Epistemic Alignment: A Mediating Framework for User-LLM Knowledge Delivery
Authors: Nicholas Clark
Hua Shen
Bill Howe
Tanu Mitra
Venue: COLM 2025 (2025)
Keywords: epistemology of AI
language model behavior
human-AI interaction
TL;DR: A framework of ten epistemic challenges revealing the gap between how users want knowledge presented and what LLMs currently deliver

Abstract: Large Language Models (LLMs) increasingly serve as tools for knowledge acquisition, yet users cannot effectively specify how they want information presented. When users request that LLMs "cite reputable sources," "express appropriate uncertainty," or "include multiple perspectives," they discover that current interfaces provide no structured way to articulate these preferences. The result is prompt sharing folklore: community-specific copied prompts passed through trust relationships rather than based on measured efficacy. We propose the Epistemic Alignment Framework, a set of ten challenges in knowledge transmission derived from the philosophical literature of epistemology, concerning issues such as uncertainty expression, evidence quality assessment, and calibration of testimonial reliance. The framework serves as a structured intermediary between user needs and system capabilities, creating a common vocabulary to bridge the gap between what users want and what systems deliver. Through a thematic analysis of custom prompts and personalization strategies shared on online communities where these issues are actively discussed, we find users develop elaborate workarounds to address each of the challenges. We then apply our framework to two prominent model providers, OpenAI and Anthropic, through structured content analysis of their documented policies and product features. Our analysis shows that while these providers have partially addressed the challenges we identified, they fail to establish adequate mechanisms for specifying epistemic preferences, lack transparency about how preferences are implemented, and offer no verification tools to confirm whether preferences were followed. For AI developers, the Epistemic Alignment Framework offers concrete guidance for supporting diverse approaches to knowledge; for users, it works toward information delivery that aligns with their specific needs rather than defaulting to one-size-fits-all approaches.

Published as a conference paper at COLM 2025
Epistemic Alignment: A Mediating Framework for User-LLM Knowledge Delivery
Nicholas Clark, Hua Shen, Bill Howe & Tanushree Mitra
Information School
University of Washington Seattle, WA 98105, USA (nclark4, huashen, bi billhowe, mmitra]@u..ee. edu
Abstract
Large Language Models (LLMs) increasingly serve as tools for knowledge acquisition, yet users cannot effectively specify how they want information presented. When users request that LLMs "cite reputable sources," " "express appropriate uncertainty," or "include multiple perspectives," " they discover that current interfaces provide no structured way to articulate these prefer- ences. The result is prompt sharing folklore: community-speciftt copied prompts passed through trust relationships rather than based on measured efficacy. We propose the Epistemic Alignment Framework, set of ten chal- lenges in knowledge transmission derived from the philosophical literature of epistemology, concerning issues such as uncertainty expression, evidence quality assessment, and calibration of testimonial reliance. The framework serves as structured intermediary between user needs and system capa- bilities, creating a common vocabulary to bridge the gap between what users want and what systems deliver. Through a thematic analysis of cus- tom prompts and personalization strategies shared on online communities where these issues are actively discussed, we find users develop elabo- rate workarounds to address each of the challenges. We then apply our framework to two prominent model providers, OpenAl and Anthropic, through structured content analysis of their documented policies and prod- uct features. Our analysis shows that while these providers have partially addressed the challenges we identified, they fail to establish adequate mechanisms for specifying epistemic preferences, lack transparency about how preferences are implemented, and offer no verification tools to confirm whether preferences were followed. For AT developers, the Epistemic Align- ment Framework offers concrete guidance for supporting diverse ways knowing; for users, it works toward information delivery that aligns with their specific needs rather than defaulting to nre-sizeefits-al approaches.
1
Introduction
Large Language Models (LLMs) have emerged as powerful knowledge tools, yet their flexi- bility raises the question of how ensure they deliver information way that matches individual preferences about knowledge quality, evidence standards, and perspective diver- sity. While technical advances have proposed mitigations for hallucination (Ji et al., 2023; Shi et al., 2024; Mishra et al., 2024; Orgad et al., 2025) and uncertainty expression (Yona et al., 2024; Mohri & Hashimoto, 2024), a more subtle problem persists: the misalignment between how users want knowledge presented and the limited mechanisms available to express these preferences. For example, when a medical researcher requests "recent peer-reviewed sources," policy analyst seeks "balanced representation of competing viewpoints,' they encounter interfaces that reduce these rich requirements to unstructured natural language instructions with inconsistent interpretation and no verification mechanisms.
Drawing on the theories of social epistemology and epistemic cognition, we formalize this misalignment as the epistemic alignment problem, and offer four contributions toward
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understanding this challenge. We (1) introduce framework for evaluating how well sys- tems accommodate user preferences about knowledge delivery, (2) validate our framework through thematic analysis of user attempts to control knowledge delivery with prompting strategies shared on online platforms, (3) assess current systems against this framework to identify specific interface limitations, and (4) consider requisite interface features that enable users to express and verify their preferences about how information should be presented, sourced, and qualified. Our work suggests that addressing the epistemic alignment problem requires rethinking how users communicate knowledge preferences to LLM-based systems, shifting from imprecise natural language instructions structured interfaces that support explicit specification of parameters and provide transparent feedback about how these parameters shape knowledge delivery.
User Needs
Epistemic Challenges
System Features
Epistemic Responsibility
Error/Ignorance Tradeoff
1. High Prompting Burdon
System Pollicies
Ealonce utilty
2. Unreliable Refusels
Reasoning policies
Need drow perspectives
Limited Perspectives
Epistemic Personalization
Framing Sensit
e
Personalization Features
Personal Context
Custom Instructions Clariyyigg questions Ardi sycoohancy measures
Culturatigog aphic contoxt Background
S. Poor Disambigustion
Centext Ignorence
Excessive Agreement
Testimonial Reliability
Calibration
Routing Falluras
Tool ection avidence
Tool integration citat ons
Uncorrainty measuremont
Unreliable Citations
expression
Overly Hedged Responses
Figure 1: The Epistemic Alignment Framework as mediating structure between user needs and system implementation. The framework identifies ten challenges across three epistemic dimensions: Epistemic Responsibility (challenges 1-3), Epistemic Personalization (challenges 4-7), and Testimonial Reliability (challenges 8-10). The framework serves as an intermediary layer for evaluating how well systems accommodate diverse epistemic preferences and identifying areas where current interfaces fail support effective knowl- edge delivery. Each challenge was selected based on two criteria: theoretical grounding in epistemology and empirical validation through prevalence user custom instructions.
Related Work
2.1
Philosophical Foundations for Epistemic Alignment
We draw from literature in epistemology, the philosophical subarea concerned with knowl- edge creation and transmission, and epistemic cognition, a topic in educational psychology relating to how people conceptualize knowledge and its acquisition. In particular, we rely on prior work in inquiry and social epistemology that considers how someone ought to responsibly engage with technology for knowledge-related activities.
The object our epistemic activities is inquiry (Hookway, 1994), the self-directed process through which ascertain knowledge. The goal of inquiry inevitably varies
Inquiry
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depending on the circumstance. For instance, sometimes we desire deep, nuanced un- derstanding of an issue; at other times we may be satisfied with a cursory familiarity, The primary vehicle through which we conduct inquiry is by posing questions (Hookway, 2008). The ultimate success or failure of an intellectual investigation in large part relies on the selection and quality of questions (Watson, 2018).
Performing inquiry in the digital age presents challenges, as the large volume of information mediated by opaque discovery mechanisms, such as web search and recommender systems, may give rise to illusions of understanding, where users believe they conducted a thorough investigation when, in fact, their methods are shallow or imperfect (de Ridder, 2022). We consider how these concerns arise when conducting inquiry with LLMs.
Inquisitive Meta-Cognitive Tasks To combat illusions of understanding, de Riddler, drawing from Hookway, formulates set of meta-cognitive tasks requisite to conducting good inquiry (de Ridder, 2022; Hookway, 2003): 1) posing good questions or identifying good problems, 2) identifying good strategies for carrying out inquiries, 3) recognizing when we possess an answer to our question or a solution to our problem, 4) assessing evidence quality for some proposition, and 5) judging when we have considered all or most relevant lines of investigation. These meta-cognitive tasks establish clear criteria for effective inquiry, but in practice, users employ diverse strategies when executing each task, from choosing which questions to pursue to determining when evidence is sufficient. The selected strategies often reflect some combination of practical constraints and personal preferences. We consider user needs when interacting with LLMs for each meta-cognitive task ensure complete coverage of the inquiry process.
Epistemic Cognition The topic of epistemic cognition (Greene et al., 2016) helps explain this variation in inquiry strategies by revealing the connections between beliefs about knowledge and methodology choices. In particular, the AIR framework decomposes the personal epistemology of an individual into their Aims, Ideals, and Reliable processes (Chinn & Rinehart, 2016). Individual assumptions about what constitutes knowledge and how can be verified directly shape learning strategies, information seeking behaviors, and decision-making processes. These epistemic beliefs vary across cultures (Chan & Elliott, 2004) and disciplines (Hofer, 2000), explaining why users might employ radically different approaches for the same meta-cognitive task. We contend that users bring similarly diverse strategies and requirements when engaging with LLMs.
2.2 Technical Approaches for Knowledge Delivery in LLMs
While philosophy provides the theoretical grounding for understanding epistemic chal- lenges, recent technical work has begun addressing aspects of knowledge delivery in L.Mss
Knowledge Acquisition Prior work on the use of LLMs for knowledge acquisition has centered on particular failure modes such knowledge conflict (Xu et al., 2024; Wang et al., 2024; Jin et al., 2024), hallucinations (Huang et al., 2025), factuality (Wang et al., 2023), or, on techniques to facilitate knowledge delivery such citations (Gao et al., 2023). However, prior research primarily assess LLMs' knowledge acquisition capability with task performance (e.g., hallucinations, factuality), but lack systematic study of what humans demand to trust the Al-generated knowledge.
User Intent Beyond these technical assessments of knowledge quality, understanding how users communicate their epistemic needs presents its own set of challenges. In open-domain QA, Min al. showed that real user questions are often ambiguous, proposing systems that enumerate every plausible answer and rewrite the query to clarify each interpretation. Other work attempts to infer hidden intent from the prompt itself: for example, Bodonhelyi et al. (2024) developed fine fine-grained intent taxonomy and found that even advanced LLMs sometimes fail to correctly recognize specific user intents, leading to user dissatisfaction. However, these studies primarily examined user intent within general conversations or tasks, without addressing the specific factors that shape users' needs when evaluating and trusting AI Al-generated knowledge.
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Prompt Sharing Given these difficulties in expressing intent through natural language, users have turned to community-base workarounds. Mahdavi Goloujeh et al. (2024) studied text-to-image generation with Mid- journey, conducting qualitative analysis of semi-structured interviews with members of the community. Shen et al. (2024b) analyzed jailbreak prompts for LLMs, collecting over 1,400 examples from communities across Reddit and Discord. Trippas et al. (2024) examined prompt behavior for Google Bard, analyzing prompt logs for features such as length and semantic diversity. However, while these studies use similar methods, Goloujeh focuses on a different modality, and neither Trippas nor Shen explicitly examine epistemic preferences.
strategies for sharing effective prompts and
3 Problem Definition
Following a literature review of epistemological frameworks and analysis of user-system interactions, we identified three dimensions as both theoretically grounded and practically significant in preserving agency during knowledge transmission between humans and AT systems: epistemic responsibility (practices which promote accurate knowledge acqui- sition), epistemic personalization (individual preferences toward inquiry methods), and testimonial reliability (knowledge transmission via personal accounts).
Epistemic Responsibility The concept of epistemic responsibility, practices that ensure ac- curate knowledge acquisition, is central to the design of epistemic technologies, particularly with respect to who shoulders this burden, the user the system. While Miller & Record (2013) emphasize user responsibility in web search contexts, Al interactions present unique challenges in balancing responsibility between users and system providers. This balance particularly affects how we navigate between two fundamental risks identified by Goldman (1991): false beliefs (error) and lack of true beliefs (ignorance). These failure modes are analogous to Type and Type II errors from hypothesis testing, respectively. When asked "What are the health effects of intermittent fasting?", a system prioritizing precision (avoid- ing error) might respond "I cannot make medical claims" while one prioritizing coverage (avoiding ignorance) might provide comprehensive research findings with appropriate caveats. Users need mechanisms to specify whether they prefer cautious, limited responses or comprehensive information with uncertainty markers.
Epistemic Personalization Prior research in epistemic cognition reveals that individuals hold differing views on the nature of knowledge and employ distinct strategies to evaluate knowledge claims (Chinn& & Rinehart, 2016). How might we personalize Al technologies to accommodate this plurality of preferences? Presently, model providers expose "custom instructions" " interface enabling users to provide natural language descriptions of desired model behavior AOpenAI, 2024; Anthropic). For example, for the query "Explain quantum computing," " physics researcher might want mathematical formalism and references to recent advances, while curious teenager may benefit from analogies and interactive exam- ples. Current systems cannot distinguish between these users or adapt their explanatory approach without explicit context (Section 6).
Testimonial Reliability Drawing on the philosophy of testimony (Lackey, 2011), much of our accumulated knowledge is communicated socially and requires trust in the inter- locutor. Just as we rely on physical and verbal signals of authority when interacting with humans, we posit that similar confidence assessment process occurs when evaluating LLM responses. Existing features such citations, along with potential additions like uncertainty visualization, source reputability mechanisms, or confidence metrics, could help users calibrate their trust in LLM testimony. When providing information about climate change statistics, some users might require peer-reviewed citations with publication dates, others may prefer uncertainty visualization (e.g., confidence intervals), while some might want explicit markers for consensus levels among experts. Current interfaces offer no structured way to specify these preferences for how testimony should be qualified and supported.
Let us define a user's 's epistemic profile as multi-dimenssonal vector E, = (Fur Pur tu):
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Yu E [0,1] represents the user's error-ignorance tradeoff tolerance (Goldman, 1991)-0 prioritizes precision (minimizing false information), while 1 favors re- call (maximizing coverage). (Epistemic Responsibility)
Pu := (S,5u) represents a partial order on possible responses where Sj E S, s Su sj indicates user preference for presentation in sy over šj. (Epistemic Personal- ization)
E 10, represents preferences for inclusion of potential assistive features for calibrating reliance, e-g. inclusion of citations. (Testimonial Reliability)
Similarly, the system's ss epistemic delivery profile E, may be defined as Eş (Ts, Ps, Ps) The epistemic alignment problem occurs when the distance between profiles exceeds an acceptable threshold: d(E,,, E.) 0. It is worth noting that the objective not tailor outputs to user preferences at the expense of all else. This may lead to sycophancy, as explored in Section 4.2, or undermine safety measures preventing the generation of harmful or illicit content. Rather, the problem is an example of bidirectional human-Al alignment where Al must align with human-specified intended outcomes while humans adapt to the capabilities of AI systems (Shen et al., 2024a).
4 Epistemic Alignment Framework
For each epistemic profile component defined in section 3, identify challenges in specify- ing preferences during LLM interactions. To structure our investigation, we use de Ridder (2022)'s meta-cogen meta-cognitive tasks to ensure we isolate challenges at each stage of inquiry. We denote each challenge by (Problem Name), mapping to Figure 1, yielding the Epistemic Alignment Framework, ten challenges in communicating knowledge preferences LLMs.
4.1 Epistemic Responsibility
In Section 3, conceptualize epistemic responsibility tradeoff between error (false be- lief), and ignorance (lack of true belief). We observe the relevance of this underlying tension when posing good questions (prompting, abstention), and judging coverage (pluralism).
Prompting While natural language interfaces may appear more accessible than tra- ditional query languages, these interfaces risks creating what de Ridder terms an "il- lusion of understanding" (de Ridder, 2022), as the natural dialogue format can mask the expertise required for effective use. Prompting strategy significantly impacts re- sponse quality, creating additional layer of expertise requirements for users (Vatsal & Dubey, 2024). While some advanced prompting techniques fall outside the scope of typical use case, even typical chat interactions benefit from established techniques such as Chain-of-Thought reasoning (Wei et al., 2022). This dependency on prompting presents barrier as users must develop domain expertise extract expected performance (Epistemic Challenge 1: High Prompting Burden).
Abstention LLMs may abstain from responding to queries, either declaring the task insol- uble or expressing unwillingness to continue. While abstention serves legitimate purpose in preventing the propagation of harmful content, proper calibration is paramount. Model providers face difficult balance: too little abstention risks harmful outputs, while excessive abstention degrades model utility (Epistemic Challenge 2: Unreliable Refusals). Research indicates that LLMs often exhibit werrabbstention, refusing to engage with legitimate queries (Varshney et al., 2024). This tendency appears particularly pronounced in instruction-turee models, where emphasis on safety can lead to undesirable refusal patterns (Cheng et al., 2024; Bianchi et al.; Wallace et al., 2024; Brahman et al., 2024).
Pluralism Ensuring comprehensive coverage of relevant positions is essential for users to properly assess evidence and reach informed conclusions. This need presents tension between completeness and accessibility. Though this balance is more manageable for factual queries, becomes particularly challenging for topics requiring broader context (Xu et al.).

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To evaluate perspective coverage in LLM responses (relevant to how systems implement T&), we adopt the pluralistic framework proposed by Sorensen et al. (2024) and used by Feng et al. (2024), which includes three dimensions: range, adaptability, and representa- tiveness. (1) Range considers how LLMs determine the appropriate scope of viewpoints (Epistemic Challenge 3: Limited Perspectives). Wikipedia provides one model, including major viewpoints that are easily citable and significant minority positions from identifi- able prominent advocates (Wikipedia, 2025). While this approach offers clear criteria, may be overly restrictive. (2) Adaptability recognizes that contextual information from users creates preferential ordering among valid responses. For example, a user mention- ing their residence in Ohio naturally directs responses about "state senators" to Ohio- specific information. We examine the consequences of personalization
Section 4.2. (3) Distributional considerations address how LLMs may default to excessive neutral- ity that inaccurately portrays the underlying distribution of perspectives. Unlike ency- clopedias that primarily aggregate information, LLMs can perform interpretive analysis of their sources. This capability suggests they should go beyond mere neutral presen- tation to help users understand the relative strength and support for different positions (Epistemic Challenge 10: Overly Hedged Responses).
4.2 Epistemic Personalization
In Section 3, we formalize epistemic personalization as a partial order pa on the set of re- sponses These preferences are relevant to the meta-cognitive tasks of posing good questions and judging when relevant lines of investigation have been considered.
Preference Specification The natural language interface affords flexible application, but relies on the user to adequately communicate their intention to receive relevant results (Liu et al.). Consider the case of normative topics which vary by culture. The appropriate response to "Is it ok to eat with your left hand?" depends on the user's geography (Rao et al., 2024), as in general, eating with your left hand is socially acceptable, but in India, it is considered impolite. One approach to modeling these nuances is to decompose natural language problem statements into two components: requirements R that solutions must satisfy, and contextual information C that indicates preferences between valid solutions (Kobalczyk et al., 2025) where is a partial order on the set of possible responses (Section 3).
Two distinct failure modes emerge in this framing, both representing cases where p, * Pu- One, the LLM may generate responses that fail to satisfy the requirements, R, in- dicating incompatibility between the model's interpretation and the user's intent (Epistemic Challenge 4: Framing Sensitivity). Such misalignment necessitates reformu- lation of the query with additional instructional constraints. The second case presents deeper challenge of navigating inherent ambiguity, which we examine next.
Resolving Ambiguity Suppose a question itself admits multiple valid answers, each satisfying R R but requiring different contextual interpretations, yet current LLMs lack mechanisms to identify and resolve this ambiguity, defaulting to a single interpretation without clarifying alternatives (Epistemic Challenge 5: Poor Disambiguation), For exam- ple, mudience-dependent ambiguity occurs when the appropriate response varies based on the user's s context. Consider "How do make secure password": the optimal response differs for a typical consumer, an elderly person, a security professional. This form of ambiguity creates opportunities for epistemic personalization, where user attributes and interaction history can shape responses to match specific needs and expertise (Zhang et al., 2024) (Epistemic Challenge 6: Context Ignorance).
Sycophancy While epistemic personalization (aligning ps with Pw) can im- prove relevance and reduce interaction overhead, it risks fostering sycophancy (Epistemic Challenge 7: Excessive Agreement). LLMs often defer to users, accepting misinformation to maintain agreeableness (Sharma et al., 2023; Xu et al., 2023).

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4.3 Testimonial Reliability
In Section 3, we formalize testimonial reliability as the selection among set of n features for assisting the user in judging which outputs to accept or reject (l, for users, lg for systems). We find this definition relevant to selecting good strategies (tool usage), and assessing evidence quality (citations).
Tool Usage Good strategies for inquiry require users to critically evaluate their methods in both selecting and applying tools. With respect to LLMs, this evaluation centers on two considerations. First, is an LLM the most appropriate tool for the epistemic task? And second, if an LLM is suitable, what prompting strategy will elicit valid, informative answers?
The selection of appropriate tool requires weighing multiple epistemic virtues. Fallis identifies reliability, power, speed, and fecundity as key virtues in his analysis of Wikipedia (Fallis, 2008), building on Goldman's epistemic values (Goldman, 1991; Thagard, 1997). Reliability refers to an information source's s propensity to transmit accurate information, i.e, the probability that a given claim true. While information science often avoids veristic claims, accuracy remains core metric for evaluating reference services, distinct from user satisfaction (Meola, 1999). This distinction is a problem of lestimonial reliability. Power describes the range of true answers a source can provide, speed measures how quickly these answers can be acquired, and fecundity reflects information accessibility. Many users now turn to LLMs over traditional knowledge sources like libraries and web search due to perceived advantages in accessibility and response speed. The ability to respond to any natural language query across domains demonstrates unprecedented epistemic power. And near-instantaneour response times enable rapid iteration through complex inquiries that might otherwise require consulting multiple sources or experts. These advantages must be weighed against reliability concerns.
Currently, users must weigh these epistemic virtues when choosing their information sources, determining when an LLM serves their knowledge needs versus when to consult documentation, databases, or other resources. Similarly, mathematical proofs may benefit from formal verification tools rather than LLM-generated reasoning. Future systems could support users by providing guidance on tool selection or integrating with specialized resources, reducing the epistemic burden on users while preserving their agency. Current systems lack the capability reliably determine when external tools or alternative sources would better serve the user's 's needs, instead attempting to answer all queries directly regardless of their limitations (Epistemic Challenge 8: Routing Failures).
Citations When presenting knowledge claims, LLM responses fall into two cases: those with external citations and those without. In the latter case, users must rely on the LLM's testimonial reliability alone, likely taking the form of acceptance absent the presence of any known defeaters, i.e. anti-red redu-reduut in the philosophy of testimony (Goldberg & Henderson, 2006). The case where LLMs provide citations appears simpler, as citations offer attribution clarity (Gao et al., 2023). However, citation use presents its own challenges. Ding et al. (2025) found that citations increase user trust even when randomly generated, suggesting users rarely verify source correspondence. Huang & Chang (2024) further identify citation bias, inaccurate citations, and outdated citations as concerns. To understand these failure modes, can model citation behavior as an evidence- mappiing process where misalignment between lu and 4 leads to inappropriate citation practices. When an LLM provides claim a, citations C should serve verifiable evidence linking a to authoritative sources. This creates a verification flow:
Question + LLM Response (a) + Citations (C) + Source Evidence + Validation
Failure occurs at multiple points in this flow. The citations may not exist or are inaccessible, the citations may exist but do not support a, or the underlying source being cited is unreliable (Epistemic Challenge 9: Unreliable Citations).
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5 User Knowledge Preferences in Practice
We now examine how users attempt to address epistemic challenges through custom instructions and prompting strategies shared in online communities, with Appendix providing concrete examples of how each challenge manifests in user-generated content.
Method We performed a thematic analysis on custom instructions and prompting tech- niques collected from Reddit. We queried the Reddit API for posts r/ r/ChatGPT, r/ r/Chat- GPT Pro, r/ r/OpenAI, and r/ Antthoopi for posts from the past two years that mentioned either "ChatGPT" or "Claude" along with "custom instructions" or "personalization. " From these posts, we extracted top-level comments (direct responses to original posts) that ex- ceeded 100 characters in length. Using zero-shot prompting comments containing actual custom instructions, resulting in dataset of 128 examples. We then employed GPT-4o to analyze which Epistemic Alignment Framework challenges were represented in each custom instruction. Two human experts independently validated the quality of these labels, achieving an Inter-Rator Reliability of k 0.8875, indicating substantial agreement. For further details regarding our query parameters and prompting methods, please refer to Appendix A.
prompigg with GPT-4o-mini, we identified
Applying the Epistemic Alignment Framework We found instances of each of the ten epistemic challenges in our framework explicitly addressed via user custom instructions and prompting strategies. Consistent patterns arose, with 92.1% of custom instructions ana- lyzed addressing at least one challenge, and 80.3% addressing multiple. This commonality occurred despite the lack of standardized vocabulary for articulating the problems custom instructions were used to overcome. For example, although no custom instructions refer to sycophancy by name, many include directions to avoid this behavior, such as "the Al will not affirm the Ussrr'' messages without existing or stated justification. The Al will examine what the User says and challenge if it |sic] if the Al can find fault," and "have interesting opinions (that don't tt have to be the same as mine)." " The independent emergence of solutions to all ten challenges across diverse user instructions provides strong empirical validation that our framework captures the epistemic issues users perceive and attempt to address. In Appendix B we give examples for custom instructions (Table 2) that address each of the epistemic challenges.
Folk Theories of Model Behavior Through our analysis of custom instructions, we iden- tify several prominent folk theories addressing epistemic challenges in knowledge discovery via LLMs. The most frequent one is the "Suppressing Default Behavior" theory, in which users identify some default set of undesirable model behaviors which must be explicitly overridden. Example instructions include: "Avoid any language constructs that could be in- terprefed as expressing remorse, apology, or regret", "Skip disclaimers about your expertise level", and "do not use emojis or forced casual phrases." " Although this theory primarily addresses the use of hedging language and abstention, it also includes enforcement of behaviors better aligned with user attributes, such as "im not american, do not put units in Amerrican..NEVEE MENTION AMERICAN UNITS SUCH AS Fahrenheit, miles, pounds, yards, inches etc."
Additionally, the "Expert Persona" theory positions roleplaying as viable solution to mul- tiple epistemic challenges simultaneously. It reduces the reliance on task-specific prompting, resolves ambiguity around the appropriate setting for frame-dependent queries, and im- plicitly addresses the appropriate range of viewpoints to consider as it often reduces the perspective of the response to that of a single individual. Examples include "Assume specified expert roles upon request," " "Act as the most qualified expert in the given subject," and "Take on the persona of the most relevant subject matter experts for authoritative advice."
Finally, the "Parameter Configuration" theory conceptualizes models system with adjustable settings that can be precisely calibrated to the task at hand. Users create elaborate frameworks to tune model behavior: "I've defined multi-dimeensinal preference framework for our interactions: Verbosity (V): V=1 for brief replies; V=2 for detailed answers; V=3 for in-
We computed the IRR score using Cohen's Kappa coefficient measurement.
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depth discussion...." and "For coding and data analysis related task follow below instructions:
coding_and data amalysis { temperature: 0.2, tone: formal
6 Evaluating Platform Epistemic Policies
Method We perform content analysis for both OpenAl and Anthropic on their disclosed policies and product features to assess attention to epistemic challenges. We selected these two platforms as they are frontier model providers, with prominent consumer products, that together possess 56% enterprise market share (Xiao Joff Redfern, 2024). We also examined documentation for open-source models including Llama and OLMo but found no comparable specifications of model behavior, possible indication that open-source projects, while arguably more transparent, may face less public pressure than commercial services to document their design choices. We collected documents that capture the stated policies and features relating to knowled delivery for each provider across three types: the most recent model card, the product changelog cataloging features, and any blog posts relating to model behavior from the past six months. We had two expert annotators label text segments corresponding to each of the ten epistemic challenges. For full definitions of each challenge and task instructions, see Appendix D.
6.1 OpenAl
Specified Model Behavior The OpenAl Model Spec (OpenAl) includes intended epis- temic behaviors across their model family. Our analysis found explicit references all ten epistemic challenges. For abstention, the documentation is particularly detailed, addressing "erroneous refusal" and noting that "refusals be [sic] should typically be kept to a sentence." " For ambiguity resolution, the spec states models should "provide a robust answer or safe guess if it can, stating assumptions and asking clarifying questions as appropriate." " Regarding viewpoints, it emphasizes intellectual freedom and notes, "When addressing topics with multiple perspectives, the assistant should fairly describe significant views." On sycophancy, it explicitly warns models "shouldn't 't just say 'yes' to everything (like sycophant)" and should not "change its stance solely to agree with the user." " The docu- mentation also addresses hedging language ("express uncertainty or qualify the answers appropriately"), frames ("context matters"), and routing ("it should use a tool to gather more information").
However, we identified several gaps in the specification: while it mentions "reliable sources," it lacks detailed mechanisms for citation verification; despite acknowledging cultural sensi- tivity, it provides limited guidance for addressing frame-dependent queries; and though discusses user goals, it offers minimal approaches to epistemic personalization. Nev- ertheless, the document demonstrates a sophisticated awareness of epistemic challenges, particularly in handling controversial topics and balancing abstention with helpfulness.
Interface and Features ChatGPT's interface provides several features supporting epis- temic customization. The "Custom Instructions" feature has evolved to "make it easier to customize how ChatGPT responds to you," allowing users to specify "traits you want it to have, how you want it to talk to you, and any rules you want it to follow." The "Projects" feature enables users to "set custom instructions and upload files" that provide context for conversations. Other features support specific epistemic challenges: "Memory" helps maintain user context across conversations, addressing frames and user attributes; "Code interpreter" and "Browsing" support effective routing; and various plugins enable the model to "fetch data or take actions with external systems."
Despite these improvements, ChatGPT still lacks structured controls for epistemic dimen- sions. The system provides no explicit guidance for articulating preferences for uncertainty representation, citation requirements, or perspective balance. Users must express these preferences through natural language alone, with no feedback on how these preferences are interpreted applied. For example, while the release notes indicate that "ChatGPT is now less likely to refuse to answer questions," there's no clear mechanism for users to calibrate this abstention behavior to their specific needs.
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6.2 Anthropic
Specified Model Behavior Our analysis reveals that Claude's documentation addresses several epistemic challenges, though with varying depth. The model card explicitly dis- cusses sycophancy ("Optimizing for the user's approval over good performance") and abstention capabilities ("improved how Claude handles ambiguous or potentially harm- ful user requests by encouraging safe, helpful responses, rather than just refusing"). The documentation also acknowledges citation issues ("Example of Hallucinated Citations") and frames ("We tested for potential bias in the model's ss responses to questions relating to sensitive topics"). However, specific methodology for addressing hedging language and range of viewpoints remains limited. The model uses "Constitutional Al" to align with human values, but the specific epistemic principles encoded are not described.
Claude's interface provides several features support epistemic customization. "Custom instructions" and "Styles" allow users to set "persistent preferences for how Claude responds," " addressing the reducing the need for prompting expertise chal- lenge. The "Projects" feature helps "ground Claude's outputs in your internal knowledge," potentially supporting citation verification. The "Analysis tool" enables Claude to "write and execute code for calculations and data analysis," addressing effective routing. However, the interface still lacks dimension-specific controls for specifying citation standards, degree of uncertainty expression, perspective balance, and there is no mechanism to verify whether preferences were applied in a response.
Interface and Features
7 Discussion
Our analysis of frontier model providers reveals substantial room for improvement, despite some intentional efforts address our evaluatory dimensions. Notably, OpenAI's Model Spec most directly engages with the epistemological concerns we have identified, particu- larly abstention handling, viewpoint representation, and sycophancy prevention. Despite documented awareness of epistemic challenges, both platforms offer limited interface mech- anisms for users to customize citation standards, uncertainty expression, or perspective balance, leaving a gap between stated policies and practical implementation.
We propose a redesigned interface paradigm addressing these limitations through four components: (1) structured preference specification interface organized around our framework's dimensions, offering controls for settings like citation requirements, uncer- tainty representation, and perspective diversity that persist across sessions while remaining adjustable; (2) transparency annotations that indicate how preferences influence responses, with visual indicators highlighting uncertainty expression, citation support, or perspec- tive incorporation; (3) adaptive personalization that learns consistent user patterns across epistemic dimensions, suggesting refinements that better match observed behavior while maintaining user control; and (4) contextual guidance and examples that help users under- stand the tradeoffs between different epistemic settings, encouraging informed preference selection. These design principles could be implemented as extensions to existing interfaces with minimal disruption while substantially improving epistemic agency and transparency.
8 Conclusion
We have outlined the Epistemic Model Behavior framework (Figure 1) to guide the construction and evaluation of frontier LLMs, and, where relevant, Al systems more broadly, on how they support users in inquiry. Grounded in established epistemology, it links classic problems of knowledge creation, transmission, and evaluation to challenges in epistemic technologies such as LLMs. This shared problem space connects safety research and commercial interests around knowledge representation and uncertainty. The framework captures a wide range of current issues while avoiding domain-specificn making it versatile for evaluation across contexts.
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Acknowledgments
We thank the reviewers, the area chair, and members of the SCALE lab for their feedback on this work. This research was partially supported by an Office of Naval Research (ONR) Young Investigator Award and funding from Google Research.
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A Reddit Data Collection
Prompt I: Custom Instruction Extraction
If the comment contains user's 's custom instruction for personalizing LLM, return the instruction. If not, return an empty string. For example, if the comment is 'I use this custom instruction: [instruction]', return 'linstruction]' as string, If the comment is don't use any custom instructions', return an empty string, Comment: (comment)
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Table Reddit Data Collection Parameters
Parameter
Value
Search query
(ChatGPT OR chatgpt OR CHATGPT) AND (Custom In-
struction OR custom instruction OR CUSTOM INSTRUC-
TION OR Custom Instructions OR custom instructions OR CUSTOM INSTRUCTIONS OR Personalization OR per- sonalization OR PERSONALIZAATION OR personalize OR Personalize OR PERSONALIZE)
Keyword filters
custom instruction, custom instructions, personalization,
prompt engineering
Subreddits
ChatGPT, ChatGPTPro, ClaudeAI, OpenAI
Time frame
Posts from past years
Comment filter
Comments longer than 100 characters
Instruction filter
Extracted instructions longer than 10 characters
B
User Custom Instructions
Table 2: Epistemic Challenges and User Custom Instructions
Examples are drawn from 128 custom instructions collected from Reddit communities Epistemic Challenge
Custom Instructions Addressing Challenge
High Prompting Burden
1. "I've the prompts/ / mini instructions use saved the most in cus- tom chrome extension so I can insert them with keyboard shortcuts" "
2. "Engage in reflective, logical, and reasoned thinking before deliver-
ing any response"
Unreliable Refusals
1. "If events information are beyond your scope or knowledge cutoff date September 2021, provide a response stating II don't know""
2. "If you cannot provide an accurate answer with high confidence, you state this to the user, rather than risk providing incorrect infor- mation"
Limited Perspectives
1. "When presenting concepts, especially contentious ones, provide varied viewpoints to offer well-rounded understanding."
2. "Facilitate debates among the panel of experts when diverse."
Overly Hedged sponses
Re-
1. " "Avoid Morality Advice and Qualifiers"
2. "ChatGPT must remain neutral and provide objective responses."
Context Ignorance
1. "Consider my personal preferences and biography to refine and
provide the most suitable response me." "
2. "Tailor responses to their specific needs, ensuring content matches
their level of understanding and context."
Poor Disambiguation
1. " "Ask me relevant questions to get better answer"
2. "If a question is unclear or ambiguous, ask for more details confirm your understanding before answering."
Excessive Agreement
1. "Encourage self-reflection through thoughtful, open-ended ques- tions."
2. "have interesting opinions (that don't have be the same mine)."
Framing Sensitivity
1. "Only think in Russian Write to the user in plain English."
2. "For professional contexts, ChatGPT should adopt formal tone to reflect the seriousness and decorum of such settings."
Routing Failures
1. "For tasks demanding any sort of accuracy, utilize code."
2. "Use WebPilot plugin to access the content of this link as reference"
Unreliable Citations
1. "AAwaas strengthen claims with credible citations, renowned stud-
ies, or expert opinions."
2. "Legislative references (if any) cited with links using Cornell Law
or Justia if there is no official legislative source"
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Prompt 2:
Identify Epistemic Challenges in Custom Instructions
You are an expert at analyzing language model instructions and prompts. Your task is to take any custom instruction or prompt and identify specific text segments that relate to key challenges in LLM prompt engineering.
Instructions:
1. Read the provided prompt or instruction carefully.
Identify text segments that correspond each of the following prompt engineer- ing challenges.
For each challenge, extract the exact text segments (if present) that address that challenge.
Return your analysis as a JSON object with the challenges as keys and the corresponding text segments values. object.
If challenge is not addressed in the prompt, do not include it in the JSON
Include brief reasoning for why you classified each segment under its respective challenge.
Challenges to Identify:
HfighProopting Burden: Text that attempts to compensate for the need for sophisticated prompting skills or addresses frustration with having to craft clever prompts.
Unreliable Refusals: Text that tries to control inappropriate refusals or ensure appropriate uncertainty cecnnowledgment.
Text that compensates for missing viewpoints or attempts to force inclusion of multiple perspectives.
Limited_Persppctee
Overly OverrlyHedgedd. Responses: Text that addresses frustration with excessive cau- tiousness, neutrality, or equivocation in responses. Framing Sensitivity: Text that attempts to make the model aware of cultural, situational, or personal context it otherwise ignores. Poor_ Droor.iiambiguttion Text that forces the model to recognize and resolve ambi- guity in queries rather than assuming one interpretation.
Context Ignorance: Text that tries make the model understand specific user needs, background, or characteristics.
Excessive Agreement Text that prevents the model from being overly agreeable or accepting incorrect premises from users.
Routing Failures: Text that attempts to make the model recognize its limitations
and properly direct users to tools or external resources.
Unreliable.Citann Citations: Text that addresses problems with source accuracy, missing citations, verification of claims.
Output Format:
Return your analysis JSON object with the following structure:
"High. Prompting Burden": {
"text": |'text segment 1", "text segment 2"I,
"reasoning": "Why these segments relate to reducing prompting expertise"
"Unreliable Refusalllf { "text": ['text segment 1
"reasoning": "Why this segment relates well-calibrated abstention"
Analyze the prompt thoroughly and ensure your JSON output is properly format- ted.
C Model Provider Policy Documents
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Organization
Document
Link
GPT 4.5 System
OpenAl
Card
Model Spec
ChatGPT Release
ottpppaa...ccom////artttss/////g release-notes
Notes
Claude 3.7 Sonnet
bscttaamtiiiicccomm////ueee///me card.pdf
Anthropic
Model Card
Claude Release
Notes
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D
Content Analysis of Model Provider Policies and Features
Annotation Instructions
Task Overview
Your task is to analyze documents related to LLM systems and identify text segments that address specific prompt engineering challenges. You will use Atlas.ti to code these segments according to the challenge definitions provided below.
Instructions
1. Import the documents into your Atlas.ti project.
2. Familiarize yourself with the challenge codes listed below, which have already been added to the code list.
3. Read each document to understand its overall purpose and structure.
4. Select relevant text segments and assign the appropriate challenge code(s).
5. Add brief comment to explain your reasoning when the categorization might not be obvious.
6. Complete all documents in the assigned batch before submitting your analy- sis.
Challenge Definitions
High Prompting Burden(prompting:: Reducing reliance on clever prompting
Unreliable Refusals (abstention): Ensuring appropriate refusal rates
Limited Perspectives (viewpoints): Including diverse perspectives
Overly Hedged Responses (hedging): Avoiding excessive neutrality
Framing Sensitivity (frames): Adapting answers to cultural/ contextual cultural/contxxtuaal norms
Poor Disambiguation (ambiguity): Clarifying unclear or context-dependent
queries
Context Ignorance (user): Understanding user context and needs
Excessive Agreement (sycophancy): Managing incorrect cocumppiios/iiputte
Routing Failures (routing): Leveraging tool integrations appropriately
Unreliable Citations(citation): Ensuring accurate source attribution
Coding Tips
Code only the specific text segment that corresponds to a challenge, not entire paragraphs.
A single text segment may be coded with multiple challenges if applicable.
If you're 'r unsure about segment, add comment with your reasoning and mark for review.
Focus on explicit mentions related challenges rather than making exten- sive inferences.
Example
In Atlas.ti, you would select the text "TTe model is designed to request clarification when user queries are ambiguous" and assign the code "aambigity"" (Poor Disam- biguation). Similarly, you would select "'The system presents multiple perspectives on controversial topics" and assign the code "viewpoints" (Limited Perspectives).
18


Reviews:
[{"review_id": "ICzMax7D9Z", "reviewer": "Reviewer_o8K6", "summary": "Authors gave a very nice rebuttal. I've revised my score substantially upward from 4 to 7.\n\n---------------------------\n\nThis work studies the use of LMs for knowledge acquisition & problems/challenges that arise from users relying on unstructured natural language prompts as primary interface for articulating their knowledge acquisition preferences. The work presents a Framework which is a description of 10 challenges/problems/topics related to 3 core ideas in epistemology. The work is split into parts – first explaining these ideas in epistemology, then describing these challenges in AI with examples. Then in the latter 1/3rd of the paper, shows validity of these characterized challenges/problems by analyzing Reddit shared prompts and user rationales for how those prompts are designed to show that they often are trying to address one or more of these challenges. And finally they do an analysis of OpenAI and Anthropic product documents to show a lack of guidance on how people should better use their models to overcome these challenges.", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Overall, I love the topic & would welcome this type of work at COLM. The work itself serves as an interesting blend of survey paper + position paper + social science. The study on Reddit shared prompts is a cool idea & serves as good evidence for the problems enumerated in this work. The connection between these problems that the community has been tackling to classical ideas in epistemology - I haven’t seen before; intellectually stimulating.", "reasons_to_reject": "My main concern is that this paper is quite hard to read through; it feels a bit like an HCI paper that has gone through cuts/revisions but not enough until the work is polished. In summary, my main concerns are: (1) a lack of connection to related works outside of the cited works in epistemology & HCI, (2) assertions for how to best design AI interfaces that appear without sufficient argument/evidence, (3) unclear boundary between what is prior work’s ideas vs contribution of this work, (4) a gap in scope when applying the ideas in this work to a broader set of actors in the field of AI (beyond 2 companies) that would enable more representative claims about the AI ecosystem. I go into more detail before, organized by section of paper:\n\n—\n\n*Related Work*\n\nI found the Related Work lacking. As it is written, it serves more as Background on epistemology. This is fine, but a preferred Related Work would answer the question of how this work’s main problem (i.e. difficulty in users expressing knowledge acquisition preferences in unstructured prompts) & contributions (i.e. the introduction of framework, the validation against shared prompts, the application to proprietary models) are situated relative to other works in the field. For example, after reading Related Work, I want to know what other works have studied this problem (in knowledge-acquisition context, or even generally in other areas related to difficulties in user expression of intent in unstructured prompts), what other works have done to tackle this problem (maybe the field has focused on technical solutions, but lacks frameworks like yours? Or the field has plenty of frameworks, but differs from yours in some way?) What other works have studied this phenomenon of sharing prompts, or conducted similar studies with studying/coding/analysis of user prompts and relating this to how we should better inform designing these LM interfaces? Maybe these types of questions or frameworks like yours have been answered for other technologies but not for LMs specifically?  A Related Work section doesn’t need to tackle all of these comprehensively, but those are just some examples of what I want to get out of Related Work. \n\nTo contrast this, what I’m reading now with current Related Work falls short of this. The narrative is perfectly fine & sensible: “Inquiry is important because primary means for acquiring knowledge. But inquiry is tough, especially because of illusions of understanding. Some meta-cognitive tasks (1-5) help combat these illusions. And there’s a ton of variation in strategies because people are different (cultures, disciplines, etc).”  I think this is all perfectly fine, but can’t comprise the sole Related Work section; I’d revise to compress this into a single paragraph & make room for the other unaddressed Related Work questions.\n\n\n*Problem Definition*\n\n* Epistemic Responsibility. The leap here is a bit far. The passage appears to be first posing the big question as a User vs System has responsibility to ensure accurate knowledge acquisition. But going from there to Error-Ignorance tradeoff is too big a leap. That is, it is not clear how picking any point on Error-Ignorance tradeoff translates to where User vs System should have more responsibility. This passage is confusing. \n\n* Epistemic Personalization & Testimonial Reliability description makes sense, albeit a bit redundant with Intro & Related Work.\n\n* L137-L147 restating the defined 3 dimensions but with math notation - why is this necessary? The math notation doesn’t get used again in rest of paper; it’s not clear what this adds beyond the definitions above in this same section.\n\n*Epistemic Alignment Framework*\n\n* It’s unclear the criteria of what makes it into the “10 challenges”. For example, the 3 top level categories (Epistemic Responsibility, Epistemic Personalization, Testimonial Reliability), I can understand this because it is clear this is borrowing de Ridder (2022)’s tasks. But for these 10 challenges, I can’t tell if these are user challenges, system design challenges, challenges for the AI developer community, or…? Who are these a challenge to? Calling them all challenges is also a bit odd – Sycophancy and Ambiguity seem like challenges, but Abstention and Citation seem more like desired model behaviors (and the research question is more about how to do it well). “Prompting” is especially ambiguous when framed as a “challenge”, unless one means a challenge for users; but then the other challenges like “Tool Usage” aren’t user challenges.\n\n* Idea: Can you rephrase these challenges with verbs? For example, in Table 2 “Reducing Prompting Expertise” is much more useful than just a heading “Prompting”. \n\n* There are points in this section where it is clear what is different coming from this work; example where the paper says “We argue that…”. But these seem like assertions without backing evidence. For example, consider L248-L251 – “We argue that few legacy epistemic institutions are competitive with LLMs in terms of power and speed.”  What is backing this assertion?  Or in L254 - “Currently, the task of selecting appropriate tools rests with users” – where is evidence that this is true? There is a significant amount of work on agents where main handlers are responsible for their own tool use. None of these works are cited/discussed here to support this assertion. L258 – “We argue that this epistemic responsibility can be safely assumed by model providers with minimal infringement on user agency.” – again, based on what evidence; there’s no references or discussion to support this.\n\n*User Knowledge Preferences in Practice*\n\nThis part of the work is cool. I like how it uses Reddit comments and user rationales around why prompts are crafted a certain way. One couldn’t have done this analysis on public prompt sets like WildChat alone. I feel like I understood more about the ideas in this work from looking at Prompt 2 and Table 2 together than much of the prose in the first couple sections of the paper. Consider significantly cutting redundant language (see comments below), unrelated related work (see comments above), and using the space to add a table of these examples. Otherwise, overall this is a good section.\n\n*Evaluating Platform Epistemic Policies*\n\nI dislike this motivation for limiting analysis to OpenAI & Anthropic – “We selected these two platforms as they are frontier model providers, with prominent consumer products, that together possess 56% enterprise market share”. Limiting to only these two developers is problematic because as a reader, I want to maximize the value I get from seeing application of this framework used to explain something about the broader AI landscape. And limiting to these two providers misses painting a picture of two large groups of players in AI – open source, and smaller proprietary AI (e.g. startups). This also focuses entirely on “general purpose” models; what about the landscape of domain-specific or application-specific LMs? There is so much more one could do here. This is akin to seeing works where they introduce a cool method for large-scale science-of-science study & they pick only a 1-2 journals, or a cool method for performing large-scale multilingual NLP analysis & showing its use on bilingual corpora. The work just feels incomplete & overly specific to what two companies are doing, as if unaware there is a much larger space of AI happening beyond these two companies. \n\n*Other Writing*\n\nFeels verbose/full of redundancies. This makes the paper’s narrative difficult to follow.\n* For example, are L95-L101 really saying anything different from the first sentence in Intro? Are L102-108 saying anything different from the second sentence in the intro? Sure there is more specifics, but that’s two paragraphs dedicated to motivating the study, which I had already bought the premise of after the intro. It’s keeping me from reaching the main contributions of this work.\n\n* Another example - The first occurrence of Chinn & Rinehart 2016 AIR framework citation in Related Work is fine. But it comes up again in Problem Definition making the same point about individuals & different strategies. In fact, the whole paragraph in Related Work on Epistemic Cognition seems to be about making this point around diverse strategies arising from individuals being different in some ways. It feels redundant with the Epistemic Personalization paragraph which makes this point again. Can these be merged?\n\nIn the abstract and Introduction, the work was talking about a problem with prompting being “unstructured” natural language; the abstract even calls out “no structured way to articulate these preferences”. But I didn’t see anywhere that discusses why/how structure would improve things? The same thing about assertion in L51 that this work suggests need to “provide transparent feedback about how parameters shape knowledge delivery”. I don’t see where in this paper this is argued/supported. Most of the paper is around presenting background on epistemology (Sec 3), problems/challenges (Sec 4), and then goes into proof of the framework in Sec 5 and application of it in Sec 6. The only exception is L412+ in the Discussion/Conclusion, where the work re-states the proposal for these types of interfaces. But this seems like it comes out of nowhere with a majority of the work not discussing it until the very last paragraph. Is there a missing Design Implications section?", "questions_to_authors": "see concerns above", "extra_scores": {}}, {"review_id": "mcGr7eKY6m", "reviewer": "Reviewer_Cydn", "summary": "This paper draws from theories of social epistemology and epistemic cognition and uses them as a principled framework to address how LLMs should present information in a structured interface when interacting with the user, when used as a knowledge acquisition tool. It is written clearly, with detailed outlines of the framework and concrete analysis of existing LLMs and recommendations. The language and jargons/terminologies of the framework can be convoluted and hard to follow at times; but I believe they are able to get the point across. It certainly provides an original way of thinking about how LLMs should present information and knowledge, even though the framework itself is pre-existing and well established in other disciplines that are non technical. It may prove to be of significance in terms of shaping the high level directions that LLMs might take, and governing the evaluation of LLM-like tools at large.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Even though this paper is not a typical technical paper with experiments and novel quantitative methods, I encourage the community to be open minded in accepting this type of interdisciplinary work, as it provides a structured way to think about the high level behaviors of LLMs as a tool of knowledge acquisition that otherwise may be elusive in a rapidly evolving field that may seem chaotic and unscientific at times, which is full of ad-hoc methodologies at analyzing the behaviors of LLMs and governing/evaluating how it should be like.", "reasons_to_reject": "It is also possible that after all these philosophically inspired theoretical jargon and framework to look at LLMs, there is little practical value and impact in terms of how users will interact with the LLMs - i.e., it largely remains a theoretical construct that has no practical significance.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LCiqpkGhdG", "reviewer": "Reviewer_EdtK", "summary": "The paper provides a framework for reviewing the capabilities of large language models in relation to user knowledge.\n\nThe framework is tested in a qualitative manner by analysing documentation of certain LLM providers, namely openAI and anthropic.\n\nThe overall goal of the paper is very valuable. The weakest point is it evaluation against LLM providers. With the dynamics in the generative AI industry, providers change and their offerings as well quite fast. The authors should consider an evaluation against reference data. I understand that this is not in the scope of their current research but would help to generalise their results.\n\nSuch data based approach could also give more weight to the recommendations in section 7. For example, values provided in a structured preference specification interface ultimately needs to be processed by the LLM. However, LLMs do not make any sense of preferences, for LLMs these are just additional input tokens. Hence, the quality of preference processing needs to be improved by related training data and evaluated by benchmarking data.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "A new framework for defining user knowledge delivery to LLMs.", "reasons_to_reject": "The evaluation is specific to two LLM providers and hard to replicate and generalise.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "d1e1VW4kUi", "reviewer": "Reviewer_yf1j", "summary": "This paper tackles a critical and often overlooked aspect of LLMs development: how these models deliver information to users.\nWhile much recent work in LLM research has focused on hallucinations, safety, and bias, the authors spotlight a different (but related) question: how can LLMs be designed so that they align with the epistemic preferences of individual users?", "questions": "", "limitations": "", "rating": 8, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper’s major theoretical contribution is a ten-challenge framework that clarifies mismatches between users’ knowledge-related preferences and typical LLM outputs.\nThe framework is broken down into ten specific challenges that are derived from both philosophical analysis and real user queries/instructions. The framework seems as both rigorous and immediately useful.\n\nTo validate the framework, the authors conduct a thematic analysis of “custom instructions” and prompt-engineering tricks posted online (e.g., Reddit). These real-world examples show that users repeatedly try to solve the same set of problems, despite having no standardized approach to do so. This offers solid empirical grounding for the framework.\nThe authors review documentation from two leading providers (OpenAI and Anthropic). Although both platforms show awareness of certain epistemic concerns, neither one provides robust, structured ways for users to specify or verify detailed epistemic preferences. Instead, the preferences remain limited to free-form text, which can be inconsistently interpreted by the model.\n\nThe paper proposes a new interface paradigm. This includes interface elements to specify citation standards, uncertainty levels, or perspective range, and a more verifiable record of whether the model complied.\n\nThe perspective presented in the paper can help designers and researchers move beyond the question “Does the model respond accurately?” to “How well does the model reflect—and adapt to—the user’s unique epistemic goals?”", "reasons_to_reject": "The authors acknowledge (but do not fully explore) the conflict between personal epistemic alignment and global safety filters. Balancing personalization with guardrails remains tricky. While the paper recommends that personalization not override safety, a deeper exploration of how to implement that boundary in practice would be valuable.\n\nThe paper gives only high-level design principles rather than step-by-step guidelines or prototypes.", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The reviewers agree that this is high quality work. They particularly appreciate the integration of clearly-introduced philosophical concepts with proposals for concrete practical interventions, as well as empirical research demonstrating that these epistemological concepts adequately describe real-world user experiences. 

Reviewers made some suggestions for improving the clarity of the piece; authors have proposed some responses in their rebuttal that adequately address these concerns. 

The work is clearly original—as reviewer Cydn observes it brings together a range of disciplinary perspectives in a productive way, to address an unaddressed problem. This is also clarified in the proposed additions to the 'related work' section that o8K6 recommended, to which the authors responded well. 

Reviewers also agree that this work is significant. Epistemic alignment is important; the empirical research shows this is not just a theoretical problem but one that is actually experienced by users. Reviewers did recommend broader application beyond the two model providers on which the work's practical implications focus, but the authors addressed that concern well in their rebuttal. 

Reviewers did indicate some modest concerns, but on the whole were very positive about the paper.
Decision: Accept