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REVIEW 4 major objections 5 minor 1 cited by

Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims that adding population-level intent categories to session-level LLM prompting makes follow-up question suggestions in an enterprise conversational assistant more useful and better at revealing underused features, and…

desk verdict A clear applied framework for proactive question suggestions in an enterprise assistant, but the evaluation doesn't yet support the 'demonstrating' claim. read the letter →

arxiv 2412.10933 v1 pith:OQ22AKW2 submitted 2024-12-14 cs.CL

Xiaobin Shen , Daniel Lee , Sumit Ranjan , Sai Sree Harsha , Pawan Sevak , Yunyao Li This is my paper · ORCID
classification cs.CL
keywords enterpriseconversationalAIquestionsuggestiondiscoverabilitylargelanguagemodelsretrieval-augmentedgenerationuserintentanalysishumanevaluationAdobeExperiencePlatform
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a simple two-stage framework can materially improve the quality of next-question suggestions in a real enterprise conversational assistant. The first stage periodically analyzes what the user population actually asks after receiving answers; the second stage uses an LLM at the chat-session level to generate suggestions conditioned on the current query, the assistant's response, retrieved documents, and those population-level categories. On 250 real interactions from Adobe Experience Platform's AI Assistant, human annotators preferred the enhanced suggestions over a plain LLM baseline on usefulness (35.4% versus 27.8%) and on discoverability (33.4% versus 23.2%), while most other criteria were rated equally good. If the result transfers to live use, proactive and category-informed suggestions could become a standard way to onboard new users and surface underused capabilities in enterprise AI systems.

What carries the argument

The central mechanism is a two-stage framework: population-level user intent analysis that categorizes next-question intents into types such as Expansion and Follow-Up, and chat-session-level question generation that prompts an LLM with the session's query history, the AI response, retrieved documents, and those predefined categories. The categories are the device that steers the LLM away from generic follow-ups toward questions that both continue the user's immediate task and introduce related, possibly unknown platform capabilities.

What would settle it

A controlled A/B test in the live AEP assistant, where users are randomly assigned to receive either the enhanced suggestions or the baseline suggestions, measuring click-through rate and downstream feature exploration: if the enhanced suggestions are not clicked or acted upon more often than the baseline over a statistically meaningful number of sessions, the paper's claim that they improve usefulness and discoverability would be contradicted.

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Extended reading notes

Core claim

The paper's central claim is that combining periodic population-level user intent analysis with chat-session-level LLM generation produces follow-up question suggestions that are both more useful to the user and better at exposing the system's underutilized features than an LLM baseline that only sees the current query and response. The evidence is a pairwise human evaluation in which ten annotators compared suggestion sets across five criteria. The largest advantages appear exactly where the framework is designed to help: usefulness and discoverability, with the enhanced set preferred 35.4% to 27.8% and 33.4% to 23.2% respectively.

Load-bearing premise

The central claim rests on the assumption that the pairwise preferences of ten annotators on 250 selected interactions predict how the broader population of real Adobe Experience Platform users would experience the suggestions in live use, despite strong disagreements among individual annotators and the lack of statistical significance testing.

Editorial extensions

If this is right

  • If the central claim is correct, the framework can be deployed in other enterprise assistants where historical interaction data is too sparse to train conventional query-prediction models.
  • The periodic intent analysis can be rerun as the platform evolves, so the suggestion categories track new capabilities and shifting user behavior.
  • Even with minimal session history, the LLM-plus-RAG setup can still produce suggestions that are contextually relevant, because the retrieved documents and categories add grounding.
  • The annotator-role differences suggest that tailoring suggestion categories to user roles, such as engineer versus product manager, could further improve perceived usefulness and discoverability.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: because the enhanced method differs from the baseline in several ways at once (a separate generation pass, inclusion of chat history, and predefined categories), a follow-up ablation varying one factor at a time could isolate which component drives the reported gains.
  • Beyond the paper: the paper's human-preference results predict, but do not measure, actual behavior; a production A/B test on click-through rate and downstream feature adoption would convert the preference signal into a behavioral outcome.
  • Beyond the paper: if the role-based differences replicate, a personalized category set per user role may outperform the one-size-fits-all Expansion/Follow-Up split, and could be tested in the same pairwise framework.
  • Beyond the paper: the 36% 'unrelated' share of next questions found in the intent analysis suggests that non-contextual suggestions, such as periodic feature spotlights, may be worth adding alongside contextual ones to truly improve discoverability.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a two-stage framework for generating next-question suggestions in an enterprise conversational AI assistant, evaluated on the Adobe Experience Platform (AEP) AI Assistant. A population-level periodic intent analysis identifies user intent categories (Expansion, Follow-Up, Other), which are then injected into a session-level LLM prompt that also receives the current query, the AI response, chat history, and retrieved documents. The evaluation uses 250 real user interactions and pairwise human annotations by ten annotators on five criteria: relatedness, validity, usefulness, diversity, and discoverability. The aggregate results show raw preference proportions favoring the enhanced method on usefulness (35.4% vs 27.8%) and discoverability (33.4% vs 23.2%), with smaller margins on the other criteria. The paper concludes that the framework improves usefulness and system discoverability.

Significance. If the causal claim were established, this would fill a genuine gap: question suggestion for enterprise assistants with sparse and noisy interaction logs, evaluated on a deployed industrial product. The paper's strengths include the use of real user logs, a clear and practical generation pipeline, pairwise annotation with role-diverse annotators, and the introduction of a discoverability criterion that is well matched to the application. The intent-analysis statistics in Table 1 are a useful descriptive characterization of the data-sparsity problem. However, the current empirical support for the headline claim is incomplete: the comparison is confounded, the reported numbers are raw proportions without significance testing or inter-annotator agreement, and the role-level analysis is post hoc. With additional controlled comparisons and statistical analysis, the contribution could be solid; as it stands, the evidence is directionally suggestive rather than demonstrative.

major comments (4)
  1. [§4.3, Table 2; Abstract] The headline claim that the framework 'demonstrat[es] improved usefulness and system discoverability' rests on raw pairwise preference proportions over 250 interactions, with no significance tests, confidence intervals, or inter-annotator agreement. The preference margins on the two key criteria are modest (35.4% vs 27.8% for usefulness; 33.4% vs 23.2% for discoverability), and Table 3 shows that one annotator (E2) rated the baseline higher on usefulness (50.0% vs 40.6%). Because annotations are clustered by item and annotator, a paired per-item test with annotator as a random effect (or at least a Wilcoxon signed-rank test on item-level proportions and Cohen's or Fleiss' kappa) is needed before the difference can be attributed to the method rather than to annotator noise.
  2. [§4.2; §3.3] The baseline differs from the enhanced method on three simultaneous dimensions: a separate generation pass, use of in-session chat history, and pre-defined intent categories in the prompt. The paper's claimed novelty is the population-level intent analysis, but as designed the aggregate win cannot be attributed to the categories or to any single component. The statement in §4.2 that the same document retrieval was applied to both methods controls only one factor. At minimum, an ablation with the enhanced pipeline minus the pre-defined categories, and ideally a version of the baseline that also receives chat history, is needed to support the paper's attribution of the improvements to its framework.
  3. [§4.3, Table 3, Appendix A.4] The 'Annotator Role-Based Insights' are presented as findings but are derived from only four of ten annotators, selected 'for conciseness' with no stated selection rule, and the paper asserts that the subset 'effectively captures the key trends' without evidence. No statistical comparison across roles is reported, and E2's reversed usefulness preference shows heterogeneity within a role. This part should be removed from the main results or relabeled as exploratory, with all annotator-level data shown or a transparent selection criterion and appropriate caveats.
  4. [Abstract; §5] The paper claims improved 'system discoverability,' but the evidence is an offline pairwise annotation of generated suggestions by annotators with AEP familiarity, not a measurement of whether users actually discover or use features. Section 5 explicitly lists production engagement metrics such as click-through rates as future work. The abstract and conclusion should therefore say that the framework improves judged usefulness and judged discoverability of the suggestions in a human evaluation, rather than claiming demonstrated improvement in realized system-level discoverability.
minor comments (5)
  1. [§3.2] There is a typo: 'Exansion' should be 'Expansion,' and the notation 'H = Sn j=1 Hi' is confusing because the subscript in the union does not match the session index used in the surrounding text.
  2. [§4.2, Table 2, Appendix A.3] The criterion name is inconsistent: 'Validity' in the text and definitions but 'Validness' in Table 2 and elsewhere; please standardize the terminology.
  3. [Appendix A.1] The prompt template says to generate at least one question for each of the 'first four categories' and later for 'four categories,' but only three categories are defined (Expansion, Follow-up, Other); this inconsistency should be reconciled.
  4. [Figure 2] The figure contains typos, including 'ST ART EXAMPLES' and 'Y our task,' and the reference list contains a broken LaTeX escape ('V osecky') that should be corrected.
  5. [Table 1; §3.3] Table 1 shows that 36% of next questions are unrelated to previous interaction and 23% fall into 'Others,' but the paper does not explain how the prompt's 'Other' category relates to the 'Unrelated' and 'Others' intents; this connection should be clarified.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the framework is evaluated by external human pairwise comparison, and the cited prior work is not load-bearing.

full rationale

This paper does not present a mathematical derivation or a fitted model whose outputs are forced by construction. The central claim is an empirical comparison: question suggestions generated by the proposed framework versus a baseline are judged by ten human annotators on five criteria. The framework's inputs (population-level intent categories, chat history, retrieved documents, and a prompt template) are all specified independently of the evaluation outcomes; the results in Table 2 are aggregate human preferences, not quantities computed from those inputs. The intent categories in Section 4.1 are derived from observed next-question patterns and then used to guide generation, but this is a design choice, not a circular reduction: the evaluation measures whether the resulting suggestions are more useful and discoverable according to human judges, and the baseline is generated without those categories. The only mild self-referential aspect is that the 'Discoverability' criterion (Appendix A.3) is defined in terms of helping users discover new features, and the enhanced prompt explicitly instructs the model to explore AEP capabilities; however, the baseline prompt also generates suggestions, and annotators were blinded to which set came from which method, so the preference is not true by definition. The paper cites prior work involving some of the same authors (e.g., Maharaj et al. 2024 for enterprise AI assistant context; Pradeep et al. 2024 for diversity), but these citations are contextual and do not carry the central argument. No uniqueness theorem, imported ansatz, or renamed empirical pattern is invoked. Concerns about statistical significance, confounding of multiple differences between conditions, and the selective display of four annotators in Table 3 are evaluation-validity issues, not circularity issues. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim rests on LLM behavior and human annotation, not on a mathematical derivation. No new entities and no numeric free parameters are introduced; the main assumptions are that the LLM follows the prompt effectively and that annotator preferences reflect real user value.

assumptions (3)
  • domain assumption GPT-3.5 generates high-quality categorized questions when prompted with context and retrieved documents.
    The framework relies entirely on LLM capability; no alternative model or ablation is tested (Section 3.3, Appendix A.1).
  • domain assumption Human pairwise judgments on five criteria are a valid proxy for real-world usefulness and discoverability.
    This is the only evaluation; no production engagement metrics are reported, and the authors list such metrics as future work (Sections 4.2, 5).
  • domain assumption The two intent categories (Expansion, Follow-up) derived from population-level analysis are sufficient and stable for generating useful suggestions.
    Categories come from a single analysis of next-question patterns (Section 4.1); no sensitivity analysis or validation on later data is provided.

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Cite this review

Pith. "Pith review of Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions." pith.science (2026). https://pith.science/paper/OQ22AKW2

@misc{pith2026241210933,
  author       = {Pith},
  title        = {Pith review of: Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OQ22AKW2}},
  note         = {Machine review of arXiv:2412.10933}
}
read the original abstract

Enterprise conversational AI systems are becoming increasingly popular to assist users in completing daily tasks such as those in marketing and customer management. However, new users often struggle to ask effective questions, especially in emerging systems with unfamiliar or evolving capabilities. This paper proposes a framework to enhance question suggestions in conversational enterprise AI systems by generating proactive, context-aware questions that try to address immediate user needs while improving feature discoverability. Our approach combines periodic user intent analysis at the population level with chat session-based question generation. We evaluate the framework using real-world data from the AI Assistant for Adobe Experience Platform (AEP), demonstrating the improved usefulness and system discoverability of the AI Assistant.

Figures

Figures reproduced from arXiv: 2412.10933 by the authors.

Figure 1
Figure 1. Next Question Suggestion Framework in Enterprise Conversational AI Systems. The framework consists [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Prompt template used for generating contextual and categorized question suggestions. Certain details [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Example of question suggestions in AEP’s AI [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Human Evaluation Interface. Annotators used this interface to compare the quality of question suggestions [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]

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Forward citations

Cited by 1 Pith paper

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