LLMs rises to demonstrate duman--ike intelligence.
as evidenced in various studies [Xiao et al. 2023 Rao et al. 2023 Li et al. 2023: Jiang et al.
2023 bench authors 2023 Brown et al., 2020; Touvron et al., 2023 Chowdhery et al. 2022 The
integration of LLMs into agents that simulate complex human behaviors and social interactions is an
area of growing research interest [Maes] 1995 Wooldridge and Jennings 1995 Xi et al.) 2023 For
example, et al. 2022] and Gao et al. [0233 employ these agents to simulate human emotions, attitudes, and behaviors in social networks, while Park al. (2023) leverages in-context learning to simulate human behaviors in sandbox world. Moreover, (Argyle et al. [2022] utilizes "algorithmic
bias" inherent in GPT-3 reflect the response patterns of different human subgroups. Horton| [2023) utilize LLMs in experimental setups of behavioral economics experiments to facilitate pilot studies. Additionally, Hämäläinen al. [2023] and Wang et al. [2023a] investigate LLM-based agents in recommender systems to simulate late and collect data on user behavior. These studies show the broad applicability and potential of --sdddded agents in simulating human behaviors and interactions across diverse applications. Our work utilizes LLM-based evaluation agents (LEAs) to fully automate interactive quality assessments, handling both interaction generation and evaluation, to enhance the evaluation of IQA models in realistic scenarios.
Personas in NLP Personas are constructed profile prompts that represent key traits of a group of users, as defined in the HCI field, reflecting their characteristics, behaviors, and goals to guide
the design of technologies that are well-suited to user needs [Cooper et al. 2014). This approach
enhances relevance and personalization in NLP applications (Nargund et al. 2022 Bamman 2015
Sheng et al. 2021 Zhong et al. 2020), offering significant potential for customizing engagement
and improving the effectiveness of conversational agents (Li et al.) 2016 Zhhng al. 2018 Chan
et al. 2019: Madotto et al. 2019 Zheng et al. 2019]. Li al. (016) introduces a persona- based neural conversation model to enhance dialogue personalization and coherence. /Zhang et al. (2018
develops personalized dialogue agents that incorporate user-specific details to enhance interaction.
In our work, persona settings enable our framework tailor interactions and assessments, aligning more closely with the specific characteristics and preferences of different user groups.
3 IQA-EVAL: Evaluating Interactive Question Answering (IQA)
In this section, we introduce our IQA-EvAL framework for automatically evaluating Interaction Question Answering Models (IQA models) with LLM-based Evaluation Agents (LEA). LEAs are used simulate humans the following two stages: (1) generating interactions with IQA models; and (2) evaluating interactions. Lastly, we discuss the use of personas to for LEAs.
3.1 Interaction Generation with LEA (Stage 1)
Inspired by peer discussions Lee et al. [023 Wang al. [024], we prompt LEAS simulate human behaviors for effective interaction generation with IQA models. The structured prompt includes three key components: (1) role deseription; (2) a task description; and (3) instructions for the discussion.
Role Description outlines the people that the LEA model will simulate during interactions. For example, the description for a standard persona could be: You are mimicking a human.
Task Deseription briefly describes the action that LEA model needs to perform in the task. For example, in a multi-choice question answering task, the prompt could be structured as follows: You
3

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are trying to choose the correct answer for the given question. Both role and task descriptions can be adjusted based on the persona, as discussed in Section 3.3
Discussion instruction guides LEAs on their subsequent steps by providing detailed descriptions facilitate progress in interactions. It comprises two essential components: (I) the actions to take; and (2) the detailed procedures to follow. For example, in a question answering task, the prompt specifies: You can ask an assistant questions for help. Please ask vub-questions each turn, please only one sub-question to
to approach answers. interact with the assistant.
In the sub-question, please include all necessary information in the original question, such as the question and all options. If you know the answer, please output "So, the answer is: A, 3, C, D"
At the start of interaction, the LEA receives system prompt that includes all three components above, along with the specific question to be addressed. As the LEA interacts with the IQA model, generates sub-questions request clarification of unknown entities, definitions, or particular aspects of the original question. Then, the IQA Model takes the questions as the input and output responses. After receiving responses, the LEA continues pose further questions until it determines the final answer. The full prompt structure and interaction details are provided in Appendix C.2
3.2 Interaction Evaluation with LEA (Stage 2)
Inspired by G-eval [Liu et al. 2023b]. which demonstrates that evaluations by GPT models align closely with human assessments in NLG tasks, we propose utilizing LEAs for interaction evaluation. LEAS assess interactions generated by LEAs and IQAs in Stage 1. The module takes task details, such questions articles, and interactions the input, and output evaluation scores. The prompt contains three parts: (1) role and task description; (2) metrics definition; and (3) evaluation instruction.
Role and task description instructs LEA to conduct evaluation. The role description acts the same as the role description in the previous stage. Moreover, briefly describes the evaluation task. The general prompt looks like: You are helpful and precise evaluator who checks the quality of the AI assistant's responses in interactions.
Metrics definitions describes the criterion that LEA needs to follow in the evaluation process. They can customized for different tasks. For the question answering task, we add the following prompt to define the "helpfulness" metrie:
Helpfuiness (5-point Likert): How helpful was having access to the AI Assistant compared to not having access?
Both the aforementioned parts can be tailored according to the persona that the LEA simulates in Stage 1, as detailed in Section 3.3
Evaluation instruction outlines the specifics of the evaluation task and the required output format. This section may appear separate part of the prompt. For example, the instruction within the prompt might be structured follows:
Please evaluate the above interactions between user and AI assistant by using the following metrics:
<Metric definitions>
Please output each of the above metrica line-by-line.
Finally, all evaluation scores for metrics calculated by averaging the results of multiple runs. The complete prompt is available in Appendix C.I and further details about our implementation can be found in Section 4
3.3 Assigning the Personas LEA
Both aforementioned evaluation stages typically use a default persona. While this constitutes some- what neutral baseline in knowledge, language proficiency, and beliefs to bo baseline, individual users often exhibit diverse personal preferences and characteristics, making one-size-fits-al evaluation less effective. Moreover, the persona distribution of the target user group significantly impacts the performance of IQA models in real-world applications. For instance, if 20% of human users prefer

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brief interactions and 70% prefer detailed information, applying general LEA to simulate this group of persons likely to result in poor correlation with downstream users.
To better simulate the diversity of the groups of people and provide individualized evaluations, assign personas to LEAs. This affects prompts of both interaction generation and interaction evaluation processes.
For example, when the LEA is assigned with the "Critical-Seekke Seekerr" interaction generation, we adapt the default role and task description (in Stage to:
persona (definition in C.3 for
You prefer interactions rich in critical information. You need help from an assistant and try to get critical information from it to answer the following questions. For interaction evaluation, the default role and task description prompt changes to The A1 Assistans should d provide strai git forward, s simple, and concise answers to aid users in deducing solution ons.. Additionally, the definition of metrics is also adjusted to align with this persona, with further details available in Appendix C.3
Meta-Evaluation of IQA-EVAL Framework
To measure how framework provides trustworthy IQA evaluations that align with human prefer- ences, we conduct meta-evaluations experiments and report correlation scores.
4.1 Experiment Settings
Dataset and Evaluation Metries We apply our evaluation method on the annotated dataset from the study by Lee et al. [003)] This dataset consists of 3641 interactions from 331 annotators. Questions in the dataset multi-choice and are derived from the MMLU dataset [Hendrycks et al. [0220 (exam- ple question in Figure I The construction of MMLU requires each worker to participate in random conversations with one of the following three IQA models: TextDavinci (text-dar
nc -011),
TextBabbage (text-brr baatttaabbaeee.. ge-001 tions, fluency and helpfulness scores annotated by annotators. The number of queries and accuracy for each IQA model can be easily deduced from annotations. In this work, We adjust the four metrics evaluate generated interactions:
001 1)), and Davinci (davi nainnnniiii -001). At the end of conversa-
Fluency (5-point Likert): How clear (or fluent) were the responses from the Al Assistant?
Helpfulness (5-point Likert): Independent of its fluency, how helpful was having access to AI Assistant compared to not having access?
Number of Queries: Counts the number of interaction turns in the conversation. This metric helps assess the efficiency the AI resolving queries within minimal number of interactions.
Accuracy: Quantifies how accurately the Al's responses match the golden answers. This is critical for evaluating the correctness of the Al's knowledge and its application in practical scenarios.
LEA Models To evaluate the effectiveness of IQA-EVAL. framework, we experiment with different LEA models on the above-mentionss three LLMSs IQA models in MMLU. For LEA that conducts both interaction generation and interaction evaluation, we use ChatGPT (GPT-3. -ttrrbo-110)),, Claude (Claude-1).
GPT4 (GPT-4-11) 111111106peeree
Evaluation of IQA-EvAL Framework
We report Pearson correlations as the measrue of agree- ment between the Human evaluations and LEA evaluations of IQA models.
4.2 Experiment Results
According to Table all models, including GPT4, GPT3. 5, and Claude, show high correlation with human evaluations. GPT4 aligns most closely with human judgments in both "Helpfulness" and "Fluency" metrics and the highest overall correlation score. This indicates that these models capable of effectively performing IQA-EVAL framework as LEA models.
In Table I ChatGPT scores closest to human judgments, particularly in the "Helpfulness" metric. Conversely, GPT4 and Claude score lower on "Helpfulness" than human evaluations, because they tend to produce inaccurate and repetitive responses that lack coherence and do not directly address
5

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Table 1: IQA-EVAL evaluation results of IQA models (TDA: TextDavinci; TB: TextBabbage; DA: Davinci). Bold numbers indicate they are the most close to human results. The empty set symbol (0) indicates the number cannot be calculated due the model's inability to follow instructions and produce a gradable answer.
Helpfulness
Fluency TB
# Queries
Accuracy
Evaluator
TDA
TB
DA
TDA
DA
TDA
TB
DA
TDA
TB
DA
Human
4.60
3.84
3.52
4.35
3.84
3.22
1.78
2.57
2.66
69.00
52.00
48.00
IQA-EVAL-GPT4
3.67
2.30
2.10
4.77
3,87
3.03
1,57
2.27
2.37
0.87
0.83
0.67
IQA-EvAL-Claude
4.13
3.03
3.00
4.47
3.47
3.23
2.20
2.67
2.07
0.67
0.53
0.57
IQA-EVAL--PT3.5
4.30
3,87
3,93
4.47
3.67
3.97
1.57
1.77
2.00
0.63
0.47
0.53
user queries, as indicated by their generated explanations. IQA-Eval scores "Fluency" are close and highly correlated to human judgments. Both scores given by humans and LEA models show that IQA models provide fluent outputs. Furthermore, according the "# Queries" metric, most models conclude conversations more quickly than humans, except for Claude, which requires more turns, potentially due to its non-OpenAl origins that it needs more turns adapt the conversational style and understand responses. Notably, GPT4 achieves the highest accuracy among all models. Moreover, we consider the impact of celf-enhancement bias and conduct more experiments. Details are Section 6.4
Table 2: Pearson Correlation (p) between IQA-EVAL evalu-
Analysis of LEA for Stage 2 (Eval- uating Interactions) For Stage it- self, we measure the LEA's capabil- ity of evaluating interactions, based real human-generated interactions from Stage
ations and human judgments.
Helpfulness
Fluency
Overall
IQA-EVAL-GPA EVAL-GPT4
0.652
0.591
0.613
IQA-EVAL-CHE IQA-EVAL-GPTS.
0.640
0.552
0,551
0.621
0.523
0.510
5.00
5.00
chatgat
chatget claude
3.75
claudel
3.75
2.50
2.50
1.25
1.25
0.00
TextDavinci TextBabbage
Davinci
0.00
TextDavinci
TextBabbage
Davinci
Helpfulness
(b) Fluency
Figure 2: Interaction evaluation results evaluated by human and two LEA models on interactions between real human and IQA Models. All scores are on a scale of 5.
Results are in shown Figures 2a and 2b helpfulness between human judgments and LEA evaluations show distinct patterns. ChatGPT demonstrates a stronger correlation with human ratings, recording a correlation score of 0.424 for fluency and correlation score of 0.306 for helpfulness. In contrast, Claude shows slightly lower correlations (0.281 for fluency and 0.287 for helpfulness). This shows that ChatGPT aligns better with human compared to Claude these specific metrics.
The Pearson correlation coefficients for fluency and
Moreover, for interactions between humans and IQA models (Figure 2 LEA evaluations moderately correlate with human evaluations. However, for interactions between LEAs and IQA models (Table 2). which the main focus our paper, LEA evaluations highly correlate (around 0.6) with human evaluations. This indicates that LEA models helpful when participating in the whole evaluation process, including both the interaction and evaluation. However, when evaluating interactions between humans and IQA models, LEAs focus differently from humans, which causes moderate correlations.
Thus, the results show that (1) both models' evaluations moderately correlate with human evaluation; (2) ChatGPT's evaluation is closer and related to human evaluation than Cl Claude's.

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4.3 Further Analysis for Free-form Feedback
Apart from the metrics above, we also prompt LLM-based Evaluation Agent (LEA) to explain the reason for generating scores in the free-form text format. We find that: 1) ChatGPT Generates more human-like, positive reviews: ChatGPT evaluations are generally more positive, frequently using terms like "helpful". "relevant", and "useful" words not always noted by workers in their annota- tions. Despite this, ChatCPT often identifies similar issues human raters, such as the provision of irrelevant and repetitive information. Overall, ChatGPT's assessments align well with human evalua- tions; Claude flags more Issues: Claude more strict and critical to IQA models in interactions. In the free-text feedback, Claude tends to highlight more issues with model responses rather than acknowledging positive aspects, especially for Davinci. For one question, after interacting with the IQA modelfassistant (ChatGPT in this case), human and two LEAS provide the following feedback:
Human: When rephrasing questiens well, the answers could be found in the A response.
ChatGPT: The Al assistant was helpful in providing relevant information, but there were issues with the accuracy.
Claude: The AI assistant's responses were not very helpful. The responses were often vague, repetitive, did not directly answer the question.
5
Effect of Assigning Persona to LLM Evaluation Agent (LEA)
5.1
Persona Definitions
As discussed in Section for diverse human alignments. We investigate assigning the following personas LEAs, which are defined based the crowdworker survey results in Lee et al. [023]]
assign personas to LEAs to simulate different groups of humans
knowledgeable; quickly learns new concepts and applies them in the reasoning process to answer questions.
Expert:
Thinkeal-Thinker: people who prefer critical information rather than redundant or detailed responses. Adaptability-Seekke people who prefer assistants can understand their questions even if they are not precise.
Clarity-Seeker: people who prefer clear explanations from assistants.
Based on the survey results on the distributions of personas of workers Lee et al. (2023) for each persona P. we split the crowdworkers into two groups: "persons with persona P". and "normal persons without specific persona P". For the first group, we initialize the role prompt in Section 3.3
and use it for the LEAs. For the second group, we utilize default prompting for LEAs. The LEA model in this section is ChatGPT(GPT-3, 5-turbo-1105).
5.2 Experimental Results
In Table we show the results of model (with personas) evaluations. To accurately simulate persona distribution, each interaction is executed multiple times, with different personas (including the standard one) assigned to LEA based on their distribution proportions. This method ensures that each persona's 's influence and characteristics are proportionally represented in the simulation, reflecting their respective prevalence within the overall distribution. The final score for persona is an average of all experiment results.
The "Expert" persona decrease LEA query counts as "Expert" already possesses relevant knowledge and only needs key explanations. The "Clarity-Seeker requires the most interaction turns among all personas for comprehension, but achieves the highest accuracy with TextBabbage and Davinci through detailed understanding of questions.
"Critic-Thinker" and Akdapability-Seekkeeee in Tables Band surpass upon the standard persona's 's human-preferenee aligment. We hypothesize that these personas are less reflected within the overall distributions of human preferences. In Table [ the varying "# Queries" across personas reveals their significant influence on LEA interaction strategies. Accuracy remains consistent after adding personas, showing performance degradation.
Together, these results indicate that assigning specific personas steer L.EAs to perform IQA-EVAL. in more fine- -nainedd and human-all auman-aligned way.
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Table 3: IQA-EVAL evaluation results of IQA models (TDA: TextDavinci; TB: TextBabbage;
DA: Davinci). LEAs, based on GPT3. 5, are assigned specific personas when representing specific groups workers.
# Queries
Accuracy TB
Evaluator
TDA
TB
TD
TDA
TD
Human
1.78
2.57
2.66
0.69
0.52
0,48
IQA-EVAL
1.57
1.77
2.00
0.63
0,47
0.53
IQA-EvAL (Expert)
1.20
1.49
2.20
0.73
0.56
0.53
IQA-EVAL (Critical-Thinker)
1.55
1.80
1.99
0.68
0.54
0.55
IQA-EVALL Addaptability-Seekee
1.50
1.75
2.10
0.66
0.52
0.55
IQA-EVAL Seerkty-Seeker)
1.64
2.10
2.34
0.63
0.57
0.57
Table 4: IQA-EVAL evaluation results (helpfulness and fluency) of IQA models. Correlations are between the LEA evaluation in each row and human evaluations.
Helpfulness
Flnency
Overall
Evaluator
TDA
TB
TD
Human
4,60
3.84
3.52
4.35
3.84
3.22
IQA-EVAL.
4,30
3.87
(-13))
0.621
4.47
3.67
3,97
0.523
0.510
IQA-EVAL (Expert)
4,17 (a0us)
3.08 (200))
(211
0.756
4.17 (20.2)
3,84 (20.4)
32004)
0.787
0.670
IQA-EVAL (Criical-Thinkeer)
4.44
4,02
4.08
0.711
4.64 (10.6)
3,97
4.10 (.00c)000))
0.624
0.634
IQA-EVAL (Adiptahllit-Sesaa
4,24 (5005)
3.67 (2011))
3.75 (0.1))
0.713
4.52 (20,0))
3.84 (20,0))
3.84 (20.09)
0.637
0.650
IQA-EVAL (Clarity Seeker)
4.45 (10,0))
3.77 (10
(.80
0.747
4.60 (1004)
3,85 (10.4)
3.94 (10.6)
0.676
0.690
It is worth nothing that our analysis of persona reassignment shows IQA-EVAL is sensitive to
incorrect assignments (see Appendix [B). Moreover, further analyses about bias evaluation, as well as measuring complementary metrics like offensiveness, are in Appendix G
6
Benchmarking LLMs with IQA-EvAL on more Types of Questions
6.1
Datasets
To evaluate the robustness and generalizability of our evaluation framework, conduct bench- marking across different models on two distinct question answering datasets, each offering unique challenges and complexities requiring advanced reasoning. AmbigQA Min al. [2020] is col- lection 14,042 annotated questions sourced from the NQ-OPEN benchmarks K Kwiatkowski et al. [2099]] open-domain QA dataset. It focuses questions with inherent ambiguities, reflecting the complexity encountered real-world queries. These ambiguities often involve diverse aspects such as events, entity references, and answer types, resulting in multiple plausible answers for each question. HotpotQA Yang et al.] [2018) comprises 1 113,000 question-answer pairs sourced from Wikipedia, which require multi-hop reasoning spanning multiple documents. It contains rich array of intricate questions that demand the synthesis of information from various texts to determine accurate answers. In this benchmark, we select 500 questions from each dataset form dataset containing 1,000 complex multi-hop and ambiguous questions.
6.2 LLMs to Benchmark
Table 5: IQA-EvAL benchmarking results on HotpotQA and AmbigQA datasets.
HotpotQA Fluency?
AmbigQA Fluency
IQA Models
Helpfulness
Queries,
Accuracy?
Helpfulness
Queries
Accuracy
TextDavinci
4.72
4.87
1.22
0.45
TextBabbage
4.70
4.88
0.37
Davinci
4.27
4.52
L.68
0.32
CPT3.5
4.72
4.95
1.49
0.63
4.91
4.97
1.89
0.60
CPT4
4,78
4.96
1.12
0.66
4.89
4.95
1.06
0.72
Claude
4.82
4.99
1.26
0.58
4.89
4.94
1.36
0.62
Llana2
4.70
4.95
1.32
1.79
0.52
Zephyr 4,64
4.88
1.01
0.40
4.38
4.66
1.03
0.45
Apart from TextDavinci, TextBabbage and Davinci we benchmark more LLMss: GPT3.5, GPT4, Claude, Llama2 and Zephyr. The checkpoints for Llama2 and Zephyr are llama-2-77 and Zephyr- alpha, respectively. GPT3. 5 is used as LEA in experiments.

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Table Comparison between new prompts and our prompts used in Table more complex and include effective debiasing instructions.
The new prompts are
Helpfulness
Fluency TB
Accuracy
LEA models
TDA
TB
DA
TDA
DA
TDA
TB
DA
Human
4.60
3.84
3.52
4.35
3.84
3.22
0.69
0.52
0.48
IQA-EVAL-GPT4 (Our Prompts)
3.67
2.30
2.10
4.77
3.87
3.03
0.87
0.83
0.67
IQA-EVAL-GPT4 (New Prompts)
3.50
2.23
2.10
4.40
4.07
3.53
0.87
0.83
0.67
Table 7: Comparison between new prompts and our prompts used in Table 5 on benchmarking LLMs
with IQA-EVAL.
LEA models
Helpfulness
Fluency
Queries
Accuracy
BRA-EVAL-GPT3. (Our Prompts)
4.72
4.95
1.49 1.35
0.63
IQA-EVAL-GPT335 (New Prompts)
4.68
4.91
0.60
6.3 Benchmarking Results
The IQA evaluation benchmarks are presented in Table We divide IQA Models into two categories: weak IQA Models (TextDavinci, TextBabbage, and Davinci) and strong IQA Models (GPT3. GPT4 Claude, Llama2, and Zephyr). Weak IQA Models can assist the LEA with answering HotpotQA questions, but due to their knowledge limitations, they cannot help much with AmbigQA questions. Zephyr achieves the lowest performance compared to other strong IQA Models. On the HotpotQA dataset, Zephyr's ' accuracy performance is only comparable to the strongest one among weak IQA Models, TextDavinci.
Most "Helpfulness" and "Fluency" scores are high (exceeding out 5), especially for strong IQA Models like GPT4. For the "# of queries", it is uncommon for interactions to extend beyond two turns. As on HotpotQA, most interactions conclude at the beginning the second turn, as IQA models have effectively guided users to reach the answers. For AmbigQA, some conversations last longer whereas LEA spends additional turns on clarifying ambiguous entities before approaching the final answer. Additional benchmarking results on the Natural Question dataset are in Appendix D
6.4
Seff-Enhancement Bias
LLMs are shown to demonstrate self-favouring behaviours Paanickssery et al. 2024], and no verified or accessible mitigations to this issue exist the best of our knowledge. This issue particularly concerning when the LEA models evaluating the IQA models share the same underlying model. In this section, we discuss our two of our attempts assess the effects of this bias.
Following Zheng al. 2023 and Furniturewala et
2024 we included some empirically useful
debiasing instructions as tollows:
Please act please be objective and do not include any bias or your preference.
impartial and unbiased judge. In your evaluation,
In Table scores on the row of GPT3. is vulnerable to self-enhancement bias. However, with the above debiasing prompt, in Table 7 shows that the results of new prompts are highly similar the original prompts. Similarly, Table 6 shows that the results of modified and original prompts are differ only lightly.
We also designed second methodology to mitigate self-enhancement bias. In this experiment, multiple LEA models evaluate the performance of IQA models during each interaction ("Multi- perspective"). In other words, we introduced third-party evaluations, where various LEA models assess the IQA models' performance instead of relying solely the LEA model itself involved the interaction. After evaluation, we use the average score from all LEA models as the final score. The results of DDA--Eyl-Muulii-erssppe MrA-EEaal-Mulii-pesppctiii and human evaluations are in Table
look as in Table 8 The correlations between
We believe that that the self ceff-preference bias has limited impact IQA-Eval.

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Table IQA-EVAL-Muli- Perspei-er Results of IQA Models. MP indicates Multi-Perspective...
Bold numbers indicate they are the closest to human results.
Helpfulness
Fluency TB
# Queries
Accuracy
LEA models
TDA
TB
DA
TDA
DA
TDA
TB
DA
TDA
TB
DA
Human
4.60
3.84
3.52
4.35
3.84
3.22
1.78
2.57
2.66
0.69
0.52
0.48
IQA-EVAL-GGTI-ME
4.32
3,70
3,53
4.57
3.74
3.68
1.57
2.27
2.37
0.87
0.83
0.67
3.96
3.13
3.10
4.29
3.51
3.22
2.20
2.67
2.07
0.67
0,53
0.57
IQA-EVAL-GPT3.5-MP
3.98
3.23
3.04
4.41
3.67
3.59
1.57
1.77
2.00
0.63
0.47
0.53
Table 9: Pearson Correlation (p) between IQA-EVAL- Perseeeiteeeeeeivee evaluations and human
judgments.
LEA models
Helpfulness
Fluency
Overall
IQA-EVAL-GPTAN
0.702
0.601
0.624
ADA-EVAL-Claude
0.663
0.613
0.602
IQA-E
0.641
0.552
0.533
6.5 Analysis
Stronger IQA models require fewer turns in interac- tions. On the more challenging AmbigQA, the stronger model, GPT4, typically requires only one turn to assist LEA in solving questions with high accuracy. In contrast, less capable models like Llama2 and GPT3. 5 need more turns clarify ambiguous entities and have lower QA accuracies. A similar trend is observed on the HotporQA.
Table 10: Accuracy of IQA Models (recent LLMs) on two datasets (Non- interactive setting).
IQA Models
HotpotQA
AmbigQA
GPT3.5
0.43
0,62
GPT4
0.46
0.63
Claude
0.28
0..5
We obtain a similar model ranking with much lower cost. Compared to Chatbot Arena our accuracy-based ranking of IQA Models follows similar trend: GPT4 Claude GPT3.5 Llama2 Zephyr. In addition, our evaluation method, IQA EVAL, is fully automated. Our method makes it a cost-effective alternative for large-scale evaluations.
Llama2 Zephyr
0.24
0.29
0.25
0.31
Evaluation of interaction performance does not always match Non-Interaction performance. In interaction evaluations, accuracy on final results is not the only metric to show IQA Models' performance. The quality of intermediate responses is a significant aspect. On both "helpfulness" and "fluency" metrics, Claude always the best IQA Model on HotpotQA questions, while on AmbigQA, Llama2 and GPT3. 5 outperform GPT4. IQA Model rankings on these two aspects differ from those Chatbot Arena (non-interaction.
The performance of IQA Models largely affects the final performance. The accuracies in both Table and Table 10 show a consistent trend. The Pearson correlations of the accuracy between the tables are 0.77 and 0.87 on both datasets, respectively. A strong IQA model, such as GPT4, can lead the LEAA to finish tasks and largely improve the LEA's performance on those tasks. Weak assistants may drag down the LEA's performance, such as the performance of the LEA on both datasets decreases after interacting with Zephyr.
Conclusion
To conclude, we introduced IQA-EVAL, novel approach for evaluating interactive question- answering systems using large language models. Our methodology achieves automatic interaction generation and evaluation with LEA. and enhances the evaluation process by assigning personas to LEA for better matching diverse groups of people. We show that our approach aligns closely with real human interactions and judgment, indicating that scalable, automatic IQA-EVAL process be achieved. We providing insights on recent LLM's capability in conducting IQA with IQA-EVAL which would cost $5,000 for human evaluations.
/ http:///wwww..ppoooo.o//e//ee/aa
10

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Acknowledgement
We thank the anonymous reviewers for valuable and insightful feedback. This research is supported in part by the National Science Foundation CAREER Grant IIS ISS-234,,3,, Amazon Research Award and Cisco Research Award. Any opinions, findings, and conclusions or recommendations expressed herein are those of the authors and do not necessarily represent the views, either expressed or implied, of the U.S. Government.
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Zhong, C. Zhang, H. Wang, Y. Liu, and C. Miao. Towards persona- persona-based empathetic conversational models. arXiv preprint arXiv:2004.12316. 2020.
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A Limitations
In conducting this study, certain limitations have influenced our scope and findings. First and foremost, LLMs are shown to demonstrate self-favouring behaviours [Panickssery et al. 2024], and verified or accessible mitigations to this issue exist to the best of our knowledge. We discuss some of our attempts address this in Section 6.4 but this limitation necessitates future research.
Our methodology was applied exclusively to multi-choice question-answerte tasks due to constraints imposed by the datasets used. Moreover, we do not investigate how allowing prompt editing could affect the results. This choice limits the generalizability our findings across the wider array of question answering formats that exist in both academic research and practical applications.
We advocate for subsequent research efforts extend the application of our proposed evaluation strategies to more diverse set of question answering tasks, beyond the multi-choice format.
Furthermore, there is significant opportunity test these methods with broader spectrum of LLMs, including those at the cutting edge of the field. Such expansions would not only validate the versatility and robustness of approaches but also potentially uncover additional insights into the nuances of LLM interaction and performance in varied contexts.
B
Ethics Statement
In our study, we meticulously crafted each persona mitigate bias, ensuring they do not adversely impact the IQA-Eval process, evidenced by the results presented in the table above. However, our personas represent only a limited range. There exists the potential for negative effects from other personas that may be inadequately designed or deliberately biased to achieve specific outcomes. Thus, we oppose irresponsible persona designs that result biased evaluation results. The principle of persona definition and design should be thoroughly studied in future works. We hope our work will help facilitate future research into better automatic interaction evaluations aligning with crowds.
C
Prompts
Both C.I and C adapted from Lee al. 2023 All the following prompts from the view of
LEA in intractions.
C.1 Interaction Evaluation Prompt
We follow the setting in the data and evaluate conversations worker-wise. other words, we combine conversations for each worker and send all of them to evaluators. The prompt send to APIs follows
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the following format:
You are a helpful and precise assistant for checking the quality of the AI assistant's responses in interactions.
(Question 1}
(Golden Answer 1)
[Conversation 1}
(User Answer 1}
(Question n)
(Golden Answer n)
(Conversation n)
(User Answer n)
Please evaluate the above conversations between user and AI assistant by using the following metrics:
Fluency (5-point Likert) How clear (or fluent) were the responses
from the AI Assistant?
Helpfulness (5-point Likert): Independent of its fluency, how helpful was having access to the AT Assistant compared to not having access? Helpfulness (free-form): Why did you find the AI Assistant helpful unhelpful?
Please output each of the above metrics line-by-line.
C.2 Interaction Generation Prompt
Since this is multi-choice question answering task, the full prompt for models is as follows:
You are mimicking a human.
You are trying choose the correct answer the given question. Please ask an assistant sub-questions for help approaching answers. In each turn, please only ask one sub-question to interact with assistant. In the sub-questions, please include all necessary information, such as the question and options, in the original question. If you know the answer, please output "So, the answer is: A, B, C, D."
(OA Question and choices)
(User Model's query: (question 11}
(Assistant's answer:
[answer 1]}
(User Model's query: (question 2]}
(Assistant's answer:
[answer 2]}
(User Model's query: [question n))
(Assistant's answer: [answer n]}
(User Model's final answer)

---

If the current turn reaches the maximum number we set, the system prompt before "[ "[Question]" looks follows:
Please choose the correct answer to the given question. Please output "So, the answer is: A, B, C, or D."
C.2.1 QA Question and choices format
The question prompt for multi-choice questions in MMLU is as follows:
<question>
A. <option A>
B. coption B>
coption C>
coption D>
For HotpotQA and AmbigQA datasets, the question prompt only contains question, such <question>
C.3 Persona Prompts
We design distinct prompts for each persona. Both prompts meta-evaluation and model interaction modules change with personas.
In model interaction prompts, we only modify the first sentence based on personas. See all persona prompts in Table [I
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Table 11: Persona Interaction and Evaluation Descriptions
Persona Expert
Persona Interaction Description
Persona Evaluation Description
You are mimi mimicking a knowl mnwwledgeable human who can quickly understand new concepts. You need help from an assistant to learn and answer questions.
The AT Assistant helps knowledgeable human to answer question.
The assistant should provide straightforward, informative, and in-depth answers to human questions.
Critical-Thinker
You are mimicking human who prefers interactions rich in critical information. You need help from assistant and try to get critical information from it to answer the following questions.
The AT Assistant should provide clear, non-vague, and precise information or options and help user deduce answers. (Detailed evaluation criteria were indicated but not fully transcribed due to length.)
Adaptability-Sedd
You are mimicking a human who prefers an adaptable assistant who can always understand his questions. You need help from an assistant to answer questions.
The AI Assistant helps a human who prefers an adaptable assistant. The assistant should understand user's
questions, provide related options, and help user deduce answers.
Clarity-Secker
You mimi cking human who prefers clear information in conversations. You need help from an assistant and want to get clear information from it to answer questions.
The AT Assistant helps a human who prefers clear information in conversations. The AI should provide non-vague, precise information to help user deduce answers.
D Additional Experiments on Natural Questions
We benchmark IQA models in another dataset called Natural Questions (wwiakkossii al. (001b) )
This dataset comprises authentic questions posed by users about Wikipedia articles, demanding true multi-turn dialogues for resolution, akin to the setup in multi-turn conversational QA dataset QuAC (Choi et al. (2018). ). The experiment results are as in Tables 12 and [3 All numbers of queries in the two tables are around 3, and each response a query from IQA models contains an average of sentences.
Given the number of sentences in each IQA model's response in Table 14 the non-interactive outputs are roughly equivalent to about two interaction turns, less than three turns in interactive outputs. Thus, The interaction process of IQA-EvAL involves not only reasoning processes but also simulating genuine interactive multi-turn conversations. This suggests that the performances shown in tables [2 by multi-turn interactions than by reasoning processes. Furthermore,
and 13 above driven these interactions lead to enhanced accuracy, as demonstrated by the superior results in the first two tables compared those in the last table (non-interactivv).
E Sensitive to Persona Distributions
We conduct two new experiments study the effects of changing persona assignments. Results indicate that IQA-EvAL is sensitive to incorrect persona assignments. When the persona distribution
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Table 12: IQA-Eval benchmarking results on the Natural Questions by Claude-3
IQA models
Helpfulness
Fluency
# Queries
Accuracy
GPT3.5 4.86
4.88
2.82
0.42
Claude
4.88
4.90
3.02
0.38
Llama2
4.90
4.84
3.18
0.34
Zephyr
4.84
4.90
3,02
0.28
Table 13: IQA-Eval benchmarking results on the Natural Question by GPT-4
IQA models
Helpfulness
Fluency
# Queries
Accuracy
GPT3.5
4.12
5.00
2.76
0.44
Claude
4.02
5.00
2.76
0.40
Llama2
3.20
4.84
3.08
0.32
Zephyr
3.30
4.86
2.92
0.36
Table 14: Average number of sentences and accuracy scores of IQA Models (non-interactive setting)
IQA models
# Sentences
Accuracy
GPT3.5
4.66
0.38
Claude
3.16
0.34
Llama2
5.21
0.30
Zephyr
4.68
0.24
Table 15: IQA-EVAL results under different persona distribution on the expert persona.
Helpfulness TB
Fluency
LEA models
TDA
DA
TDA
TB
DA
Human
4.60
3.84
3.52
4.35
3.84
3.22
IQA-EVAL (Expert)
4.17
3.08
3.12
0.756
4.47
3.84
3.40
0.787
IQA-EVAL (20% Expert)
4.31
3.26
3.44
0.708
4.62
4.09
3.65
0.741
IQA-EVAL (40% Expert)
4.21
3.14
3.23
0.751
4.49
3.88
3.44
0.779
IQA-EVAL (60% Expert)
4.11
3.01
3.00
0.725
4.43
3.77
3.34
0.734
IQA-EVAL (80% Expert)
4.02
2.90
2.79
0.680
4.30
3.56
3.12
0.703
Human (Pure Expert)
4.69
4.00
3.73
4.36
3.96
3.26
IQA-EVAL (Pure Expert)
4.37
3.57
3.33
0.778
4.20
3.40
2.97
0.786
incorrect (such 20% Expert in Table 15 the performance of IQA-EVAL shows a lower correlation with human evaluations.
Moreover, the last two lines in Table 15 describe the correlation between human evaluations and IQA-EVAL within a sub-group only containing pure experts. The correlation results in line "IQA- EVAL (Pure Expert)" represent that (1) our personas accurately represent the pure expert group, as its correlation with the line "Human (Pure Expert)" remains nearly consistent with those in line "IQA-EvAL (Expert)" and (2) given this completely correct persona distribution, our IQA-EVAL correlates well with human evaluations.
F
Accurate QA models are preferred by humans in IQA-EVAL
The quote from our cited paper Lee et al. 2023 is "[...] perception of helpfulness not necessarily reflected in the overall interaction accuracy." It describes the conclusion of multiple tasks in that paper (e.g. text summarization, social dialogue, QA). However, the QA settings, Table Lee et al. (2023) shows that humans prefer accurate models on the QA task.
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Table 16: Evaluation results of interactions between LEA and IQA models.
Helpfulness Claude -instant
Huuency
Accuracy
GPT
Llama2
Zopbyr -Alpha
GPT
Claude
Llama2
Zepbyr -Alpha
GPT
Claude
Llama2
Zephyr
LEA models
3.5
-Alphu
IQA-EVAL-GPT+
4.60
4.60
3.83
4.27
4.97
5.00
4.87
4,93
0.93
0.93
0.83
0,93
IQA-EVAL-Chane
5.00
4.97
5.00
4.87
0.73
0.57
Table 17: Evaluation results of non-interactions (direct answers) between LEA and IQA models.
Helpfulness
Fluency
Accuracy
GPT
Claude instant
Llama2
Zephyr
GPT
Claude
Llama2
Zephyr Alpha
GPT 3.5
Claude iinsant
Lluma2 -8b
Zepbyz
LEA models
8h
Alpha
3.5
-instant
-Aihh
EVAL-GGPP
IQA-EVAL-Claud
4.97
5.00
4.53
4.87
4.97
4.97
4.47
4,97
0.83
0.80
0.47
0.57
We also conducted experiments (1) using LEA to evaluate interactions between LEAs and IQA models (interactive) and (2) using LEA to evaluate direct answers generated by IQA models (non-interactive). Our experiments in Tables 66] and 17 show that L.EA models prefer accurate models, which aligns well with the conclusion from human annotations.
G Bias Evaluation
We follow the method proposed by [hheng al. [021] and conduct new experiment evaluate the offensiveness and harmfulness of our personas using the Real RealToxicityyPonge
dataset (Gehman LEA models. The results are in the table 18 The values in the table above represent the success rates (higher is better) for each bias metric, persona, and LEA model (CPT3. and Claude). Scores labeled "None" are consistently lower than those for all personas, indicating that our personas do not increase offensiveness or harmfulness in conversations.
al. 2020)
Table 18: Evaluating persona biases on offensiveness and harmful metrics. high score indicates better results.
Offensiveness
Harmful
Persona
IQA-EVAL-GPT3.5 IQA-EVAL-Claude
IQA-EVAL-GPT35 IQA-EVAL-Claude
None
91.7
62.5
72.5
Expert
95.5
97.3
67.8
75.5
Critical-Thinker
93.3
95.5
65.4
73.7
Adaptability-Seen Seeker
94.5
95.5
62.8
74.6
Clarity-Seeker
95.0
94.0
62.5
73.0
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Reviews:
[{"review_id": "CqJk90Src3", "reviewer": "Reviewer_RAi2", "summary": "This research addresses the evaluation methodology for multi-turn conversation using Large Language Models (LLMs), a topic of active research recently. The study proposes an evaluation framework called IQA-Eval, which consists of the target model (IQA model) and an agent (LEA model) that engages in conversation with the IQA model and evaluates each turn. The primary challenge in multi-turn evaluation is to 1) interactively converse with the IQA model and 2) evaluate each generated turn in a cost and time-efficient manner. This research aims to overcome this challenge by leveraging LLMs that consider personas to facilitate both conversation and evaluation.\nThrough various experiments, the study confirms that applying LLMs for conversation and evaluation results in a high correlation with Human Evaluation. Additionally, it includes an analysis of the differences in the number of turns due to the varying capabilities of each IQA model.", "questions": "- How does the Free Form Feedback in this study differ from the explanations provided by existing LLM-based evaluation methods (such as LLM-Eval and G-Eval)?\n- What is the total number of repetitions for the experiments mentioned in section 5.2? Could you provide the variance values for each experiment?\n- Why are the results for AmbigQA not included? Even if the performance of the IQA models is not optimal, the results should still be presented.", "limitations": "- The limitations are stated in the Appendix, clearly outlining the limitations of the experiments.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 1, "contribution": 2, "strengths": "1.Compared to existing LLM multi-turn evaluations, this approach enables faster and more economical assessments.\n2.Examining the human correlation suggests that the evaluation is reliable. It is very promising in that it allows for automatic evaluation of interactive multi-turn conversation capabilities that resemble real-world scenarios.\n3.The evaluation framework is very simple, making it adaptable for assessing various multi-turn situations.", "weaknesses": "- The evaluation dataset consists entirely of multiple-choice questions, making it unsuitable for generating and evaluating multi-turn conversations. This is evident in Tables 3 and 5, where the average length of conversation turns is less than 2. Such a situation suggests that the process might be more about reasoning rather than simulating genuine interactive multi-turn conversations. It is necessary to analyze whether the performance shown in Table 6 is due to multi-turn interactions or the influence of the reasoning path. Alternatively, an evaluation using datasets that assume genuine multi-turn dialogue scenarios is needed.\n- The verification process for personas is lacking. There is no substantial evidence to consider that the performance differences by persona shown in Table 4 are reflective of the actual characteristics of those personas.\n- A self-preference issue appears to have arisen in Table 5, and there is no control in place to address this.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful review. We would like to address your concerns as follows:\n\n# Weaknesses:\n\n>1. The evaluation dataset consists entirely of multiple-choice questions, making it unsuitable for generating and evaluating multi-turn conversations. This is evident in Tables 3 and 5, where the average length of conversation turns is less than 2. Such a situation suggests that the process might be more about reasoning rather than simulating genuine interactive multi-turn conversations. It is necessary to analyze whether the performance shown in Table 6 is due to multi-turn interactions or the influence of the reasoning path. Alternatively, an evaluation using datasets that assume genuine multi-turn dialogue scenarios is needed.\n\nWe focus on task-driven dialogues. In the IQA setting, humans interact with assistant models to answer questions. This is a type of multi-turn interaction. In our work, the number of turns of interactions between human and IQA models is similar to those between LEA and IQA models. We leave other forms of dialogues, potentially less well-defined and also lacking annotated public datasets, for future research.\n\nTable 6 accuracy results come from a non-interactive, Chain-of-thought enabled setting, and as a result, this setting already allowed the reasoning path to be fully fledged by the non-interactive model itself. Table 5 accuracy results are from LEA models after interacting with IQA models. Since both CoT outputs and interactions include reasoning processes, the difference between Table 5 and Table 6 naturally arises from the LEA-IQA models’ interaction process, suggesting that multi-turn interactions help LEA models reach higher accuracy scores. \n\n>2. The verification process for personas is lacking. There is no substantial evidence to consider that the performance differences by persona shown in Table 4 are reflective of the actual characteristics of those personas.\n\nWe conduct a new experiment that shows the standard deviation in Table 4. The new table is in reply to question 2. Standard deviations show that personas affect evaluation performances.\n\nAdditionally, we conduct an experiment demonstrating that personas impact LEA's performance. Using the \"Expert\" persona, we calculated the correlation between human evaluations and IQA-EVAL within a sub-group of pure experts. The results indicate that our personas accurately represent the pure expert group, as the correlation remains nearly consistent. In other words, if there is a dramatic change in correlation within the sub-group, it suggests that the proposed persona fails to represent the current group or the broader crowd.\n\n| 5-point Likert         | Helpfulness  |               |              |       | Fluency      |              |              |       |\n| ---------------------- | ------------ | ------------- | ------------ | ----- | ------------ | ------------ | ------------ | ----- |\n| LEA models             | TDA          | TB            | DA           | ρ     | TDA          | TB           | DA           | ρ     |\n| Human (Pure Expert)    | 4.69         | 4.00          | 3.73         |       | 4.36         | 3.96         | 3.26         |       |\n| IQA-EVAL (Pure Expert) | 4.37 | 3.57  | 3.33 | 0.778 | 4.20 | 3.40 | 2.97 | 0.786 |\n| Human                  | 4.60         | 3.84          | 3.52         |       | 4.35         | 3.84         | 3.22         |       |\n| IQA-EVAL (Expert)      | 4.17         | 3.08          | 3.12         | 0.756 | 4.47         | 3.84         | 3.40         | 0.787 |\nBased on the results, personas affect the performance of large language models.\n\n>3. A self-preference issue appears to have arisen in Table 5, and there is no control in place to address this.\n\nPlease refer to the answer in the general author response on the top of the page.\n\n# Questions:\n\n>1. How does the Free Form Feedback in this study differ from the explanations provided by existing LLM-based evaluation methods (such as LLM-Eval and G-Eval)?\n    \n\n  \n\nThe main difference is that the free-form feedback In this work evaluates interactions between LEA and IQA models, especially the performance of IQA models, while previous LLM-based automatic evaluation methods, like G-Eval, only evaluate direct outputs from Large Language Models (LLMs), which are non-interactive. Moreover, most related papers only provide numeric analysis, while we manually analyze feedback texts and correlate them to other results.\n\n>2. What is the total number of repetitions for the experiments mentioned in section 5.2? Could you provide the variance values for each experiment?\n\nWe run the experiment in section 5.2 five times. The standard deviations are in the table below.\n\n|   |   |   |   |   |   |   |\n|---|---|---|---|---|---|---|\n|5-point Likert|Helpfulness|   |   |Fluency|   |   |\n|LEA models|TDA|TB|DA|TDA|TB|DA|\n|Human|4.60|3.84|3.52|4.35|3.84|3.22|\n|IQA-EVAL|4.30 (±0.06)|3.87  (±0.11)|3.93 (±0.13)|4.47 (±0.05)|3.67 (±0.08)|3.97 (±0.06)|\n|IQA-EVAL (Expert)|4.17 (±0.08)|3.08 (±0.09)|3.12 (±0.11)|4.47 (±0.02)|3.84 (±0.04)|3.40 (±0.04)|\n|IQA-EVAL (Critical-Thinker)|4.44  (±0.08)|4.02 (±0.13)|4.08 (±0.17)|4.64 (±0.06)|3.97 (±0.08)|4.10 (±0.08)|\n|IQA-EVAL (Adaptability-Seeker)|4.24 (±0.05)|3.67 (±0.11)|3.75 (±0.11)|4.52 (±0.08)|3.84 (±0.07)|3.884 (±0.09)|\n|IQA-EVAL (Clarity-Seeker)|4.45 (±0.07)|3.77 (±0.15)|3.80 (±0.12)|4.60 (±0.04)|3.85 (±0.04)|3.94 (±0.06)|\n\n  \n\n>3. Why are the results for AmbigQA not included? Even if the performance of the IQA models is not optimal, the results should still be presented. \n\nThe main reason for “-” in IQA-EVAL benchmarking (Table 5) is that weak IQA models cannot assist LEA models in answering questions at all. In most turns of interactions, these weak IQA models, for the task of outputing a disambiguated sentence, only repeat meaningless sentence pieces or questions proposed by LEAs. Thus, We use “-” instead of “0” in Table 5 to mark this inability of these models to complete the task.", "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": "u6tTOo7FE0", "reviewer": "Reviewer_JewS", "summary": "The authors introduce a novel method to simulate a human conversation when evaluated on an interactive question answering (IQA) and evaluate the simulated interaction according to some predefined metrics.", "questions": "See Weaknesses", "limitations": "The authors addressed the limitations.", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The presentation of the paper is clear and easy to follow\n- The paper is well-motivated. It recognizes how costly and time-consuming it is to use human evaluations\n- The idea is reasonable", "weaknesses": "When using LLMs to evaluate the outputs from LLMs, recent research has shown LLMs to be biased in preferring their own generations compared to generations from other models, even if their generation wasn’t better [0]. This may bias the IQA-EVAL metrics if the same model is used for both IQA-EVAL and the IQA model.\n\nThe authors provided limited evidence for their claims in section 4.3. Could the authors provide a more robust justification of why “ChatGPT’s assessments align well with human evaluations”, and other claims made in this section?\n\nThe authors did not recognise that this method could cause negative societal impacts, instead stating “Evaluation works bear little risks for negative scoeital impacts”. LLMs have been repeatedly shown to exhibit strong biases as a result of their training procedure. Using LLMs for evaluation opens the risk of reinforcing these biases, whether within training of future models, or benchmark answers, or whatever is downstream of this method.\n\n[0] https://arxiv.org/abs/2404.13076", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your helpful review. We would like to address the mentioned weakness and questions below.\n\n\n----\n \n> **When using LLMs to evaluate the outputs from LLMs, recent research has shown LLMs to be biased in preferring their own generations compared to generations from other models, even if their generation wasn’t better. This may bias the IQA-EVAL metrics if the same model is used for both IQA-EVAL and the IQA model.**\n\nPlease refer to the answer in the general author response on the top of the page.\n\n  \n  \n----\n> **The authors provided limited evidence for their claims in section 4.3. Could the authors provide a more robust justification of why “ChatGPT’s assessments align well with human evaluations”, and other claims made in this section?**\n\nThe content in section 4.3 contains our findings after reading LEA free-form outputs about “helpfulness”. The ChatGPT in section 4.3 only indicates GPT-3.5.\n\nFor the claim ``ChatGPT’s assessments align well with human evaluations’’. In Table 1, GPT-3.5 always gives high sores, indicating it is more positive generally. Its scores are the closest to human scores in Table 1, and its correlation is also high in Table 2. Thus, we claim GPT-3.5 assessments align well with human evaluations.\n\n\nFor the other claim, compared to GPT-3.5, Claude assigns lower scores. These results align with the claim that Claude flags more issues.\n\n\n----\n> **The authors did not recognise that this method could cause negative societal impacts, instead stating “Evaluation works bear little risks for negative societal impacts”. LLMs have been repeatedly shown to exhibit strong biases as a result of their training procedure. Using LLMs for evaluation opens the risk of reinforcing these biases, whether within training of future models, or benchmark answers, or whatever is downstream of this method.**\n    \n\n\nWe apologize for the previous statement that “Evaluation works bear little risks for negative societal impacts”; this claim is inaccurate. Large Language Models (LLMs) have been repeatedly shown to exhibit strong biases due to their training procedures, which can result in significant societal impacts. These biases can perpetuate stereotypes, disadvantage certain groups, and influence downstream applications in harmful ways.\n\n\nTo partially  address these concerns, we have implemented several measures to reduce bias in our evaluation process. For instance, we have fine-tuned prompts to minimize self-enhancement bias. Our experiments on the MMLU dataset demonstrate that bias does not significantly affect our evaluation results. Specifically, LEAs mainly evaluate the performance of IQA models in conversations. In IQA-Eval, each LEA model evaluates all IQA models, and since LEA models and IQA models are distinct, the impact of bias, such as LLMs favoring their own outputs, is mitigated in the evaluation results presented in sections 4 and 5.\n\n  \n  \n----\n## References:\n\n1. Panickssery, Arjun, Samuel R. Bowman, and Shi Feng. \"Llm evaluators recognize and favor their own generations.\" arXiv preprint arXiv:2404.13076 (2024).\n    \n2. Zheng, Lianmin, et al. \"Judging llm-as-a-judge with mt-bench and chatbot arena.\" Advances in Neural Information Processing Systems 36 (2024).\n    \n3. Furniturewala, Shaz, et al. \"Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models.\" arXiv preprint arXiv:2405.10431 (2024).", "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": "zH8xpuGoAq", "reviewer": "Reviewer_unmC", "summary": "This paper introduces IQA-EVAL, a framework for automatically evaluating interactive question-answering (IQA) systems using large language models. The authors propose using LLM-based Evaluation Agents (LEAs) to simulate human behavior in both generating interactions with IQA models and evaluating those interactions. The framework also incorporates persona assignments to LEAs to better represent diverse user groups. The authors demonstrate that IQA-EVAL achieves high correlation with human judgments and use it to benchmark several recent LLMs on complex question-answering tasks.", "questions": "- Line 207~213: can you provide more explanations into why ChatGPT sometimes outperformed GPT-4 in your experiments? This seems counterintuitive given GPT-4's generally stronger capabilities.\n- Have you considered incorporating more diverse interaction patterns, such as clarification questions, ambiguous queries, or follow-up questions? How might this affect the evaluation results?\n- Have you explored how different persona distributions might impact the evaluation results? How sensitive is the framework to changes in persona assignments?", "limitations": "The authors have made some effort to address limitations, such as acknowledging potential biases in LLM-based evaluation and discussing the impact of persona assignments. However, the paper would benefit from discussing the limited diversity of interaction patterns considered.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Proposes a fully automated framework for evaluating interactive QA systems, addressing the need for more efficient evaluation methods\n- Incorporates persona assignments to LEAs, allowing for more nuanced and diverse simulations of user interactions\n- Demonstrates strong correlation with human judgments", "weaknesses": "- Limited novelty, as using LLMs as judges is quite common in evaluation tasks. This paper mainly focuses on a relatively new setting - the interactive QA setting.\n- The considered interaction scenarios could be more diverse to better reflect real-world user behaviors", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your helpful review. We would like to address the mentioned weakness and questions below. \n\n----\n# Weaknesses:\n\n> **Limited novelty, as using LLMs as judges is quite common in evaluation tasks. This paper mainly focuses on a relatively new setting - the interactive QA setting.**\n\nPrevious works, like G-Eval, implement automatic evaluation for direct outputs from Large Language Models (LLMs) which is non-interactive. However, chat interactions involving **multiple turns are more prevalent in real-world applications**, as recognized by the HCI community [1]. Currently, **no method is available to automatically evaluate the performance of models in such interactive settings**. Our approach, for the first time, addresses this gap by using LLMs to automatically assess the performance of assistant models during interactive scenarios. \n\nAdditionally, **for the first time, we augment LLM-agent with personas** in our IQA-Eval to better represent the crowd, further significantly improving correlations.\n\n----\n> **The considered interaction scenarios could be more diverse to better reflect real-world user behaviors** \n\nPlease see the reply to question 2 Below.\n\n  \n----\n# Questions:\n\n> **Line 207~213: can you provide more explanations into why ChatGPT sometimes outperformed GPT-4 in your experiments? This seems counterintuitive given GPT-4's generally stronger capabilities.**\n\n\nTable 1 evaluates how close evaluators are to humans on absolute scores. Based on the results, although GPT-4 outperforms GPT-3.5 in accuracy and demonstrates stronger capabilities, scores given by GPT-3.5 are closer to humans than GPT-4. The main reason for low GPT-4 scores is its **more critical and strict evaluation** of “Helpfulness”.\n\nAlthough giving low scores, Table 2 shows that **GPT-4 mostly correlates to human evaluations** since its scores have the most similar trend as human scores. Given GPT-4’s generally strong capability, this result is not counterintuitive and supports the above reason.\n\n  \n----\n> **Have you considered incorporating more diverse interaction patterns, such as clarification questions, ambiguous queries, or follow-up questions? How might this affect the evaluation results?** \n\n\nIn section 5, we discussed the effect of different types of persona and more diverse interaction patterns (i.e. clarification, ambiguous, complex, follow-up, expert, detailed, straightforward, knowledgable, etc.). For example, each persona produces different interaction patterns which are diverse. The persona “Clarity-Seeker” prefers to clarify questions, terminologies, proper nouns, etc. The persona “Adaptability-Seeker” always proposes ambiguous questions and prefers IQA models that can understand its questions. The persona “Critical-Seeker” always follows the original question and asks critical questions to IQA models to answer the question. Our different personas have covered a variety of interaction patterns. Results are shown in Tables 3 and 4. **By adding personas and having more diverse interactions, IQA-Eval achieves higher correlations with human evaluations and better represents the crowd**.\n\nMoreover, in section 6.2 (LLM to Benchmarks), we benchmark IQA models using our IQA-EVAL **on question answering data and ambiguous queries**, showing our IQA-EVAL can be applied to different types of questions and interaction patterns and provide useful feedback.\n\n  \n----\n> **Have you explored how different persona distributions might impact the evaluation results? How sensitive is the framework to changes in persona assignments?**\n\n\nIn practice, the distribution of personas should be thoroughly surveyed. Given those distributions, the evaluation performance of IQA-Eval should always be similar to human evaluations (high correlation scores). **We expect that the distribution of personas should have little effect on IQA-Eval since all scores well in correlation with human raters (0.634-0.690)**.\n\n----\n## References:\n\n1. Lee, Mina, et al. \"Evaluating human-language model interaction.\" arXiv preprint arXiv:2212.09746 (2022).", "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": "Ek11HwaogJ", "reviewer": "Reviewer_RXxM", "summary": "This paper proposes an automated evaluation framework for Interactive Question Answering tasks based on LLM-based agent. The authors utilize LLMs to simulate the interaction between human and IQA models and use them to evaluate the interactions automatically. Additionally, they assign predefined personas to LLMs to better simulate the interaction characteristics of different groups. Their evaluation framework achieves a stronger correlation with human evaluations and eliminates the high costs of manual evaluation.", "questions": "Major questions:\n1. The paper utilizes LLMs as evaluators for Interactive Question Answering. However, according to some previous research papers [2], a systematic bias may be introduced by LLM evaluators and the results of evaluation may be influenced. Did you consider the impact of bias on the results in your research and try any methods to eliminate it?\n2. You mentioned that accurate models are not necessarily preferred by humans in your paper, but you did not demonstrate this result in your experiments. Could you add a control group in your experiment?\n3. There exists some errors in your analysis of LEA for stage 2. You stated that models’ evaluations highly correlate with human evaluation, while the Pearson correlation coefficient between 0.2 and 0.5 only indicates a weak or moderate correlation. \n4. In your analysis of Table 1, you concluded that model gave higher scores for the “Fluency” metric than human because of the clarity and grammatical correctness of the response generated by IQA models. Could you please provide a detailed explanation of why you concluded that the model understands the concept of “Fluency” better than humans? \n\nMinor questions:\n1. Could you please point out the LEA model used in the experiments shown in Table 3, like in Table 1 and 2?\n\n[2] P. Wang, L. Li, L. Chen, D. Zhu, B. Lin, Y. Cao, Q. Liu, T. Liu, and Z. Sui. Large language models are not fair evaluators. CoRR, abs/2305.17926, 2023.", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The strength of this paper is that it highlighted the importance of interaction in evaluation process for QA tasks and provided a solution for this problem, showing new insights into the evaluation for QA tasks.", "weaknesses": "The weakness of this paper is the lack of technical contributions and novelty, and its experimental analysis is not strong enough. They only used prompts to implement their methods, without any prompt engineering approach. Furthermore, their idea of using LLMs as evaluators is similar to some previous research papers, such as G-Eval [1].\n\n[1] Y. Liu, D. Iter, Y. Xu, S. Wang, R. Xu, and C. Zhu. G-eval: Nlg evaluation using gpt-4 with better human alignment. CoRR, abs/2303.16634, 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you Reviewer RXxM for your reviews. Below we'd like to address your concerns.\n# Weaknesses\n\n----\n> **This paper's weakness is its lack of technical contributions and novelty, and its experimental analysis is not strong enough. It only used prompts to implement its methods without any prompt engineering approach.**\n\nPlease refer to the generel rebuttal above.\n\n----\n> **Their idea of using LLMs as evaluators is similar to some previous research papers, such as G-Eval**\n\nG-Eval implements automatic evaluation for direct outputs from Large Language Models (LLMs) which is non-interactive. However, chat interactions involving multiple turns are more prevalent in real-world applications. Currently, no method is available to automatically evaluate the performance of models in such interactive settings [3]. Our approach, for the first time, addresses this gap by using LLMs to automatically assess the performance of assistant models during interactive scenarios. Moreover, we incorporate personas in our IQA-Eval to better represent the crowd, further significantly improving correlations.\n\n# Questions\n\n----\n> **A systematic bias may be introduced by LLM evaluators and the results of evaluation may be influenced. Did you consider the impact of bias on the results in your research and try any methods to eliminate it?**\n\nWe acknowledge this is a common conern. Please refer to the \"Concerns on self-favoring bias\" section in the general rebuttal.\n\n----\n> **You mentioned that accurate models are not necessarily preferred by humans in your paper, but you did not demonstrate this result in your experiments. Could you add a control group in your experiment?**\n\nThank you for pointing out this. We are sorry for this inaccurate statement. We will add this accurate claim in the paper: accurate QA models are preferred by humans in interaction-aware evaluations.\n\nThe quote from our cited paper [3] is “[...] perception of helpfulness is not necessarily reflected in the overall interaction accuracy.” It describes the conclusion of multiple tasks in that paper (e.g. text summarization, social dialogue, QA). However, in the QA settings, Table 3 in Lee et al. (2023) shows that humans prefer accurate models on the QA task.\n\nWe also conducted **new experiments** **(1)** using LEA to evaluate interactions between LEAs and IQA models (interactive) and **(2)** using LEA to evaluate direct answers generated by IQA models (non-interactive). Our experiments show that **LEA models prefer accurate models, which aligns well with the conclusion from human annotations.**\n\n(1) Evaluate interactions between LEA and IQA models.\n\n||||||||||||||\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|5-point Likert|Helpfulness||||Fluency||||Accuracy||||\n|LEA models|GPT-3.5|Claude-instant|Llama2-8b|Zephyr-Alpha|GPT-3.5|Claude-instant|Llama2-8b|Zephyr-Alpha|GPT-3.5|Claude-instant|Llama2-8b|Zephyr-Alpha|\n|IQA-EVAL-GPT4|4.60|4.60|3.83|4.27|4.97|5.00|4.87|4.93|0.93|0.93|0.83|0.93|\n|IQA-EVAL-Claude|4.90|5.00|4.97|4.97|4.87|5.00|4.93|4.87|0.73|0.8|0.57|0.73|\n\n(2) Evaluate direct answers generated by IQA models\n||||||||||||||\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|5-point Likert|Helpfulness||||Fluency||||Accuracy||||\n|LEA models|GPT-3.5|Claude-instant|Llama2-8b|Zephyr-Alpha|GPT-3.5|Claude-instant|Llama2-8b|Zephyr-Alpha|GPT-3.5|Claude-instant|Llama2-8b|Zephyr-Alpha|\n|IQA-EVAL-GPT4|4.33|4.17|2.70|3.53|5.00|4.97|4.13|4.33|0.83|0.80|0.47|0.57|\n|IQA-EVAL-Claude|4.97|5.00|4.53|4.87|4.97|4.97|4.47|4.97|0.83|0.80|0.47|0.57|\n\n----\n----\n> **There exists some errors in your analysis of LEA for stage 2. You stated that models’ evaluations highly correlate with human evaluation, while the Pearson correlation coefficient between 0.2 and 0.5 only indicates a weak or moderate correlation**\n\nThank you for bringing this to our attention. We will modify the claimed relation to be weak to moderate in the paper.\n\nFor interactions between humans and IQA models (Figure 2), LEA evaluations moderately correlate with human evaluations. However, for interactions between LEAs and IQA models (Table 2) which is the main focus of our paper, LEA evaluations highly correlate (around 0.6) with human evaluations. This indicates that **LEA models are helpful when participating in the whole evaluation process**, including both the interaction and evaluation. However, **when evaluating interactions between human and IQA models, LEAs focus differently from humans**, which causes moderate correlations in the “analysis for LEA for stage 2”.\n\n----\n> Unsupported paper claim: **the model understands the concept of “Fluency” better than humans?**\n\nSorry for the confusion. We did not conclude that the model understands the concept of “Fluency” better than humans. Instead, we wanted to emphasize that **IQA-Eval scores on “Fluency” are close and highly correlated to human judgments**. Both scores given by human and LEA models show that IQA models provide fluent outputs. We will clarify and update this claim in the final version.\n\n----\n> **Could you please point out the LEA model used in the experiments shown in Table 3, like in Table 1 and 2?**\n\nIn Table 3, the LEA model is GPT-3.5-turbo-1106. We will update it in the final version.\n\n----\n**References:**\n1. Zheng, Lianmin, et al. \"Judging llm-as-a-judge with mt-bench and chatbot arena.\" Advances in Neural Information Processing Systems 36 (2024).\n    \n2. Furniturewala, Shaz, et al. \"Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models.\" arXiv preprint arXiv:2405.10431 (2024).\n    \n3. Lee, Mina, et al. \"Evaluating human-language model interaction.\" arXiv preprint arXiv:2212.09746 (2022).\n    \n4. Liu, Yang, et al. \"G-eval: Nlg evaluation using gpt-4 with better human alignment.\" arXiv preprint arXiv:2303.16634 (2023).", "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:
We would like to thank all reviewers for their insightful reviews. Below we address common concerns by topics.

# Insufficient technical contribution, especially on the lack of prompt engineering.

Inspired by G-eval [4], we did conduct prompt engineering and designed our prompts by combining detailed instructions for different functions, such as task description, role description, metrics instructions, and evaluation instructions (we will add these information in the final version of the main paper). Each prompt is tuned based on a general question not included in IQA-Eval. We combine those instruction prompts flexibly based on evaluation stages. 

We believe that the prompts in the paper are well-designed. To be detailed by our new experiments below, adding new tricks of prompting, such as using few-shot prompting or otherwise adding details and de-bias instruction does not show significant improvements. 

# Concerns on LLM self-favoring bias

The impact of bias is low in our IQA-Eval.
During the develpoment of the prompts, we considered the impact of bias and manually evaluated all model interactions. Based on our evaluations, we tuned prompts as mentioned above to instruct our model to best reduce the impact of potential bias.

To prove that the bias effect is low, **we conduct a new experiment using a more complex prompt and few-shot, including effective debiasing prompts** (following FastChat [1] and Shaz et al [2]) to evaluate interactions. In the following table, the scores assigned by LEA models in response to our prompts align more closely with human evaluations.

||Helpfulness |||Fluency |||Accuracy |||
|----------------------------- |----------- |---- |---- |------- |---- |---- |-------- |---- |---- |
|LEA models |TDA |TB |DA |TDA |TB |DA |TDA |TB |DA |
|Human |4.60 |3.84 |3.52 |4.35 |3.84 |3.22 |0.69 |0.52 |0.48 |
|IQA-EVAL-GPT4 (our prompting) |3.67 |2.30 |2.10 |4.77 |3.87 |3.03 |0.87 |0.83 |0.67 |
|IQA-EVAL-GPT4 (New) |3.50 |2.23 |2.10 |4.40 |4.07 |3.53 |0.87 |0.83 |0.67 |

In our experiments on the MMLU dataset (Section 4&5), LEAs mainly evaluate the performance of IQA models in the interactions. Each LEA model evaluates all IQA models. **LEA models and IQA models are not the same models.** Thus, the impact of bias, as LLMs prefer themselves, is not a concern in the evaluation results presented in sections 4 (Meta Evaluation of IQA-EVAL Framework) and 5 (Effect of Assigning Persona to LEA).

Finally, in our benchmarking of different IQA models (Section 6). The impact of self-preference bias is low when using IQA-EVAL for benchmarking different IQA models. In IQA-EVAL benchmarking results (Table 5), line GPT3.5 appears to be the only one impacted by self-preference bias since it serves as both the LEA and IQA models. To prove that the self-preference bias effect is low, **we run the experiment using again the more complex prompt and few-shot like above**. The experiment results are highly similar to the line “GPT3.5” in Table 5, indicating that the self-preference bias issue has little impact on IQA-Eval.
 
 **HotpotQA**:

|LEA models |Helpfulness |Fluency |# Queries |Accuracy |
|-------------------------- |----------- |------- |--------- |-------- |
|IQA-EVAL-GPT3.5 (In paper) |4.72 |4.95 |1.49 |0.63 |
|IQA-EVAL-GPT3.5 (New) |4.68 |4.91 |1.35 |0.60 |
We will add these results and corresponding prompts to the paper.

----
[1] Zheng, Lianmin, et al. "Judging llm-as-a-judge with mt-bench and chatbot arena." Advances in Neural Information Processing Systems 36 (2024).

[2] Furniturewala, Shaz, et al. "Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models." arXiv preprint arXiv:2405.10431 (2024).

[3] Lee, Mina, et al. "Evaluating human-language model interaction." arXiv preprint arXiv:2212.09746 (2022).

[4] Liu, Yang, et al. "G-eval: Nlg evaluation using gpt-4 with better human alignment." arXiv preprint arXiv:2303.16634 (2023).

Meta-review: The authors propose an interactive evaluation framework that uses an LLM to pose multi-turn questions to QA models under evaluation. The reviewers in general appreciate the usefulness of the framework, the fact that it is multi-turn and the results are correlated with human judgement. There are some concerns raised (including ethical concerns), such as the danger of self-bias during evaluation, the novelty of the work, and several technical details and questions. The authors conduct further experiments to support their rebuttal, addressing most concerns adequately.
Decision: Accept (poster)