Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience Interviews
Authors: Mengqiao Liu, Tevin Wang, Cassandra A. Cohen, Sarah Li, Chenyan Xiong
Accepted at: acl25

Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience Interviews

Mengqiao Liu1*,†, Tevin Wang2*, Cassandra A. Cohen2, Sarah Li3, Chenyan Xiong2
1 Amazon 2 School of Computer Science, Carnegie Mellon University
3 University of California, Berkeley
liumengq@amazon.com, tevin@cmu.edu, ccohen2@andrew.cmu.edu,
wansarah_3037@berkeley.edu, cx@cs.cmu.edu

Abstract

Which large language model (LLM) is better? Every evaluation tells a story, but what do users really think about current LLMs? This paper presents CLUE, an LLM-powered interviewer that conducts in-the-moment user experience interviews, right after users interact with LLMs, and automatically gathers insights about user opinions from massive interview logs. We conduct a study with thousands of users to understand user opinions on mainstream LLMs, recruiting users to first chat with a target LLM and then be interviewed by CLUE. Our experiments demonstrate that CLUE captures interesting user opinions, e.g., the bipolar views on the displayed reasoning process of DeepSeek-R1 and demands for information freshness and multi-modality. Our code and data are at https://github.com/cxcscmu/LLM-Interviewer.

1 Introduction

Foundation and large language models (LLMs) are redefining the way users interact with the digital world. Billions of users now chat with LLMs regularly through one unified interface and consume information through generative content for their information, entertainment, and task assistance needs (Duarte, 2025).

Understanding user opinions about LLMs is, however, a challenging task. It is difficult to design one evaluation to reflect the rich LLM capabilities. The user experiences powered through generative content are also hard to characterize by metrics like accuracy and BLEU. The tendency for data contamination during pretraining further complicates the understanding of LLM performance (Schaefer, 2023). The community often relies on coarse user preference ratings to understand LLMs’ performances (Chiang et al., 2024).

*These authors contributed equally to this work.
†This work does not relate to their position at Amazon.

CLUE Session Example

Great! To start, can you tell me about your overall experience using the ChatBot? How did you feel during the interaction?

I felt good; I was a little intrigued with the reasoning foretext in each response, but I thought it was a breath of fresh air from other AI programs. Everything seemed appropriate otherwise.

I'm glad to hear that! It sounds like the reasoning foretext added a unique element to your experience. Could you share more about how this transparency affected your understanding or engagement with the ChatBot?

I think it made me want to jump around to different topics and see if I could pull something "out of left field" to see how the AI responded to it, and how they'd accomodate different topics to create a flowing, natural conversation. And it did pretty darn well!

That's interesting! It seems like the transparency encouraged you to explore a variety of topics. ...

Figure 1: An example interview sequence about user experience with their previous LLM interaction.

This paper presents CLUE, Contextualized LLM-powered User Experience understanding, a new framework to gather fine-grained user opinions using massive in-the-moment user experience (UX) interviews. Following interview principles (Wilson, 2013), we develop CLUE-Interviewer on top of an LLM to automatically conduct semi-structured UX interviews: asking user opinions on target dimensions and probing for deeper insights (Figure 1). As an automatic interviewer, it can chat with users right after their interactions with a product, enabling massive in-the-moment interviews. We then build CLUE-Insighter to automatically an-
alyze CLUE interview logs to generate high-level insights of user opinions.

Approved by our university Institutional Review Board (IRB), we recruited thousands of crowd-source workers to chat with an LLM, randomly selected from six LLMs from the GPT, Claude, Gemini, LLaMA, and DeepSeek families. Then CLUE-Interviewer interviews participants right after their chat sessions. We obtained 1206 chat-and-interview sessions on diverse topics initiated by crowdsource workers, ranging from travel destinations to programming developments, that we will make publicly available.

Our human evaluation demonstrates the effectiveness of CLUE-Interviewer in conducting semi-structured and in-the-moment UX interviews. We manually annotated 120 interview sessions or 180 interview turns, randomly sampled for each of the six target LLMs. Two annotators annotated 25% of the labeled data and their agreement level was high. Human annotations confirmed that CLUE-Interviewer covers 74% of the pre-defined dimensions to evaluate target LLMs and actively asks follow-up questions to gain deeper insights. The evaluation also confirms that CLUE-Insighter is highly accurate in categorizing interview rounds and evaluating user responses on target dimensions.

Our user study reflects interesting user opinions of LLMs. Users expressed reserved opinions about LLMs in their text responses to CLUE-Interviewer, considering all mainstream LLMs mediocre, but gave a near-perfect rating when asked explicitly. Users considered LLMs decent at understanding their needs but not as effective in meeting them. Users also expressed new feature requests in the interviews, for example, visual and multimedia capabilities, fresh knowledge access, personalized responses, and flexible access to reasoning and explanation processes, providing motivations and evidence for future LLM development.

The main contributions of this paper are:

1. We present a new methodology to gather user opinions on LLMs via in-the-moment UX interviews.

2. We develop an LLM-powered interview framework that enables automatic in-the-moment interviews at a massive scale.

3. We collect and publicly release thousands of chats and interviews that reflect interesting user opinions of LLMs.

2 Related Work

Many benchmarks have been developed to evaluate predictive effectiveness of LLMs. Notable examples include GLUE (Wang et al., 2018) and SuperGLUE (Wang et al., 2019), both including a suite of natural language understanding tasks. The suite of language tasks quickly grows to hundreds, such as in FLAN (Wei et al., 2022) and BIG-Bench (bench authors, 2023) benchmarks. The community also keeps increasing task difficulty, for example, from MMLU (Hendrycks et al., 2020) to “Humanity’s Last Exam” (Phan et al., 2025), to test the boundary of LLM intelligence. These evaluations are effective in reflecting LLMs’ ability to predict the right label, which aligns closely with some real-world applications such as question answering.

Evaluating generated content is challenging, as two text sequences with high n-gram overlap (Papineni et al., 2002) or semantic similarities (Zhang et al., 2020) may not lead to the same user experience (Hanna and Bojar, 2021). Recent approaches switch to model-based evaluation and employ LLM-as-a-judge for predefined dimensions (Zheng et al., 2023). Model-based evaluation has become a common practice in evaluating generated content, albeit various challenges such as self-biases and inaccuracies (Li et al., 2024a; Ye et al., 2025; Wei et al., 2024).

The ultimate verdict of an AI model is how it serves its users. For established applications like search engines, understanding model performances based on noisy and coarse user feedback is a long-lasting research topic (Chuklin et al., 2022). There are various efforts to collect user feedback on new LLM-powered scenarios. One notable effort is Chatbot Arena (Chiang et al., 2024). It asks users for preferences on side-by-side LLM chats and computes arena scores based on that. Many view the arena score as a reliable reflection of user preferences on LLMs (Hart, 2024).

UX interview is a standard approach to gather in-depth insights about user opinions (Rubin and Chisnell, 2011). It is widely recognized as an effective tool to guide product developments toward increased adoption, consumer loyalty, and overall product success (Hartson and Pyla, 2012). Effective UX interviews often require interview experts (Ahrend, 2025) and are too expensive to scale up. Many explored the potential of AI-powered interview bots, for example, to conduct job interviews (Li et al., 2017) and conversational
surveys (Xiao et al., 2020). Recently, Li et al. (2024b) built an LLM-powered interview system and demonstrated their effectiveness in evaluating student experiences in AI-assisted classrooms.

3 Methodology

CLUE leverages LLMs to conduct in-the-moment UX interviews and understand user opinions. It includes two components: CLUE-Interviewer and CLUE-Insighter.

3.1 CLUE-Interviewer

CLUE-Interviewer conducts in-the-moment UX interviews right after users interacted with a product, which in this study is chatting with a mainstream LLM. We develop CLUE-Interviewer by implementing standard user interview practices (Hartson and Pyla, 2012) into LLMs through carefully designed prompts.

Interview Design. CLUE-Interviewer is designed to conduct semi-structured interviews (Wilson, 2013), starting with a set of predefined interview dimensions and probing users for deeper insights, with the flexibility to explore topics emerging from user responses.

Specifically, to understand user opinions on LLMs, we follow previous research on chatbot user experiences (Casas et al., 2020) and include the evaluation dimensions listed in Table 1. The first four dimensions gather insights about the effectiveness of the target LLMs. The improvements dimension aims to elicit user opinions in an open-ended manner, while the last one asks users for an explicit Likert rating of the target LLM.

LLM-Powered Interviewer. We implement CLUE-Interviewer by building these interview principles into prompting an LLM.

We design the prompts to include instructions that assign the interviewing task to the LLM, specifications of the interview task, and a step-by-step guide of how to complete the task. The steps include reviewing the chat history between the user and the target LLM for in-the-moment study, the designed interview flow, and instructions to conduct a semi-structured interview. The instructions include dimensions to cover and encourage follow-up probes. The full prompts used in CLUE-Interviewer can be found in Appendix Figure 9.

The capabilities of LLMs enable CLUE-Interviewer to collect in-depth user interviews at a reduced cost, without the need for much human intervention from the UX side. It is also easier to collect in-the-moment interviews as users can interact with the LLM interviewer anywhere they want, rather than being interviewed in a controlled setting by a human UX researcher.

<table>
  <tr>
    <th>Dimension</th>
    <th>Description</th>
  </tr>
  <tr>
    <td>Understanding</td>
    <td>Did the LLM understand the user’s request?</td>
  </tr>
  <tr>
    <td>Meet Need</td>
    <td>Did the LLM address user needs?</td>
  </tr>
  <tr>
    <td>Credibility</td>
    <td>Is the LLM factual and relevant?</td>
  </tr>
  <tr>
    <td>General</td>
    <td>What are the user’s general thoughts on the LLM?</td>
  </tr>
  <tr>
    <td>Improvements</td>
    <td>What are potential improvements of the LLM?</td>
  </tr>
  <tr>
    <td>Explicit Rating</td>
    <td>Ask user for an explicit 1-5 satisfaction rating</td>
  </tr>
</table>

Table 1: Interview dimensions in CLUE-Interviewer to understand user opinions on LLMs.

3.2 CLUE-Insighter

To gain collective insights from massive user experience interviews, we build CLUE-Insighter to analyze the interview logs. It first maps raw interview rounds into the interview dimensions and then analyzes corresponding user responses.

Categorize Interview Rounds. We first categorize each interview round—an interchange between CLUE-Interviewer and the user—into one of the evaluation dimensions in Table 1. This is done by prompting LLMs with instructions.

Specifically, we provide the previous rounds in the interview session as well as the to-be-classified round, as context to an LLM and prompt the model to categorize the round into the targeted categories. This is applied on all interview rounds to assign a dimension category to them. The detailed prompt for this step is listed in Appendix Figure 11.

Quantitative Metrics. CLUE-Insighter automatically generates a numerical score for each user response in the first four dimensions in Table 1. It is done by prompting an LLM to convert the user responses to a Likert rating of 1 (bad), 2 (mediocre), and 3 (good). Similar to the dimension mapping, we perform a zero-shot classification to the LLM and instruct it to produce the numerical rating. The prompt is in Appendix Figure 12.

The numerical ratings from individual user responses are merged to quantitative metric scores for corresponding dimensions.

Topic Analysis. To surface high-level user insights from raw responses, CLUE-Insighter applies standard topic analysis (Grootendorst, 2022) on the user responses categorized to each dimension.

CLUE-Insighter first applies simple rule-based filters to remove chit-chat phrases and non-informative texts from user responses, as detailed in Appendix A.1. LLM prompting is used on
<table>
  <tr>
    <th rowspan="2">Target LLM</th>
    <th rowspan="2">Sessions</th>
    <th colspan="3">Chats with Target LLM</th>
    <th colspan="4">CLUE Interviews</th>
  </tr>
  <tr>
    <th>Rounds</th>
    <th>User Words</th>
    <th>LLM Words</th>
    <th>Time (s)</th>
    <th>Rounds</th>
    <th>User Words</th>
    <th>LLM Words</th>
    <th>Time (s)</th>
  </tr>
  <tr>
    <td>DeepSeek-R1</td>
    <td>233</td>
    <td>7.41</td>
    <td>88.37</td>
    <td>2886.21</td>
    <td>645.29</td>
    <td>9.82</td>
    <td>105.71</td>
    <td>3246.73</td>
    <td>445.51</td>
  </tr>
  <tr>
    <td>DeepSeek-V3</td>
    <td>184</td>
    <td>9.44</td>
    <td>97.10</td>
    <td>2193.64</td>
    <td>638.04</td>
    <td>9.53</td>
    <td>96.79</td>
    <td>2521.17</td>
    <td>658.24</td>
  </tr>
  <tr>
    <td>Gemini-1.5-Flash</td>
    <td>207</td>
    <td>11.50</td>
    <td>130.93</td>
    <td>2247.18</td>
    <td>607.28</td>
    <td>9.00</td>
    <td>87.13</td>
    <td>2519.87</td>
    <td>532.52</td>
  </tr>
  <tr>
    <td>LLaMA-3-70B</td>
    <td>204</td>
    <td>9.84</td>
    <td>110.61</td>
    <td>2394.66</td>
    <td>640.63</td>
    <td>9.20</td>
    <td>83.96</td>
    <td>2612.41</td>
    <td>488.43</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>183</td>
    <td>11.14</td>
    <td>124.37</td>
    <td>2158.25</td>
    <td>623.14</td>
    <td>9.52</td>
    <td>80.22</td>
    <td>2357.73</td>
    <td>440.98</td>
  </tr>
  <tr>
    <td>Claude-3.5-Sonnet</td>
    <td>195</td>
    <td>11.01</td>
    <td>107.09</td>
    <td>1639.85</td>
    <td>604.63</td>
    <td>8.54</td>
    <td>73.56</td>
    <td>1666.04</td>
    <td>431.40</td>
  </tr>
  <tr>
    <td>Overall</td>
    <td>1206</td>
    <td>9.98</td>
    <td>109.26</td>
    <td>2275.73</td>
    <td>626.94</td>
    <td>9.28</td>
    <td>88.42</td>
    <td>2513.49</td>
    <td>497.19</td>
  </tr>
</table>

Table 2: Overall statistics of collected chat-and-interview data. All Interviews are by the same CLUE-Interviewer.

![Bar chart showing top topics discussed with target LLMs and topic counts per session](page_101_370_495_246.png)

(a) Top Topics

![Bar chart showing number of unique topics per chat session](page_596_370_495_246.png)

(b) Topic Counts

Figure 2: Top topics user discussed with target LLMs and the number of unique topics per chat session.

the rest of the user responses to extract a list of insights from those responses. We then use BERTopic (Grootendorst, 2022) to cluster these user insights into topics.

Specifically, we embed user chat rounds using OpenAI text-embedding-3-small, and then reduce their dimension from 1536 to 5 using uniform manifold approximation and projection (McInnes et al., 2018), with a local neighborhood size of 5. We cluster the resulting embeddings using HDBSCAN (Malzer and Baum, 2020). We set the minimum cluster size to be 5 and used Claude 3.5 Sonnet (10-22) to summarize the key themes of each clustering on ten randomly sampled user rounds.

The quantitative metrics and topics produced by CLUE-Insighter aim to provide a bird’s-eye view of user opinions. They serve as entry points to, but not replacements of, the interview logs. The latter is the ultimate source for user opinions.

4 User Study

Approved by our university Institutional Review Board (IRB), we conduct a large-scale study on user opinions of LLMs using CLUE. This section describes the user study methodology and the collected data.

4.1 User Study Methodology

We build the user study pipeline by hosting LLM APIs through our customized user interface. In our system, a user first chats with the target LLM. Then CLUE-Interviewer conducts in-the-moment interviews with the user with access to the user’s previous chat history. Screenshots of our UI can be found in Figure 8 in the Appendix.

Recruiting. We recruited participants via Amazon Mechanical Turk (MTurk) for our study. For this open-ended study, we set the qualification criteria as US only, 1k+ tasks completed, and 99% prior approval rate.

Each participant reviewed the study description and provided informed consent that the collected data will be publicly available. They were instructed not to share any personal information in the study.1 We included instructions to improve data quality, such as not using an external chatbot to complete the study. We set up FAQs in all phases of the study to provide a detailed walk-through of how one can complete the task.

Chat with Target LLMs. Participants then engaged in a 10-15 minute conversation with one of the six mainstream LLMs through our hosted chat interface: DeepSeek-R1, DeepSeek-V3, Gemini-1.5-Flash, Llama-3-70B, GPT-4o, and Claude-3.5-Sonnet. Model settings can be found in Table 9 in the Appendix.

The specific LLM used during the chat session was randomly assigned and the identity was not revealed to the user.2

Participants freely chatted with the LLM on any topics. They were encouraged to interact for 15 minutes but could finish early.

CLUE Interviews. After participants interact with the target LLM, the system directs them to discuss their experiences about the interaction with CLUE-Interviewer. The system has access to the previous interaction between the user and the target LLM and performs semi-structured interviews as designed in Sec. 3.1. We use GPT-4o to power CLUE-Interviewer.

1We will do our best to remove personally identifiable information (PII) before releasing the data.
2Some users asked the LLM who it was, though the answer returned was not necessarily true.
<table>
  <tr>
    <th>Annotation Task</th>
    <th>Annotator Agreement</th>
  </tr>
  <tr>
    <td>Interview Dimension</td>
    <td>Cohen’s Kappa: 0.67</td>
  </tr>
  <tr>
    <td>User Response Rating</td>
    <td>Spearman Correlation: 0.94</td>
  </tr>
  <tr>
    <td>Probing Round</td>
    <td>Spearman Correlation: 0.86</td>
  </tr>
  <tr>
    <td>Explicit Reference to Chat</td>
    <td>Spearman Correlation: 0.68</td>
  </tr>
</table>

Table 3: Agreement between two human annotators on various labeling tasks.

<table>
  <tr>
    <th>Dimension</th>
    <th>Interviewer Coverage</th>
    <th colspan="2">Insighter</th>
    <th>Corr.</th>
  </tr>
  <tr>
    <th></th>
    <th></th>
    <th>Prec.</th>
    <th>Rec.</th>
    <th></th>
  </tr>
  <tr>
    <td>Understanding</td>
    <td>79.8</td>
    <td>0.94</td>
    <td>1.00</td>
    <td>0.67</td>
  </tr>
  <tr>
    <td>Meet Need</td>
    <td>69.5</td>
    <td>0.90</td>
    <td>0.81</td>
    <td>0.66</td>
  </tr>
  <tr>
    <td>Credibility</td>
    <td>37.3</td>
    <td>0.86</td>
    <td>0.60</td>
    <td>0.83</td>
  </tr>
  <tr>
    <td>General</td>
    <td>89.9</td>
    <td>0.71</td>
    <td>0.98</td>
    <td>0.77</td>
  </tr>
  <tr>
    <td>Improvements</td>
    <td>81.3</td>
    <td>0.88</td>
    <td>0.91</td>
    <td>–</td>
  </tr>
  <tr>
    <td>Explicit Rating</td>
    <td>83.5</td>
    <td>0.75</td>
    <td>0.88</td>
    <td>0.88</td>
  </tr>
  <tr>
    <td>Macro Average</td>
    <td>73.6</td>
    <td>0.84</td>
    <td>0.87</td>
    <td>0.76</td>
  </tr>
</table>

Table 4: Evaluation of CLUE using human labels, including the interviewing coverage on designated dimensions, the precision/recall of Insighter’s categorization of interview rounds, and the Spearman correlation of an auto-generated score run and manual scores.

Closing Survey. At the end of the study, users were asked to complete a voluntary demographic survey, including gender, race, age group, education level, and marital status. Demographic data was collected to understand the distribution of our study and whose opinions it is going to reflect.

Data Filtering. Open-ended user studies inevitably include noise. We prompt Claude 3.5 Sonnet (10-22) to filter out incomplete and low-quality chats and interviews. We filter out interactions that did not complete either the chatbot or the interview portion of the study, used a chatbot to complete the study, or provided responses in the interview that did not make logical sense (e.g., did not understand the task, responded randomly, etc.). The details of this filter can be found in Appendix Figure 10. We manually labeled the quality of 120 sessions and compared with the automatic filter. The automatic filter has 91 precision and 72 recall.

4.2 Collected Data

We ran the user study on Amazon MTurk in the period of December 2024 to January 2025 and collected 1989 user chat-and-interview sessions. In total, 1206 (60.6%) are kept after filtering.

Overall Statistics of our collected data are listed in Table 2. Users on average interacted around 10 turns with the LLMs. The shortest interaction was with DeepSeek-R1, which produces long reasoning chains in between chats. The interaction with CLUE-Interviewer is slightly shorter as it is a more focused conversation.

The volunteered demographic survey shows that the majority of our participants are White/Caucasian, in their 20s-40s, 60% male, and with a Bachelor’s degree. All user opinions collected in this study would be representing this specific MTurker population. The detailed breakdown of participant demographics can be found in Appendix Table 10.

Chat Topics. Additionally, we perform topic analysis on the user chat rounds, using similar techniques discussed in Sec. 3.2, except using a larger minimum cluster size (15) and neighborhood size (15) to account for more chat rounds and lighter filtering.

Figure 2a shows the top topics our participants engaged with LLMs. As expected, participants chatted with LLMs about a large variety of tasks, covering various information seeking, entertainment, and task assistance topics. Figure 2b plots the number of topics included in each chat session. On average, each participant chatted with the target LLMs around 6.60 topics, showing the diversity of user interactions with LLMs.

4.3 Baseline Study

Compared to traditional free-text survey questions that are static, CLUE-Interviewer is dynamic and enables multi-round feedback, probing for deeper insights, and the flexibility to explore topics emerging from user responses. We conducted a small-scale baseline study where we used survey questions instead of CLUE-Interviewer to collect user feedback on LLMs in the same setup. In the survey baseline setting, we included close-ended rating questions on the evaluation dimensions of Understanding, Met Needs, Credibility, and User Explicit Rating, as well as open-ended questions asking for General Improvements feedback.

5 Evaluation of CLUE

Before sharing the findings from our user study, this section presents human evaluations on the effectiveness of CLUE-Interviewer and CLUE-Insighter.

All the human evaluations are done by manually annotating 120 interview sessions or 180 interview turns, randomly sampled for each of the six target LLMs. Two annotators performed annotations on 25% of the labeled data. The agreement between the two annotators is listed in Table 3. Additionally, an example CLUE-Interviewer session with
(a) Probing Frequency    (b) Probing Depth    (c) Interviewer Referred    (d) User Referred
Figure 3: Interviewer evaluations including probing frequency and depth for semi-structured effectiveness, and explicit reference by Interviewer and User to previous interactions for in-the-moment ability.

<table>
  <tr>
    <th>Model</th>
    <th>Understanding<br>(1-3)</th>
    <th>Met Needs<br>(1-3)</th>
    <th>Credibility<br>(1-3)</th>
    <th>General<br>(1-3)</th>
    <th>User Explicit Rating<br>(1-5)</th>
  </tr>
  <tr>
    <td>DeepSeek-R1</td>
    <td>2.58 ± 0.05</td>
    <td>2.31 ± 0.06</td>
    <td>2.38 ± 0.07</td>
    <td>2.06 ± 0.03</td>
    <td>4.38 ± 0.06</td>
  </tr>
  <tr>
    <td>DeepSeek-V3</td>
    <td>2.62 ± 0.05</td>
    <td><b>2.35</b> ± 0.06</td>
    <td>2.31 ± 0.08</td>
    <td><b>2.10</b> ± 0.04</td>
    <td>4.56 ± 0.06</td>
  </tr>
  <tr>
    <td>Gemini-1.5-Flash</td>
    <td>2.54 ± 0.05</td>
    <td>2.31 ± 0.06</td>
    <td>2.28 ± 0.08</td>
    <td>2.02 ± 0.04</td>
    <td>4.64 ± 0.05</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>2.65 ± 0.05</td>
    <td>2.25 ± 0.06</td>
    <td><b>2.48</b> ± 0.08</td>
    <td>2.05 ± 0.04</td>
    <td>4.69 ± 0.05</td>
  </tr>
  <tr>
    <td>Claude-3.5-Sonnet (10-22)</td>
    <td>2.56 ± 0.05</td>
    <td>2.24 ± 0.06</td>
    <td>2.30 ± 0.09</td>
    <td>1.68 ± 0.04</td>
    <td>4.58 ± 0.07</td>
  </tr>
  <tr>
    <td>LLaMA-3.3-70B</td>
    <td><b>2.68</b> ± 0.04</td>
    <td>2.29 ± 0.06</td>
    <td>2.44 ± 0.09</td>
    <td>1.80 ± 0.04</td>
    <td><b>4.71</b> ± 0.04</td>
  </tr>
  <tr>
    <td>Overall</td>
    <td>2.60 ± 0.02</td>
    <td>2.29 ± 0.02</td>
    <td>2.36 ± 0.03</td>
    <td>1.97 ± 0.01</td>
    <td>4.58 ± 0.02</td>
  </tr>
</table>

Table 5: User ratings of LLMs from CLUE Interviews. The first four evaluation dimensions use five run mean ratings automatically categorized by CLUE-Insighter from user textual responses (1–3 Likert). The final column shows the user explicit rating (1–5 Likert). Error bars (±) indicate the combined within/between standard errors for auto-ratings and standard errors for explicit ratings across sessions. Uncategorizable answers are removed.

<table>
  <tr>
    <th></th>
    <th>Understanding</th>
    <th>Meet Need</th>
    <th>Credibility</th>
    <th>General</th>
    <th>Explicit Rating</th>
  </tr>
  <tr>
    <td>Understanding</td>
    <td>1.00</td>
    <td>0.22</td>
    <td>0.18</td>
    <td>0.18</td>
    <td>0.19</td>
  </tr>
  <tr>
    <td>Meet Need</td>
    <td>0.22</td>
    <td>1.00</td>
    <td>0.26</td>
    <td>0.27</td>
    <td>0.26</td>
  </tr>
  <tr>
    <td>Credibility</td>
    <td>0.18</td>
    <td>0.26</td>
    <td>1.00</td>
    <td>0.24</td>
    <td>0.14</td>
  </tr>
  <tr>
    <td>General</td>
    <td>0.18</td>
    <td>0.27</td>
    <td>0.24</td>
    <td>1.00</td>
    <td>0.14</td>
  </tr>
  <tr>
    <td>Explicit Rating</td>
    <td>0.19</td>
    <td>0.26</td>
    <td>0.14</td>
    <td>0.14</td>
    <td>1.00</td>
  </tr>
</table>

Figure 4: Spearman correlation between Insighter-generated ratings and user explicit ratings.

manual annotations can be found in Appendix Figure 15.

Semi-Structured Interview. We first manually labeled the evaluation dimensions (Table 1) of CLUE-Interviewer rounds. Table 4 shows the coverage of CLUE-Interviewer for each dimension. It confirms that our prompts are effective in converting the LLM into an interviewer. CLUE-Interviewer covers the majority of designed dimensions in its interviews. The only exception is coverage of credibility, which can be improved in future research.

We manually labeled the interview rounds where CLUE-Interviewer asks follow-up questions. The distributions of probing frequency and depth are plotted in Figure 3a and 3b. In more than half of interview sessions, CLUE-Interviewer probes users for more detailed feedback, asking on average 1.3 follow-up questions, rather than merely asking predefined questions.

In-the-Moment Interview. This experiment evaluates the ability of CLUE-Interviewer to conduct in-the-moment interviews. We manually labeled explicit references to previous interactions in the interview sessions by the interviewer and the users, as a reflection of the in-the-moment effect. The distributions are plotted in Figure 3c and 3d. Interestingly, users actively refer to their previous interactions more frequently than the interviewer for more than 1/3 of the time during interviews, reflecting users’ in-the-moment status.

Bird’s-Eye Insights. CLUE-Insighter automatically maps interview questions into target dimensions and converts user responses into categorical
(a) Understanding
(b) Meet Need
(c) Credibility
(d) General
Figure 5: Top topics popped by CLUE-Insighter from user response in the corresponding dimensions in our user study. The user sentiment of such topics are marked by + (user praised), . (mixed), and - (user criticized).

(a) Deepseek-R1
(b) DeepSeek-V3
(c) Gemini-1.5-Flash
(d) GPT-4o
(e) Claude-3.5-Sonnet
(f) LLaMA-3.3-70B
Figure 6: Top topics discovered by CLUE-Insighter from user responses to the Improvement dimension in interviews.

ratings. To evaluate this process, we compare these two automatic operations with our human annotations in Table 4. It shows that CLUE-Insighter, though not perfect, is sufficient to provide a bird’s-eye view of user opinions from the raw interview logs.

6 User Opinions of LLMs

This section presents the quantitative ratings (Sec. 6.1) and qualitative insights (Sec. 6.2) about user opinions of LLMs from our user study.

6.1 Quantitative Ratings

Table 5 shows the ratings of LLMs from our user study. The first four are automatically assigned by CLUE-Insighter based on users’ textual responses. The last rating is explicitly provided by users.

In contrast to “beyond Ph.D. intelligence” performances on various exam-style benchmarks (Phan et al., 2025), all studied LLMs have significant room for improvement in open-ended chats, their main consumer scenario. All LLMs are scored around a mediocre 2 General rating. These fine-grained interview dimensions show that current LLMs are better at understanding user needs but less effective at meeting them. The credibility is also often questioned.

Users are much more lenient when asked for explicit ratings. The majority of studied LLMs received 4.5 scores. This discrepancy aligns with the
<table>
  <tr>
    <th>Dimension: Topics</th>
    <th>Example User Responses</th>
  </tr>
  <tr>
    <td><b>Understanding:</b><br>Knowledge Understanding</td>
    <td>"...I asked about a couple of different wrestlers some who have not been in the ring in 30 years and it knew who they were and were well informed"<br>"...One example was when I asked about cultural jokes that don’t translate well. The response not only provided multiple examples from different cultures (German, Chinese, Egyptian, Russian, and British humor) but also connected them to humor theories we had discussed earlier..."</td>
  </tr>
  <tr>
    <td><b>Meet Need:</b><br>Handling Complex Topics</td>
    <td>"...There were moments when it struggled with more complex questions or when I needed nuanced advice..."<br>"...if I wanted to talk about the election or the LA fires, I felt the chatbot wouldn’t be able to respond"</td>
  </tr>
  <tr>
    <td><b>Credibility:</b><br>Limited Depth & Authenticity</td>
    <td>"Pretty much gives good info on why they are good, but nothing about the negatives."<br>"...the response felt more general and could have included specific examples or recent developments to make it more relevant and up-to-date."</td>
  </tr>
  <tr>
    <td><b>General:</b><br>Transparency of Reasoning</td>
    <td>"I thought it was pretty interesting to be able to see its reasoning"<br>"I liked seeing how it was processing its response and showing me its thinking"</td>
  </tr>
  <tr>
    <td><b>General:</b><br>Overly Verbose</td>
    <td>"the bot talks too much"<br>"I felt it was helpful, and gave good answers if maybe a pit verbose."</td>
  </tr>
  <tr>
    <td><b>Improvements:</b><br>Access Real-time Data</td>
    <td>"Could be allowed internet access to get the latest information."<br>"live search to provide up to date information"</td>
  </tr>
  <tr>
    <td><b>Improvements:</b><br>Add Visual Content</td>
    <td>"Add images of the spots will be more helpful."</td>
  </tr>
  <tr>
    <td><b>Improvements:</b><br>More Personalized</td>
    <td>"I’d suggest making responses more personalized and detailed, perhaps by asking follow-up questions to better understand the user’s needs..."<br>"I would recommend making responses more personalized and providing more rapid clarification for complicated queries."</td>
  </tr>
</table>

Table 6: Example topics (cluster names) and responses from participants to CLUE-Interviewer.

common challenges of Likert ratings; users have different levels of leniency and may not reason much about scoring (Subedi, 2016). In comparison, UX interviews are known to be effective in probing out actual opinions from consumers (Wilson, 2013).

Figure 4 shows the Spearman correlations between different evaluation dimensions. Among all dimensions, meet needs has the strongest correlations with other dimensions, showing that the utility of LLMs—their ability to satisfy user needs—is still the north star of LLM user experience. Similar to the cross-LLM comparisons, the user explicit rating only has weak correlations with user sentiment underlying their interview responses. A more detailed scatter plot of correlations can be found in Figure 7 in the Appendix.

6.2 Qualitative Insights

This set of analyses presents the qualitative insights gathered from our user interviews.

Fine-Grained User Feedback. Figure 5 plots the top topics gathered from user responses categorized in the first four interviewing dimensions. The topics are aggregated from all six LLMs.

These topics reveal more fine-grained user opinions than numerical ratings. While some users praised LLMs’ understanding, others raised questions on LLMs’ ability to handle complex topics and their authenticity. When asked about their general impressions, some users praised the transparency of AI reasoning. This opinion likely came from those matched with DeepSeek-R1 which displayed reasoning chains.

User Suggested Improvements. We conduct a deeper dive into the suggestions provided by users when asked about potential improvements to their LLM experience. The top topics popped up in our analysis for each LLM are plotted in Figure 6. Note that there are no pre-defined features for users to pick from in our free-text interviews. All topics come from responses users provided.

In contrast to some previous research, users have a strong preference for concise responses and complained about LLMs’ verbosity. Some suggestions correlate with the quantitative insights from other evaluation dimensions. For example, users have issues with LLMs’ ability to understand longer contexts and would like more conversational interactions.

We provide one of the first studies on user opinions of displaying reasoning processes. Our user study indicates that it is quite a bipolar feature. Some users explicitly requested it, while some preferred the display of the thought process be optional.

Aligned with the views from many in the community, users actively request features such as multi-
<table>
  <tr>
    <th>Model</th>
    <th>Understanding (1-3)</th>
    <th>Met Needs (1-3)</th>
    <th>Credibility (1-3)</th>
    <th>User Explicit Rating (1-5)</th>
  </tr>
  <tr>
    <td>Survey Baseline</td>
    <td>2.61</td>
    <td>2.60</td>
    <td>2.61</td>
    <td>4.58</td>
  </tr>
  <tr>
    <td>CLUE-Interviewer</td>
    <td>2.60</td>
    <td>2.29</td>
    <td>2.36</td>
    <td>4.58</td>
  </tr>
</table>

Table 7: User ratings of LLMs from Baseline Study. The first four evaluation dimensions use five run mean ratings automatically categorized by CLUE-Insighter from user textual responses (1–3 Likert). The final column shows the user explicit rating (1–5 Likert). Uncategorizable answers are removed.

<table>
  <tr>
    <th>Topics</th>
  </tr>
  <tr>
    <td>Improve Response Quality and Actionable Feedback</td>
  </tr>
  <tr>
    <td>User-Based Personalization</td>
  </tr>
  <tr>
    <td>Improving Contextual Memory in Long Conversations</td>
  </tr>
  <tr>
    <td>Limited Access to Current Information</td>
  </tr>
  <tr>
    <td>Natural Conversational Flow</td>
  </tr>
  <tr>
    <td>Complex Query Handling Limitations</td>
  </tr>
  <tr>
    <td>Improving AI’s Contextual and Tonal Understanding</td>
  </tr>
  <tr>
    <td>Need for more diverse and context-aware AI responses</td>
  </tr>
  <tr>
    <td>Need for Enhanced Emotional Intelligence Capabilities</td>
  </tr>
  <tr>
    <td>Interactive Follow-up Questions</td>
  </tr>
  <tr>
    <td>Natural Language Understanding Improvements</td>
  </tr>
  <tr>
    <td>Human Agent Handoff Capabilities</td>
  </tr>
</table>

Table 8: Top improvement topics from the baseline survey study.

modality capabilities, both processing and generation. Access to real-time data is another common request, which is not surprising as many of the top chat topics (Figure 2a) are time-sensitive, signifying the benefits of retrieval augmentation.

Example Interview Rounds. Table 6 lists some user responses. These organic consumer opinions elicited by CLUE-Interviewer provide interesting and valuable information about consumer opinions on LLMs, for example, which forms of multi-modality they requested on what occasions. The topics captured by CLUE-Insighter serve as a convenient entry point to these valuable user responses. We will release a demo of these interview dimensions, topics, and organic user interview logs to enable more studies from the community.

6.3 Baseline Comparison

Based on the 154 valid survey responses collected, we compared user ratings on the evaluation dimensions between the survey baseline vs CLUE-Interviewer settings; see Table 7. We found that while average user ratings on the Understanding and User Explicit Rating dimensions were comparable, users were slightly more lenient and gave higher scores on the Met Needs and Credibility dimensions. This corresponds to our finding that users tend to be more lenient when asked to provide explicit feedback, which could mask nuanced user opinions on where LLMs might be lacking in meeting user needs and credibility. Compared to CLUE-Interviewer that probed users in more than 50% of the sessions, survey questions are by design static and there is no opportunity to probe for deeper insights. Table 8 shows the improvement topics from the baseline study, which tend to be less diverse and less comprehensive compared to topics uncovered by CLUE.

7 Conclusion

CLUE is a new methodology to gather user opinions using LLM-powered interviews, enabling a deeper understanding of users through large-scale in-the-moment user experience interviews. Our study with thousands of users shows that CLUE collects fine-grained user opinions on current LLMs, potential improvements, and frequently requested new functionalities.

We view CLUE as a new user understanding tool widely applicable to many products beyond LLMs. It creates a tight-knit connection between consumers and AI developers, enabling more data-driven decisions based on large-scale, targeted, and authentic user opinions reflected in interviews.

8 Limitations

One major limitation is that, being an academic project, our user study is limited to the population available on MTurk, which is not a thorough representation of the US nor global market. The user opinions collected thus only reflect this specific demographic distribution, which can be different from other populations. This is also a potential risk of our framework: CLUE is to capture the opinions of studied users, but may be misinterpreted as the universal opinions of all potential users and introduce potential bias. In addition, collecting user data on crowdsourced platforms can raise concerns over privacy and anonymity issues. We tried to mitigate these issues in the participant informed consent (Figure 14) by informing participants of the potential risks, future use of information, and confidentiality. It specifically calls out that participants should not provide any personally identifiable
information (PII) during this study. We also plan to remove all PII before releasing the data.

Another limitation is that as the first demonstration of LLM-powered interviewing, we heavily rely on the power of existing LLMs through prompting APIs. Though useful, there are many possible improvements from the modeling side which can potentially further improve the interviewing effectiveness.

The Insighter produces both quantitative and qualitative insights automatically extracted from interview logs. Its effectiveness is sufficient to provide a high-level idea of user opinions but not perfect as a once-for-all ultimate LLM leaderboard. For example, the numerical ratings are not sensitive enough to tell the differences between evaluated LLMs, albeit these are all top-tier models with similar performances. How to better extract insights from massive user interview logs is another future research direction.

9 Acknowledgments

We would like to thank Tianzhuo Deng and Tongshuang Wu for the discussions that motivated this research. This work is partly supported by Google and Serendipity One.

References

Jan Ahrend. 2025. Shall i do a phd in hci to become a good ux researcher?

BIG bench authors. 2023. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. TMLR.

Jacky Casas, Marc-Olivier Tricot, Omar Abou Khaled, Elena Mugellini, and Philippe Cudré-Mauroux. 2020. Trends & methods in chatbot evaluation. In Proc. of ICMI Companion.

Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastassios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E Gonzalez, et al. 2024. Chatbot arena: An open platform for evaluating llms by human preference. ArXiv preprint.

Aleksandr Chuklin, Ilya Markov, and Maarten De Rijke. 2022. Click models for web search.

Fabio Duarte. 2025. Number of chatgpt users (jan 2025).

Maarten Grootendorst. 2022. Bertopic: Neural topic modeling with a class-based tf-idf procedure. ArXiv preprint.

Michael Hanna and Ondřej Bojar. 2021. A fine-grained analysis of bertscore. In Proc. of WMT.

Robert Hart. 2024. What ai is the best? chatbot arena relies on millions of human votes.

Rex Hartson and Pardha S Pyla. 2012. The UX Book: Process and guidelines for ensuring a quality user experience.

Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020. Measuring massive multitask language understanding. ArXiv preprint.

Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, et al. 2024a. From generation to judgment: Opportunities and challenges of llm-as-a-judge. ArXiv preprint.

Hanming Li, Jifan Yu, Ruimiao Li, Zhanxin Hao, Yan Xuan, Jiaxi Yuan, Bin Xu, Juanzi Li, and Zhiyuan Liu. 2024b. Lm-interview: An easy-to-use smart interviewer system via knowledge-guided language model exploitation. In Proc. of EMNLP System Demonstrations.

Jingyi Li, Michelle X Zhou, Huahai Yang, and Gloria Mark. 2017. Confiding in and listening to virtual agents: The effect of personality. In Proc. of IUI.

Claudia Malzer and Marcus Baum. 2020. A hybrid approach to hierarchical density-based cluster selection. In Proc. of MFI.

Leland McInnes, John Healy, and James Melville. 2018. Umap: Uniform manifold approximation and projection for dimension reduction. JOSS.

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proc. of ACL.

Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhange, and et al. 2025. Humanity’s last exam. ArXiv preprint.

Jeffrey Rubin and Dana Chisnell. 2011. Handbook of usability testing: How to plan, design, and conduct effective tests.

Rylan Schaeffer. 2023. Pretraining on the test set is all you need. ArXiv preprint.

Basu Prasad Subedi. 2016. Using likert type data in social science research: Confusion, issues and challenges. IJCAS.

Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019. Superglue: A stickier benchmark for general-purpose language understanding systems. In Proc. of NeurIPS.
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018. GLUE: A multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP.

Hui Wei, Shenghua He, Tian Xia, Andy Wong, Jingyang Lin, and Mei Han. 2024. Systematic evaluation of Ilm-as-a-judge in Ilm alignment tasks: Explainable metrics and diverse prompt templates. ArXiv preprint.

Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2022. Finetuned language models are zero-shot learners. In Proc. of ICLR.

Chauncey Wilson. 2013. Interview techniques for UX practitioners: A user-centered design method.

Ziang Xiao, Michelle X Zhou, Q Vera Liao, Gloria Mark, Changyan Chi, Wenxi Chen, and Huahai Yang. 2020. Tell me about yourself: Using an ai-powered chatbot to conduct conversational surveys with open-ended questions. TOCHI.

Jiayi Ye, Yanbo Wang, Yue Huang, Dongping Chen, Qihui Zhang, Nuno Moniz, Tian Gao, Werner Geyer, Chao Huang, Pin-Yu Chen, et al. 2025. Justice or prejudice? quantifying biases in Ilm-as-a-judge. In Proc. of ICLR.

Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with bert. In Proc. of ICLR.

Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2023. Judging Ilm-as-a-judge with mt-bench and chatbot arena. In Proc. of NeurIPS.

A Appendix

We provide more details about the implementation of CLUE, our user study, and additional results.

A.1 More Implementation Details

CLUE System UI. A CLUE system UI example can be found in Figure 8. We design the UI to resemble mainstream LLM interfaces.

Model Settings. Table 9 shows the model settings of the six LLMs used in CLUE-Interviewer. The model settings include max tokens, temperature, top-P, top-K, and API.

Interviewer Prompts. The full prompt used in the CLUE-Interviewer system can be seen in Figure 9. In the prompt, we instruct the LLM to serve as a UX researcher and conduct an interview with a user who had just chatted with a chatbot. Via step-by-step instructions, we provided the specific evaluation dimensions to cover and encouraged follow-up questions to be asked.

In an earlier version of the interviewer development, we encouraged the interviewer to probe users for multiple rounds. Sometimes the interviewers were probing too much. We limit the interviewer to ask no more than two follow-ups per question, a conservative choice as the first step towards LLM-based UX interviewers. Future research can explore a better balance of thoroughness and user experience.

Insighter Data Filtering Prompt is in Figure 10. It is a simple prompt that leverages the LLM (Claude-3.5-Sonnet) to filter out obvious noisy data. As discussed in Sec. 4, the automatic filter has very high precision but is lenient in recall.

Insighter Dimension Classification Prompt. The prompt used to classify the interview sessions into evaluation dimensions can be seen in Figure 11. We simply describe each dimension to the LLM (Claude-3.5-Sonnet) and utilize its zero-shot ability for the classification.

Insighter Rating Prompt is in Figure 12.

We acknowledge that there is still room to further improve the implementation of the CLUE-Interviewer and CLUE-Insighter. Better prompt engineering, finetuning dedicated LLMs for our tasks, or using next generation LLMs (e.g., GPT-5 or Claude-4), will certainly improve the performance of the CLUE-Interviewer and CLUE-Insighter. Our simple design has already illustrated many interesting user opinions of LLMs. It is only the beginning.
Figure 7: Scatter plots between 5-run mean dimension scores automatically extracted from user responses and explicit ratings given by users in the interview.

Figure 8: An example of the UIs used for the hosted LLM chat and CLUE-Interviewer.

Insighter Topic Analysis Filtering. We filter some common generic responses by checking if the response provided by the user is a substring of any of the generic responses, which include responses like “nothing much” or “i don’t know”. In addition, this filter removes all responses that have less than 10 characters, as it is unlikely for a user to describe a quality insight in such brevity. For misspelled responses and other generic responses not caught by this rule-based filtering, a three-shot prompt, which is in Figure 13, filters out any remaining basic yes or no responses. This prompt also extracts a list of insights from the answer. Using this filtering process, roughly 27.06% of the answers are kept and used for insight extraction.

A.2 More Details of User Study

Informed Consent and Instructions. Figure 14 shows the informed consent and instructions for the MTurk user study. Participants were informed of the study procedures, potential risks, compensation, future use of information, confidentiality, and voluntary participation. One detail to note is that it specifically calls out that participants should not provide any personally identifiable information during this study.

Demographics. Table 10 shows the demographic statistics of the MTurk participants in our user study. Demographic variables reported included gender, race/ethnicity, age, education, and marital status. Our participant population is biased towards certain demographic groups, perhaps due to the population distributions of MTurkers during our study period. As a result, all the opinions reflected in this paper are from this specific population, which might be different from current user bases of mainstream LLMs.

Payment. All participants were paid between $4 and $6 for their time and participation in this study. This payment rate was determined to be above the US federal minimum wage of $7.25 per hour. We started with $4 but then increased to $6 to facilitate more participation. On average, users spent a total of 25 minutes on our study, corresponding to an average hourly rate of $12, which is significantly more than the federal minimum wage.
<table>
  <tr>
    <th>Model</th>
    <th>Max Tokens</th>
    <th>Temperature</th>
    <th>Top-P</th>
    <th>Top-K</th>
    <th>API</th>
  </tr>
  <tr>
    <td>Deepseek-R1</td>
    <td>2048</td>
    <td>0.7</td>
    <td>0.7</td>
    <td>50</td>
    <td>Together AI</td>
  </tr>
  <tr>
    <td>Deepseek-v3</td>
    <td>2048</td>
    <td>0.7</td>
    <td>0.7</td>
    <td>50</td>
    <td>Together AI</td>
  </tr>
  <tr>
    <td>Gemini-1.5-Flash</td>
    <td>8192</td>
    <td>1</td>
    <td>0.95</td>
    <td>40</td>
    <td>Gemini Developer</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>16384</td>
    <td>1</td>
    <td>1</td>
    <td>N/A</td>
    <td>OpenAI</td>
  </tr>
  <tr>
    <td>Claude-3.5-Sonnet (10-22)</td>
    <td>2048</td>
    <td>1</td>
    <td>1</td>
    <td>Disabled</td>
    <td>Amazon Bedrock*</td>
  </tr>
  <tr>
    <td>LLaMA-3.3-70B</td>
    <td>2048</td>
    <td>1</td>
    <td>1</td>
    <td>N/A</td>
    <td>Amazon Bedrock*</td>
  </tr>
</table>

Table 9: Model settings used for the six mainstream LLMs in CLUE-Interviewer. *via https://github.com/aws-samples/bedrock-access-gateway

<table>
  <tr>
    <th>Demographic Variable</th>
    <th>Frequency</th>
    <th>Percentage</th>
  </tr>
  <tr>
    <th colspan="3">Gender</th>
  </tr>
  <tr>
    <td>Male</td>
    <td>688</td>
    <td>58.35</td>
  </tr>
  <tr>
    <td>Female</td>
    <td>446</td>
    <td>37.83</td>
  </tr>
  <tr>
    <td>Nonbinary / Third Gender</td>
    <td>2</td>
    <td>0.17</td>
  </tr>
  <tr>
    <td>Dual Gender</td>
    <td>1</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>Prefer Not to Say</td>
    <td>42</td>
    <td>3.56</td>
  </tr>
  <tr>
    <th colspan="3">Race/Ethnicity</th>
  </tr>
  <tr>
    <td>White or Caucasian</td>
    <td>1036</td>
    <td>87.87</td>
  </tr>
  <tr>
    <td>Black or African American</td>
    <td>42</td>
    <td>3.56</td>
  </tr>
  <tr>
    <td>Asian</td>
    <td>71</td>
    <td>6.02</td>
  </tr>
  <tr>
    <td>Native American or Alaska Native</td>
    <td>8</td>
    <td>0.68</td>
  </tr>
  <tr>
    <td>Native Hawaiian or other Pacific Islander</td>
    <td>4</td>
    <td>0.34</td>
  </tr>
  <tr>
    <td>Other</td>
    <td>7</td>
    <td>0.59</td>
  </tr>
  <tr>
    <td>Prefer Not to Say</td>
    <td>41</td>
    <td>3.48</td>
  </tr>
  <tr>
    <th colspan="3">Age</th>
  </tr>
  <tr>
    <td>18–20</td>
    <td>5</td>
    <td>0.42</td>
  </tr>
  <tr>
    <td>21–29</td>
    <td>326</td>
    <td>27.65</td>
  </tr>
  <tr>
    <td>30–39</td>
    <td>522</td>
    <td>44.27</td>
  </tr>
  <tr>
    <td>40–49</td>
    <td>154</td>
    <td>13.06</td>
  </tr>
  <tr>
    <td>50–59</td>
    <td>76</td>
    <td>6.45</td>
  </tr>
  <tr>
    <td>60+</td>
    <td>53</td>
    <td>4.5</td>
  </tr>
  <tr>
    <td>Prefer Not to Say</td>
    <td>43</td>
    <td>3.65</td>
  </tr>
  <tr>
    <th colspan="3">Education</th>
  </tr>
  <tr>
    <td>High School Diploma</td>
    <td>95</td>
    <td>8.06</td>
  </tr>
  <tr>
    <td>Associate’s Degree</td>
    <td>7</td>
    <td>0.59</td>
  </tr>
  <tr>
    <td>Bachelor’s Degree</td>
    <td>774</td>
    <td>65.65</td>
  </tr>
  <tr>
    <td>Master’s Degree or Higher</td>
    <td>246</td>
    <td>20.87</td>
  </tr>
  <tr>
    <td>Other</td>
    <td>11</td>
    <td>0.93</td>
  </tr>
  <tr>
    <td>Prefer Not to Say</td>
    <td>46</td>
    <td>3.9</td>
  </tr>
  <tr>
    <th colspan="3">Marital Status</th>
  </tr>
  <tr>
    <td>Single</td>
    <td>237</td>
    <td>20.10</td>
  </tr>
  <tr>
    <td>Married</td>
    <td>858</td>
    <td>72.77</td>
  </tr>
  <tr>
    <td>Divorced</td>
    <td>29</td>
    <td>2.46</td>
  </tr>
  <tr>
    <td>Prefer Not to Say</td>
    <td>55</td>
    <td>4.66</td>
  </tr>
</table>

Table 10: Demographic statistics of the MTurk participants in the user study.

A.3 Additional Results

Correlations with User Explicit Rating. Figure 7 plots the detailed correlations between explicit ratings provided by users when asked by CLUE-Interviewer, and the ratings automatically generated based on user interview responses. Users are significantly more lenient when asked for an explicit rating, giving a lot of perfect 5s. Their responses are more scattered, with a significant fraction of 1 (bad) and 2 (mediocre). These results align with the findings in UX research that interviewing is a more effective tool to discover users’ true opinions.

Interview Examples with Human Annotations. Figure 15 shows an example of an interview session with human annotations on evaluation dimension, probing occurrences and depth, and previous chat mentioned.

Interview Dimension Topics per Model. We showcase the top four topics from the interview responses associated with each model separately for the Understand, Meet Need, Credibility, and General dimensions in Figures 16, 17, 18, and 19 respectively.
Interviewer System Prompt:

Instructions: You are a user experience (UX) researcher. You are going to design a UX interview and conduct the interview with a user. The product for the UX interview is a ChatBot. The user in this interview has just had a conversation with the ChatBot prior to this interview. The goal of the interview is to understand the user’s experience using the ChatBot, if the ChatBot successfully met their needs or solved their problems, and gather feedback on how to improve the ChatBot. Your interview flow and follow-up questions should be tailored to the user’s specific experiences and perspectives regarding using the ChatBot.

<Instructions>

You will receive the chat history between the user and the ChatBot. Your interview language should be friendly, concise, and professional. Incorporate the following tones: curious, welcoming, conversational, empowering, and objective. Do not mention any names. Do not make any judgments about the ChatBot, the user, or the user’s experience. Do not explain your reasoning. Only respond in English and respond to English. Total interview time should be 10-15 minutes. Total number of questions should range from 5 to 10.

To do this task, you should:

1. Review the [Chat History]. The chat history will contain “content” which is the content of the conversation, and “role” which will be either “user” or “assistant” (chatbot).

2. Start the interview with the user. First, greet the user in one sentence and thank them for their participation.

3. Interview the user, one question at a time. Wait for the user to respond before asking another question.

4. Based on the user’s response to the question, ask follow-up questions to understand the how/why behind the user’s experience, behavior, and rationale. If the user provided a yes or no answer with no explanations, probe with follow-up questions to understand the rationales behind the answer. Ask no more than two follow-up questions based on each question. Move on to the next interview question once you’ve gathered sufficient information on the previous question.

5. Make sure you cover the following areas in your interview: understand the user’s experience using the ChatBot, if the ChatBot correctly understood the user’s question or request, if the ChatBot successfully met their needs or solved their problems, if the ChatBot provided coherent, factual, and relevant information, what the user’s overall satisfaction was with the interaction (on a 1-5 scale), and gather feedback on how to improve the ChatBot. Stay focused on these topics. If the conversation starts to deviate from these topics, gently redirect the conversation smoothly back to the main areas of focus.

6. After you’ve gathered sufficient information about the user’s experience, thank the user for their participation again and end the interview.

Figure 9: The system prompt used to instruct CLUE-Interviewer.
Data Filtering Prompt:

If any of the following criteria is observed in the input session or interview, this data point is of low quality:

1. If the user used a chatbot to complete the chatbot
2. If the user used a chatbot to complete the interview
3. If the user’s responses to the chatbot did not make logical sense (e.g., did not understand the task, responded randomly, etc.)
4. If the user’s responses to the interview did not make logical sense (e.g., did not understand the task, responded randomly, etc.)

Predict if the following data point is low quality or not and no need to tell me why.
# First in a new line predict if the passage is of low quality of high quality. Just say “low quality” or “high quality”, nothing else in this line.
session:
[session]

interview:
[interview]

Figure 10: The prompt used to instruct the data filtering system.

Dimension Classification Prompt:

A group of users have been interviewed on their experience using a ChatBot. The interviewer’s messages (questions) are marked with 'role': ‘assistant’, and the user’s responses are marked with 'role': ‘user’. Classify the last interview question in the chat history based on these types:

RQ1: Question asking the user about how well the ChatBot understood the user’s question or request.
RQ2: Question asking the user about how well the ChatBot met their needs or solved their problems.
RQ3: Question asking the user about how well the ChatBot provided coherent, factual, and relevant information.
RQ4: Question asking the user about overall satisfaction with the interaction.
RQ5: Question asking the user about how the ChatBot can be improved.
RQ6: General question asking the user about what they think about the ChatBot
WILD: Other questions, are you ready questions, thanking the user

Chat history:
[HISTORY]
Output the class type and nothing else.

Figure 11: The prompt used to classify interview sessions into evaluation dimensions. The system prompt used is “You are a UX researcher. You are an expert at summarizing insights and themes from user experience interviews.”.

Dimension Rating Prompt:

Based on the following user response about [dimension], provide a rating on a scale of 1–[top_rating]. Only provide the numeric rating without any explanation. If you are not confident about your rating criteria, respond 'NaN'.

Question: [question]

Answer: [answer]

Figure 12: The prompt used to classify dimensions ratings. The system prompt used is “You are a UX researcher. You are an expert at summarizing insights and themes from user experience interviews.”.
Insight Filtering Prompt:

You are a user experience researcher extracting insights from feedback.
Your task is to:
- Ignore any basic yes or no responses
- If the response contains many useful insights, break them into key points.
- Only include as many points as necessary (if there’s only one insight, return just one).

Examples:
Answer: The chatbot seemed to understand my questions and my intentions very well. It understood that I was interested in supplements for strength training and it provided me with an overview of the most popular and useful supplements. When I switched my focus to vitamin B12, it gave me the chemical names of the injectable forms and helped with my concerns. I thought the chatbot did very well at understanding why I was asking the questions.
Insights: [
    "Strong intent recognition across different topics",
    "Able to provide detailed, specific information about supplements and vitamins",
    "Demonstrated contextual understanding and adaptability in conversation"
]

Answer: yes it was fast
Insights: []

Answer: yes
Insights: []

Provide the insights as a **Python list** (e.g., ["Insight 1", "Insight 2"]). Keep these insights concise.

Answer: "[answer]"
Insights:

Figure 13: The prompt used to extract quality insights from interviews for topic analysis. The system prompt used is “You are an AI assistant that extracts key insights from user feedback.”
MTurk User Study Informed Consent:

This AI Chatbot Evaluation task is part of a research study conducted by XXX at XXX University. You will chat with a chatbot for 10-15 minutes and participate in a survey study about your chatbot experience.

Purpose
The purpose of the research is to evaluate large language model powered chatbots.

Procedures
First, chat with a chatbot for 10-15 minutes. You are encouraged to engage with the chatbot for up to 15 minutes but can choose to stop engaging with the chatbot at any point of time. Second, immediately after this chatbot interaction, you will complete a survey including questions about the chatbot experience and demographics. This study is voluntary and you can choose to stop or not answer any questions.

Participant Requirements
Participation in this study is limited to individuals age 18 and older located in the United States. They must have completed at least 1000 HITs and have an approval rate of 99% or above. Participants must have had prior chatbot experience to participate in this study.

Risks
The risks and discomfort associated with participation in this study are no greater than those ordinarily encountered in daily life or during other online activities. To minimize the potential risk of a breach of confidentiality, please do not share any personally identifiable information during this study.
If you use your personal device for research purpose, there is a risk of incurring data charges. There might be data charges on your device if you are not connected to Wi-Fi.

Benefits
There may be no personal benefit from your participation in the study. However, we will gain knowledge about the potential pros and cons of different large language models; such knowledge will be valuable to the scientific community and to the public who use these large language models for various tasks.

Compensation & Costs
You will be compensated $5 for your participation in this 30-min study. There will be no cost to you if you participate in this study.

Future Use of Information
In the future, once we have removed all identifiable information from your data, we may use the data for our future research studies, or we may distribute the data to other researchers for their research studies. We would do this without getting additional informed consent from you (or your legally authorized representative). Sharing of data with other researchers will only be done in such a manner that you will not be identified.

Confidentiality
The data captured for the research does not include any personally identifiable information about you. Your IP address will not be captured. The study will collect your research data through your use of Amazon MTurk and Qualtrics. These companies are not owned by XXX University. These companies will have access to the research data that you produce and any identifiable information that you share with them while using their product. Please note that XXX University does not control the Terms and Conditions of the companies or how they will use or protect any information that they collect.

Payment Confidentiality: Payment methods, especially those facilitated by third-party vendors (such as Visa, Venmo, MTurk, Prolific, Amazon, PayPal), may require that the researchers and/or the vendor collect and use personal information (such as your first and last name, email addresses, phone numbers, banking information) provided by you in order for your payment to be processed. As with any payment transaction, there is the risk of a breach of confidentiality from the third-party vendor. All personal information collected by the researcher will be held as strictly confidential and stored in a password-protected digital file, or in a locked file cabinet, until payments are processed and reconciled. This information will be destroyed at the earliest acceptable time. Personal information held by the third-party vendor will be held according to their terms of use policy.

Right to Ask Questions & Contact Information
If you have any questions about this study, you should feel free to ask them by contacting XXX, XXX University, XXX@XXX. If you have questions later, desire additional information, or wish to withdraw your participation please contact the Principal Investigator by e-mail. If you have questions pertaining to your rights as a research participant; or to report concerns to this study, you should contact the Office of XXX at XXX University. Email: XXX@XXX . Phone: XXX-XXX -XXXX.

Voluntary Participation
Your participation in this research is voluntary. You may discontinue participation at any time during the research activity. You may print a copy of this consent form for your records.

Figure 14: Informed consent and instructions provided to MTurk user study participants
Assistant: Hello! I’m here to interview you about your experience with the chatbot you just spoke to. Are you ready?
User: yes

Assistant: Great! To start, could you share your overall experience using the ChatBot? How did you feel about the interaction?
Dimension: General

User: My experience with the chatbot was rather informative and engaging.
Human Dimension Rating: 3 - good (scale: 1-3)

Assistant: I’m glad to hear that! Could you tell me more about what made the interaction informative and engaging for you?
Dimension: Improvement; Probing Occurrence 1 (Depth 1)

User: Well it provided me with factual and historical information about the conversations we were having.
Human Dimension Rating: 3 - good (scale: 1-3)

Assistant: That’s good to know. Did you feel that the ChatBot understood your questions or requests accurately throughout the conversation?
Dimension: Understand

User: Yes I do.
Human Dimension Rating for Understand: 3 - good (scale: 1-3)

Assistant: That’s great to hear. Did the ChatBot successfully meet your needs or solve any problems you had during the conversation?
Dimension: Meet Needs

User: I didn’t really have any problems to be solved. We had a really good conversation about the recent and rare winter storm affecting the south where I live
Dimension: Improvement; Previous chat mentioned by user
Human Dimension Rating for Meet Needs: 3 - good (scale: 1-3)

Assistant: Thank you for sharing that. Did the ChatBot provide coherent, factual, and relevant information about the winter storm and its impact on your area?
Dimension: Credibility & Improvement; Previous chat mentioned by interviewer

User: Absolutely the chatbot was incredibly relevant and facutal. I was very impressed.
Human Dimension Rating for Credibility: 3 - good (scale: 1-3)

Assistant: I’m glad to hear that the information was helpful and accurate. On a scale of 1 to 5, how satisfied are you with your overall interaction with the ChatBot?
Dimension: User Explicit Rating

User: I would give it a 4.5
Human Dimension Rating for User Explicit Rating: 4.5 (scale: 1-5)

Assistant: Thank you for your rating. Is there anything specific that could be improved to make your experience a perfect 5?
Dimension: Improvement; Probing Occurrence 2 (Depth 1)

User: I knew I was chatting with AI, in order for it to be a perfect 5, train AI responses to be a bit less robotic and more humanlike.

Assistant: Thank you for that valuable feedback. It’s important for interactions to feel natural and engaging. Is there anything else you’d like to add or suggest for improving the ChatBot?
Dimension: Improvement

User: None that I can think of at the moment.

Assistant: Thank you for your insights and feedback. I appreciate your time and participation in this interview. Have a great day!

User: Thank You

Assistant: You’re welcome! Take care!
(a) Deepseek-R1
+ Strong user query comprehension
+ Strong User Input Understanding & Precise...
+ Information Accuracy Concerns

(b) DeepSeek-V3
+ Conversational Flow and Contextual Relevance
+ Contextual Understanding & Engagement in Conversations
+ Response Accuracy Levels

(c) Gemini-1.5-Flash
+ Accurate and Detailed Response Quality
+ User Accuracy and Comprehension Quality
+ Comprehensive Understanding of Technical Topics

(d) GPT-4o
+ Comprehension and Response Accuracy
+ Accurate Information Delivery
+ Accurate Response Provision
+ Understanding User Queries and Questions

(e) Claude-3.5-Sonnet
+ Question Understanding & Comprehension
+ Response Accuracy & Relevance
+ Information Accuracy and Clarity
+ Chatbot Intelligence & Contextual Understanding

(f) LLaMA-3.3-70B
+ User Question Understanding
+ Topic Knowledge Accuracy & Comprehension
+ Accurate Information Delivery
+ Accurate Response Delivery

Figure 16: Top topics discovered by CLUE-Insighter from user responses to the Understand dimension in interviews.
+ Meeting User Needs Successfully
+ Response Quality and Effectiveness
+ Providing Relevant Recommendations and Information
. Response Time and Detail Quality

(a) Deepseek-R1

. Quality Information Response & Communication
+ Effective User Query Resolution
. Personal Growth Through AI Communication
+ Provides Helpful Solutions and Options

Fraction of Insights (%)

+ Provides Helpful Information and Advice
+ Helpfulness and Relevance of Provided Information
+ Response Quality and Accuracy
+ Successfully Addresses User Needs

(b) DeepSeek-V3

+ Effective User Problem Resolution & Support
. Information Quality and Delivery
+ Basic User Needs Satisfaction
+ Comprehensive Information Delivery

Fraction of Insights (%)

+ Effective user interaction and support
. Quality of Information and Assistance Provided
+ Providing Helpful Recommendations & Suggestions
. Problem-solving capability

(c) Gemini-1.5-Flash

+ Effective Problem-Solving and Guidance
. Conversational Ability & Natural Interaction
+ Meeting User Needs Successfully
- Improving Response Quality and Natural Interaction

Fraction of Insights (%)

(d) GPT-4o

(e) Claude-3.5-Sonnet

(f) LLaMA-3.3-70B

Figure 17: Top topics discovered by CLUE-Insighter from user responses to the Meet Need dimension in interviews.
+ Maintaining relevance and coherence in responses
+ Relevant Response Generation
+ Information Coherence and Relevance
. Transparent and Unconventional Thinking

(a) Deepseek-R1

Fraction of Insights (%)

+ Managing Information Flow and Balance in...
+ Coherent and Relevant Responses
. Information Content Delivery
+ Provides Relevant Responses

(b) DeepSeek-V3

Fraction of Insights (%)

+ Information Quality and Relevance
+ Providing Relevant Responses
. Quality of Response Delivery
+ Clear and factual communication

(c) Gemini-1.5-Flash

Fraction of Insights (%)

+ Clear and Comprehensive Information Delivery
+ Provides Coherent and Relevant Responses
+ Accurate and Relevant Response Quality
+ Satisfactory Information Quality

(d) GPT-4o

Fraction of Insights (%)

+ Information Coherence and Relevance
+ Communication Clarity and Coherence
+ Generic but Helpful Service
+ Factual Accuracy Assessment

(e) Claude-3.5-Sonnet

Fraction of Insights (%)

+ Factual and Relevant Response Delivery
+ Coherent and Relevant Information Provided
+ Effectively Explaining Complex Topics
+ Information Quality and Relevance Management

(f) LLaMA-3.3-70B

Fraction of Insights (%)

Figure 18: Top topics discovered by CLUE-Insighter from user responses to the Credibility dimension in interviews.
Figure 19: Top topics discovered by CLUE-Insighter from user responses to the General dimension in interviews.