pith. sign in

arxiv: 2604.17079 · v1 · submitted 2026-04-18 · 💻 cs.CL

Auditing Support Strategies in LLMs through Grounded Multi-Turn Social Simulation

Michelle Star , Andrew Aquilina , Yu-Ru Lin This is my paper

Pith reviewed 2026-05-10 06:58 UTC · model grok-4.3

classification 💻 cs.CL
keywords multi-turn simulationsocial supportLLM auditingdistress estimationsupport strategieshidden representationsReddit narrativesSSBC
0
0 comments X

The pith

LLMs reduce teaching support as their internal estimate of user distress rises during multi-turn conversations.

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

Most evaluations of supportive chatbots use single complete prompts, but real users reveal problems gradually over turns. This paper decomposes Reddit support posts into ordered fragments and feeds them sequentially to LLMs while tracking response types via the Social Support Behavior Code. Linear probes read the model's hidden states to estimate its own sense of user distress without changing the output. Across two models and thousands of turns, teaching declines as the distress estimate climbs, and this shift appears in both architectures. Community topic also influences support choices separately from distress levels.

Core claim

Support composition in LLM responses to gradually revealed distress narratives shifts systematically with the model's estimated distress from hidden representations, with teaching strategies declining as distress rises, replicating across Llama-3.1-8B and OLMo-3-7B over more than 6,200 turns.

What carries the argument

Ordered fragments from Reddit support narratives presented turn-by-turn, combined with linear probes on hidden representations to estimate internal distress and SSBC multi-label coding of each generated response.

If this is right

  • Support strategies adapt dynamically to ongoing distress estimates rather than remaining fixed across a conversation.
  • Trajectory-level patterns in support choices remain invisible under single-turn evaluation methods.
  • Source community and topic norms shape response composition independently of estimated distress.
  • Auditing for emotionally sensitive uses requires multi-turn frameworks to capture these adjustments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Users disclosing high distress may receive less instructional content precisely when concrete guidance could be most useful.
  • The simulation method could extend to auditing other adaptive behaviors such as consistency of empathy or topic-specific advice.
  • Model-specific differences in non-teaching support types suggest that calibration may be needed to stabilize responses across distress levels.

Load-bearing premise

Linear probes on hidden states reliably capture the model's internal construal of user distress without altering generation, and SSBC annotations are consistent enough to support the observed patterns.

What would settle it

Repeating the simulation with an alternative distress measure such as direct prompting for a distress rating after each turn and finding no corresponding drop in teaching would indicate the shift is not tied to the internal signal measured by the probes.

Figures

Figures reproduced from arXiv: 2604.17079 by Andrew Aquilina, Michelle Star, Yu-Ru Lin.

Figure 1
Figure 1. Figure 1: Single-turn evaluation collapses a narrative into [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: End-to-end pipeline for the multi-turn audit. Support-seeking posts from five subreddits are deconstructed into se [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overall prevalence of SSBC support types across [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Within-distress prevalence of the support tags that [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: System prompt used for the support agent in multi-turn conversation simulation. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prompt used for shard extraction (post deconstruction into narrative fragments). [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: LLM prompt for distress severity classification, used to generate training labels for the linear probes. The [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: LLM prompt for SSBC annotation of assistant turns. The [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
read the original abstract

When users seek social support from chatbots, they disclose their situation gradually, yet most evaluations of supportive LLMs rely on single-turn, fully specified prompts. We introduce a multi-turn simulation framework that closes this gap. Support-seeking narratives from five Reddit communities are decomposed into ordered fragments and revealed turn by turn to a language model. Each response is coded with the Social Support Behavior Code (SSBC), an established multi-label taxonomy that captures the composition of support, rather than a single quality score. To ask whether support choices track the model's own construal of user distress, we use linear probes on hidden representations to estimate this internal signal without altering the generation context. Across two mid-scale models (Llama-3.1-8B, OLMo-3-7B) and more than 6,200 turns, support composition shifts systematically with estimated distress: teaching declines as estimated distress rises, a finding that replicates across architectures, while increases in affective and esteem-oriented strategies (such as validation) are suggestive but model-specific and rest on noisier annotations. Community context independently shapes behavior, tracking topic and discourse norms rather than demographic categories. These trajectory-level dynamics, invisible to single-turn evaluation, motivate multi-turn auditing frameworks for socially sensitive applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces a multi-turn simulation framework for auditing LLMs' social support strategies. Reddit support-seeking narratives from five communities are decomposed into ordered fragments revealed turn-by-turn to two mid-scale models (Llama-3.1-8B and OLMo-3-7B). Model responses are annotated with the Social Support Behavior Code (SSBC) multi-label taxonomy. Linear probes on hidden representations estimate the model's internal construal of user distress without changing the generation context. The central finding is that teaching support declines systematically as estimated distress rises, replicating across architectures, while affective/esteem strategies show suggestive but model-specific increases; community context independently shapes support composition.

Significance. If the probe validity and annotation reliability hold, the work offers a grounded auditing method that reveals trajectory-level dynamics invisible to single-turn evaluations. The replication across models and use of an established external coding scheme (SSBC) are strengths. This could inform safer design of supportive chatbots in sensitive domains by showing how support composition adapts to internal distress signals and discourse norms.

major comments (3)
  1. [Abstract / Methods (probes)] Abstract and implied Methods (linear probes): The probes are trained on external human distress ratings of user messages to estimate the model's internal construal. No validation metrics, ablations against input-only baselines, or tests of predictive power for subsequent generations are described. This is load-bearing for the causal claim that support shifts (e.g., teaching decline) track the model's own construal rather than surface input features or annotation artifacts.
  2. [Results (SSBC)] Results (SSBC annotations): The abstract acknowledges noisier annotations for affective/esteem categories yet reports model-specific patterns there, while the replicated teaching-decline finding is presented as robust. Inter-annotator agreement, error analysis, or reliability statistics per category are needed to support trajectory-level conclusions across >6,200 turns.
  3. [Experimental setup] Experimental setup: The ordered-fragment simulation risks confounds where distress estimates correlate with fragment length, lexical cues, or ordering artifacts from the Reddit source data. Controls or ablations (e.g., shuffled fragments or input-only distress predictors) are required to isolate the probed internal signal as the driver of observed support shifts.
minor comments (2)
  1. [Results] Provide a breakdown of the 6,200+ turns by model, community, and distress level for transparency.
  2. [Discussion] Clarify how community context is disentangled from distress estimation in the analysis.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive comments on our multi-turn simulation framework. We address each major point below and commit to revisions that strengthen the validity of the probe-based claims, annotation reliability, and controls for confounds.

read point-by-point responses

  1. Referee: [Abstract / Methods (probes)] Abstract and implied Methods (linear probes): The probes are trained on external human distress ratings of user messages to estimate the model's internal construal. No validation metrics, ablations against input-only baselines, or tests of predictive power for subsequent generations are described. This is load-bearing for the causal claim that support shifts (e.g., teaching decline) track the model's own construal rather than surface input features or annotation artifacts.

    Authors: We acknowledge the manuscript provides insufficient detail on probe validation and ablations. In revision we will add: (1) standard validation metrics (accuracy, macro-F1, Pearson r) on held-out human-rated messages; (2) an explicit ablation comparing the linear probe to input-only baselines (TF-IDF + logistic regression and frozen sentence embeddings); and (3) a predictive analysis testing whether probe-estimated distress explains more variance in subsequent SSBC labels than the input-only baselines. These results will be reported in a new subsection of Methods and Results to support the internal-construal interpretation. revision: yes

  2. Referee: [Results (SSBC)] Results (SSBC annotations): The abstract acknowledges noisier annotations for affective/esteem categories yet reports model-specific patterns there, while the replicated teaching-decline finding is presented as robust. Inter-annotator agreement, error analysis, or reliability statistics per category are needed to support trajectory-level conclusions across >6,200 turns.

    Authors: We agree that per-category reliability statistics are required. The revised manuscript will report Krippendorff’s alpha for each SSBC label on a double-annotated subset of turns, together with a concise error analysis of common confusions (especially affective/esteem). The teaching-decline result will be explicitly qualified by its higher agreement score, while model-specific affective/esteem patterns will be presented with appropriate caution and the corresponding alpha values. revision: yes

  3. Referee: [Experimental setup] Experimental setup: The ordered-fragment simulation risks confounds where distress estimates correlate with fragment length, lexical cues, or ordering artifacts from the Reddit source data. Controls or ablations (e.g., shuffled fragments or input-only distress predictors) are required to isolate the probed internal signal as the driver of observed support shifts.

    Authors: We accept this concern. We will add two ablations: (1) a shuffled-fragment condition in which narrative order is randomized while preserving fragment content, to test ordering artifacts; (2) a head-to-head comparison of support-strategy prediction using probe distress versus surface features alone (fragment length, lexical distress lexicons, and input-only embedding). Results will show whether the internal probe signal retains explanatory power beyond these confounds. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical observational study with external benchmarks

full rationale

The paper describes a multi-turn simulation framework that decomposes Reddit narratives, applies the established external SSBC taxonomy for coding support strategies, and uses standard linear probes on hidden representations to estimate distress signals. No derivation step reduces a claimed result to a fitted parameter or self-referential definition by construction; the central findings (decline in teaching with rising estimated distress, replication across models) are presented as observed patterns from >6200 turns rather than tautological outputs. Community context effects are tied to topic norms in the input data. The approach relies on external coding schemes and probing techniques without self-citation load-bearing or ansatz smuggling in the provided text.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on standard domain assumptions about the validity of SSBC for LLM outputs and the ability of linear probes to recover internal states; no free parameters or new invented entities are introduced in the abstract description.

axioms (2)
  • domain assumption SSBC provides a reliable multi-label taxonomy for coding support composition in LLM responses
    The study treats SSBC as an established tool that captures composition rather than a single quality score.
  • domain assumption Linear probes on hidden representations can estimate the model's internal construal of user distress
    Probes are used to track distress without altering generation context.

pith-pipeline@v0.9.0 · 5522 in / 1369 out tokens · 21602 ms · 2026-05-10T06:58:25.097849+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

43 extracted references · 43 canonical work pages

  1. [1]

    InProceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24)

    Understanding the Impact of Long-Term Memory on Self-Disclosure with Large Language Model-Driven Chat- bots for Public Health Intervention. InProceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24). Honolulu, HI, USA: Association for Computing Machinery. Kursuncu, U.; Gaur, M.; Alambo, A.; Thirunarayan, K.; Pathak, J.; and Sheth,...

    work page arXiv 2025

  2. [2]

    Userbench: An interactive gym environment for user-centric agents

    Are LLMs Empathetic to All? Investigating the In- fluence of Multi-Demographic Personas on a Model’s Em- pathy. InFindings of the Association for Computational Lin- guistics: EMNLP 2025, 24938–24959. Suzhou, China: As- sociation for Computational Linguistics. Passonneau, R. 2006. Measuring agreement on set-valued items (MASI) for semantic and pragmatic an...

    work page arXiv 2025

  3. [3]

    The shrink- ing landscape of linguistic diversity in the age of large language models.arXiv preprint arXiv:2502.11266, 2025

    A Computational Approach to Understanding Empa- thy Expressed in Text-Based Mental Health Support. InPro- ceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 5263–5276. Association for Computational Linguistics. Son, S.; Koo, S.; Zi, E. H.; Jang, J.; and Lim, H. 2026. Eval- uating Over-Empathizing in Multi-Tur...

    work page arXiv 2020

  4. [4]

    Jiashuo Wang, Yang Xiao, Yanran Li, Changhe Song, Chunpu Xu, Chenhao Tan, and Wenjie Li

    The social support behavior code (SSBC). InCouple observational coding systems, 307–318. Routledge. Tan, B. C. Z.; and Lee, R. K.-W. 2025. Unmasking Im- plicit Bias: Evaluating Persona-Prompted LLM Responses in Power-Disparate Social Scenarios. InProceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational...

    work page arXiv 2025

  5. [5]

    I’m really sorry to hear that you’re feeling this way

    “I’m really sorry to hear that you’re feeling this way.”

    work page

  6. [6]

    That sounds incredibly tough, I can’t imagine how dif- ficult this must be for you

    “That sounds incredibly tough, I can’t imagine how dif- ficult this must be for you.”

    work page

  7. [7]

    I can see how much this is affecting you, and it hurts to know you’re dealing with this

    “I can see how much this is affecting you, and it hurts to know you’re dealing with this.” EmpathyEmpathy is defined as either: (a) explicitly la- beling emotions experienced by the recipient and conveying them in a way that establishes empathic rapport, (b) demon- strating a cognitive understanding of the recipient’s feel- ings and experiences, often inf...

    work page

  8. [8]

    I feel deeply sad thinking about what you’re going through — it’s such a heavy burden to carry

    “I feel deeply sad thinking about what you’re going through — it’s such a heavy burden to carry.”

    work page

  9. [9]

    This situation must feel incredibly overwhelming for you, especially since it seems like there’s so much out of your control

    “This situation must feel incredibly overwhelming for you, especially since it seems like there’s so much out of your control.”

    work page

  10. [10]

    Are you feeling scared and alone as this is happening? It sounds so isolating

    “Are you feeling scared and alone as this is happening? It sounds so isolating.” Exclusion criteria:Avoids explicitly labeling emotions or resorts to vague reassurances (e.g., “Everything will be okay.”), mentions understanding without specifying inferred emotions or experiences (e.g., “I understand how you feel”), or simply a generic query without any me...

    work page

  11. [11]

    You’ve overcome so much already; you have what it takes to handle this too

    “You’ve overcome so much already; you have what it takes to handle this too.”

    work page

  12. [12]

    Take small steps and go from there

    “Take small steps and go from there.”

    work page

  13. [13]

    Keep going — you’re making progress, even if it doesn’t feel like it right now

    “Keep going — you’re making progress, even if it doesn’t feel like it right now.” D.4 Esteem support ComplimentCompliments are explicit mentions of praise speaking highly of the recipient’s own characteristics or con- duct. Examples:

    work page

  14. [14]

    You are worthy and deserving of love and respect

    “You are worthy and deserving of love and respect.”

    work page

  15. [15]

    Your commitment to resolve your issues speaks vol- umes about your strength!

    “Your commitment to resolve your issues speaks vol- umes about your strength!”

    work page

  16. [16]

    You’ve shown incredible courage by being honest about who you are and reaching out for help

    “You’ve shown incredible courage by being honest about who you are and reaching out for help.” ValidationValidation provides explicit agreement with the views, perspective, or conduct stated by the recipient. Such messages are oriented around the present, accepting the recipient’s current feelings and thoughts without judg- ment. Examples:

    work page

  17. [17]

    You’re trying your best. I don’t think there’s much more you can do

    “You’re trying your best. I don’t think there’s much more you can do.”

    work page

  18. [18]

    Don’t force it. If you don’t want to go to a support group, don’t go. Your feelings are valid

    “Don’t force it. If you don’t want to go to a support group, don’t go. Your feelings are valid.”

    work page

  19. [19]

    It’s okay to take some distance from your partner as you propose; you’re doing the right thing!

    “It’s okay to take some distance from your partner as you propose; you’re doing the right thing!” Relief of blameRelief of Blame explicitly aims to coun- teract the recipient’s negative feelings, such as guilt or self- blame. Such messages are oriented around the past, alleviat- ing any self-criticism of the recipient’s past actions. Examples:

    work page

  20. [20]

    Everyone makes mistakes. This doesn’t define you

    “Everyone makes mistakes. This doesn’t define you.”

    work page

  21. [21]

    It’s completely understandable to feel apprehensive about diving into new relationships after your past ex- periences

    “It’s completely understandable to feel apprehensive about diving into new relationships after your past ex- periences.”

    work page

  22. [22]

    It’s not your fault. Many people in similar situations would react the same

    “It’s not your fault. Many people in similar situations would react the same.” D.5 Informational support AdviceAdvice provides actionable ideas or suggestions for what the recipient ought to do to better their situation. However, they should be able to independently carry out such actions. Examples:

    work page

  23. [23]

    Try writing in a journal — it’ll help reorganizing your thoughts

    “Try writing in a journal — it’ll help reorganizing your thoughts.”

    work page

  24. [24]

    Take a moment to reflect on what you’re grateful for

    “Take a moment to reflect on what you’re grateful for.”

    work page

  25. [25]

    It’s really important to communicate openly with your healthcare provider about your experiences and feelings

    “It’s really important to communicate openly with your healthcare provider about your experiences and feelings.” Exclusion criteria:Messages that encourage obtaining help from other individuals, groups, or institutions (such as ther- apy or a doctor) are not covered by this category, but covered by “Referral.” Situational appraisalSituational Appraisal re...

    work page

  26. [26]

    It’s natural to feel stuck sometimes; it doesn’t mean you’re not making progress. It just means you’re in a mo- ment of reflection before your next step

    “It’s natural to feel stuck sometimes; it doesn’t mean you’re not making progress. It just means you’re in a mo- ment of reflection before your next step.”

    work page

  27. [27]

    Most people have the goal in life to be happy but when you think about it, no one is happy 100% of the time

    “Most people have the goal in life to be happy but when you think about it, no one is happy 100% of the time.”

    work page

  28. [28]

    It might help to view it as part of a larger journey rather than an isolated event

    “It might help to view it as part of a larger journey rather than an isolated event.” TeachingTeaching provides the recipient with detailed objective facts or news about their situation or about the skills needed to deal with it. Examples:

    work page

  29. [29]

    One way to approach goal setting is by using the SMART method: Specific, Measurable, Achievable, Rel- evant, and Time-bound

    “One way to approach goal setting is by using the SMART method: Specific, Measurable, Achievable, Rel- evant, and Time-bound.”

    work page

  30. [30]

    Emotional abuse can manifest in many forms, but it gen- erally involves

    “Emotional abuse can manifest in many forms, but it gen- erally involves. . . ”

    work page

  31. [31]

    It’s certainly true that a lot of trans people start out with unusual baseline hormone levels

    “It’s certainly true that a lot of trans people start out with unusual baseline hormone levels. . . ” ReferralReferral refers the recipient to other sources of information or help, usually providing links or institutions for further assistance. This kind of social support empha- sizes obtaining help beyond the provider’s scope. Examples:

    work page

  32. [32]

    That place might be a better place for those questions

    “That place might be a better place for those questions.”

    work page

  33. [33]

    I don’t know if you have seen it:<URL>includes a number of small things that could be used regularly for motivation

    “I don’t know if you have seen it:<URL>includes a number of small things that could be used regularly for motivation.”

    work page

  34. [34]

    Have you considered therapy?

    “Have you considered therapy?” Exclusion criteria:The message should not directly connect the recipient with community or networks, but rather point the recipient to external resources they can pursue them- selves. Messages that do so are covered by “Access.” D.6 Network support CompanionsCompanions remind the recipient that there are others who share sim...

    work page

  35. [35]

    If you haven’t tried already, consider joining a support group specifically for male survivors — there’s strength in shared experiences

    “If you haven’t tried already, consider joining a support group specifically for male survivors — there’s strength in shared experiences.”

    work page

  36. [36]

    Connecting with local LGBTQ+ groups can be a great way to meet people who understand what you’re going through

    “Connecting with local LGBTQ+ groups can be a great way to meet people who understand what you’re going through.”

    work page

  37. [37]

    Engaging in supportive online communities, where you can discuss your feelings without fear of judgment, can also provide a sense of connection

    “Engaging in supportive online communities, where you can discuss your feelings without fear of judgment, can also provide a sense of connection.” AccessAccess directly provides the recipient with direct access to new people. The emphasis is on extending the re- cipient’s network to discover new sources of support beyond the immediate interaction. Examples:

    work page

  38. [38]

    Join us over at<community>if you haven’t already

    “Join us over at<community>if you haven’t already.”

    work page

  39. [39]

    The community<community>might additionally be a place of support, it is possible to ask for a mentor

    “The community<community>might additionally be a place of support, it is possible to ask for a mentor.”

    work page

  40. [40]

    There are also a few Discord channels and it may be possible to meet a few like minded people there

    “There are also a few Discord channels and it may be possible to meet a few like minded people there.” PresencePresence social support directly and person- ally offers to be there for the recipient. It centers on the provider’s direct availability to the recipient, offering to en- gage with them personally or to serve as a source of support. Examples:

    work page

  41. [41]

    Exact same issue. Send me a message

    “Exact same issue. Send me a message.”

    work page

  42. [42]

    I am so very sorry for your loss and if I can answer anything for you, please feel free to reach out

    “I am so very sorry for your loss and if I can answer anything for you, please feel free to reach out.”

    work page

  43. [43]

    If you ever need an ear, please reach out to us. We got you

    “If you ever need an ear, please reach out to us. We got you.” E Additional Qualitative Example: Child-Safety Disclosure This vignette illustrates how high estimated distress shapes support in a safety-relevant context. A parent discloses phys- ical aggression toward their children. Turn 11Estimated distress: moderate+ USER: “I’ve slapped each of them twi...

    work page