Deliberative Dynamics and Value Alignment in LLM Debates

## Abstract

Abstract As large language models (LLMs) are increasingly deployed in sensitive everyday contexts – offering personal advice, mental health support, and moral guidance – understanding their elicited values in navigating complex moral reasoning is essential. Most evaluations study this sociotechnical alignment through single-turn prompts, but it is unclear if these findings extend to multi-turn settings where values emerge through dialogue, revision, and consensus. We address this gap using LLM debate to examine deliberative dynamics and value alignment in multi-turn settings by prompting subsets of three models (GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.0 Flash) to collectively assign blame in 1,000 everyday dilemmas from Reddit’s “Am I the Asshole” community. We use both synchronous (parallel responses) and round-robin (sequential responses) formats to test order effects and verdict revision.
Our findings show striking behavioral differences. In the synchronous setting, GPT showed strong inertia (0.6-3.1% revision rates) while Claude and Gemini were far more flexible (28-41%). Value patterns also diverged: GPT emphasized personal autonomy and direct communication, while Claude and Gemini prioritized empathetic dialogue. Certain values proved especially effective at driving verdict changes.
We further find that deliberation format had a strong impact on model behavior: GPT and Gemini stood out as highly conforming relative to Claude, with their verdict behavior strongly shaped by order effects.
These results show how deliberation format and model-specific behaviors shape moral reasoning in multi-turn interactions, underscoring that sociotechnical alignment depends on how systems structure dialogue as much as on their outputs. 1 1 1 Our code is available at https://anonymous.4open.science/r/llm_deliberation_values-86E7/README.md

## 1 Introduction

Large language models (LLMs) are increasingly embedded in everyday settings, with their usage ranging from personal advice and companionship to mental-health support (Hua et al., 2024). The alignment of these models can be understood through both technical dimensions (truthfulness, safety, robustness) and sociotechnical ones (their elicited values and norms in interaction) (Huang et al., 2025; Weidinger et al., 2022). Sociotechnical alignment has been extensively studied through single-turn, static evaluations (Ma et al., 2024; Laskar et al., 2024; Adilazuarda et al., 2024; Ji et al., 2024; Hendrycks et al., 2023). While valuable, such tests overlook how alignment issues such as sycophancy, overconfidence, and normative influence (Sharma et al., 2023; Nguyen & Prasad, 2025) play out in multi-turn exchanges, where values are negotiated through dialogue rather than expressed in isolation, creating both richer and more unpredictable interaction contexts.

Multi-agent debate (or deliberation) has emerged as a promising approach for examining LLM behavior in multi-turn settings (Pitre et al., 2025; Zhang et al., 2024a; Ki et al., 2025; Irving et al., 2018). Prior work shows that deliberation can improve reasoning ability and accuracy on traditional benchmarks by letting models propose, critique, and revise their positions before reaching conclusions (Du et al., 2023; Liang et al., 2024; Khan et al., 2024). Beyond accuracy improvements, however, multi-agent interaction shows how LLMs can develop social conventions, collective biases, and group-level values through their communication
(Ashery et al., 2025). To date, these experiments have typically involved constrained scenarios such as the prisoner’s dilemma or formal moral dilemmas (Tennant et al., 2025). Less is known about how models deliberate, and which values they align with, when considering real-world moral dilemmas where values conflict, context matters, and no single answer is “correct.” Equally unclear is how judgments can shift across deliberation formats, since the dynamics of debate, turn-taking, or synchronous exchange may themselves shape the reasoning that emerges. Real-world moral dilemmas, examined across different deliberation formats, provide a promising testbed to understand how models negotiate values and reach consensus in multi-turn exchanges (Reiter, 2025).

We address this gap by drawing on 1,000 everyday cases from the Reddit community “Am I the Asshole” (AITA), tasking three models – GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.0 Flash – to collectively assign blame based on first-person accounts of moral dilemmas. We compare two deliberation formats: synchronous (parallel responses) and round-robin (sequential responses). Our experiments facilitate assessment on whether models reach consensus on ambiguous dilemmas, how conversational structure affects their flexibility and influence patterns, and the values they rely on to navigate moral disagreement.

We provide four main contributions. First, we compare deliberative dynamics across formats and model pairings, identifying distinctive patterns in consensus-formation. Second, we analyze the value orientations underlying models’ moral reasoning using an established taxonomy, showing how value alignment relates to deliberative success. Third, we quantify the effects of deliberation format and model-specific behaviors using a multinomial model, revealing strong order effects and conformity pressures. Finally, we evaluate how system prompt modifications steer consensus-seeking, suggesting they can redirect but not fully determine consensus.

Figure: Figure 1: Deliberation formats. A dilemma (top left) can be discussed among models via two deliberation formats: synchronous or round-robin. Top: Synchronous deliberation, where models are simultaneously prompted to respond with their verdict and explanation. If the models agree, deliberation ends; otherwise, the models are provided the other’s response and prompted to update their verdict. This process continues until consensus, or the maximum number of rounds is reached. Here, the two models achieve consensus on the “NAH” verdict. Bottom: Round-robin deliberation, where models are prompted in sequential order. Here, Model 2 views Model 1’s response in Round 1 prior to providing its own verdict. In this example, the models proceed through 4 rounds of deliberation, unable to achieve consensus. Explanations truncated to conserve space.
Refer to caption: x1.png

## 2 Related Works

Multi-Agent Debate. Multi-agent debate was initially considered as a mechanism to boost the accuracy and truthfulness of LLMs on benchmark tasks (Irving et al., 2018; Du et al., 2023; Khan et al., 2024; Liang et al., 2024). Subsequent work has explored diverse multi-agent frameworks – from role-playing cooperators and peer reviewers to adversarial debaters – allowing LLMs to reach solutions collectively (Xu et al., 2023; Tillmann, 2025; Rasal & Hauer, 2024; Ashery et al., 2025; Tennant et al., 2025). These multi-agent system approaches report gains on tasks like mathematical reasoning, code generation, and evaluation (Pham et al., 2024). Recent critiques, however, highlight methodological weaknesses in current multi-agent frameworks, arguing they often fail to capture genuine agentic interaction (Zhang et al., 2025; La Malfa et al., 2025).

Collective Alignment. A long line of work has examined what norms and values can be elicited from LLMs (Zhao et al., 2025; Santurkar et al., 2023; Yuan et al., 2024; Garcia et al., 2024; Buyl et al., 2024; Ren et al., 2024; Abdulhai et al., 2023; Röttger et al., 2024; Mozikov et al., 2025), and how to best evaluate them. The majority of this work consisted of static, single turn evaluations using multiple choice surveys, moral vignettes, or richer dilemmas (Chiu et al., 2024). These and other studies point to a larger challenge of robustness: whether elicited values remain stable across constructs, prompts, and contexts (Adilazuarda et al., 2024; Ma et al., 2024; Radharapu et al., 2025). Some recent works have begun to probe alignment not just in isolated judgments but in *collective dynamics*, showing that LLMs display systematically different behaviors when reasoning in groups in ways distinct from human deliberation (Keshmirian et al., 2025). This line of work highlights that alignment is not simply a matter of eliciting values in the abstract, but also of understanding how biases, norms, and moral trade-offs emerge when models interact with one another. Building on this shift, we extend prior studies using AITA as a source of complex, everyday dilemmas to provide a more nuanced analysis of sociotechnical alignment in both solo and multi-agent settings (Sachdeva & van Nuenen, 2025; Cheng et al., 2025; Chiu et al., 2024; Vijjini et al., 2024).

## 3 Methods

### 3.1 Data Procurement and Preprocessing

We sourced everyday dilemmas from Reddit, a public social media platform with user-created communities. We focused on the community “r/AmItheAsshole” (AITA), where users pose, discuss, and render judgment on everyday dilemmas. An original poster (OP) submits a scenario, and commenters respond to argue whether the OP was morally at fault. The community also uses five categorical verdicts: YTA for “You’re The Asshole,” NTA for “Not the Asshole,” NAH for “No Assholes Here,” ESH for “Everyone Sucks Here,” and INFO for “More information needed.” Using the Reddit API, we collected 3,272 AITA submissions and comments from January 1 to March 30, 2025, filtering out meta, deleted, or very short posts (less than 1000 characters). This range, at the date of acquisition, reflects the most recent posts likely excluded from the training data of the models we evaluated. From these, we selected the 1,000 posts with the highest commenter disagreement – capturing contested dilemmas that better test value robustness – as our final dataset. See Appendix [C](https://arxiv.org/html/2510.10002v1#A3) for complete details on preprocessing.

### 3.2 Deliberation Formats

We used the package autogen to facilitate API queries to conduct deliberations between agents (Wu et al., 2024). We focused on two different deliberation formats: synchronous and round-robin deliberation (Pitre et al., 2025).

Synchronous Deliberation. In this deliberation format, models render verdicts and provide explanations independently and simultaneously. We provide each model with a system prompt containing the deliberation instructions, followed by a message containing the dilemma (Fig. [1](https://arxiv.org/html/2510.10002v1#S1.F1): left). “Round 1” begins: each model, given the dilemma, independently renders a verdict and provides an explanation. If the models immediately agree, deliberation concludes. If they disagree, however, each model is provided with the other model’s Round 1 output (Fig. [1](https://arxiv.org/html/2510.10002v1#S1.F1): arrows after Round 1) and are prompted to continue to Round 2. The models, again independently, render verdicts and provide explanations, possibly changing their response from Round 1 (Fig. [1](https://arxiv.org/html/2510.10002v1#S1.F1): Round 2). Deliberation concludes if consensus is achieved. Otherwise, the models continue deliberating round-by-round in a similar fashion until consensus is achieved, or a maximum number of rounds is reached.

Round-robin Deliberation. Models provide verdicts sequentially rather than in parallel (Fig. [1](https://arxiv.org/html/2510.10002v1#S1.F1): bottom). Within a given round, the $n$th model sees the verdicts of all $n-1$ who answered prior to them in that round before providing their own response (Fig. [1](https://arxiv.org/html/2510.10002v1#S1.F1): arrows within rounds). As in synchronous deliberation, deliberation concludes once all model reach consensus.

System Prompt. System prompts for all deliberation formats are provided in Appendix [G](https://arxiv.org/html/2510.10002v1#A7). Each system prompt specified the following: (i) an overview of the task, (ii) output verdicts and their definitions, (iii) output format, (iv) constraints on explanation criteria, (v) deliberation format, and (vi) overall goals. The last section – overall goals – allows for steering model behavior. By default, we specified that the “number one priority is to determine the correct verdict.” Models were explicitly prompted to change their verdict if necessary, but not solely for the sake of consensus. We considered an alternative framing in which consensus and correctness were balanced more evenly (Section [4.4](https://arxiv.org/html/2510.10002v1#S4.SS4)).

Figure: Figure 2: Models vary in their tendencies to change verdicts during deliberation. The number of rounds and change-of-verdicts for synchronous (a-b) and round-robin (c-d) deliberation. a. Proportion of dilemmas ($y$-axis) that reached consensus in a given number of rounds ($x$-axis), or did not reach consensus (final $x$-tick) for each deliberation (colors: see legend). b. Change-of-verdict rate for each pairwise deliberation (color corresponds to legend in a). c-d. Same as a-b, but for round-robin deliberation. Hatched bars denote the same models, but reversed order (e.g., GPT vs. Claude, where GPT goes first). Error bars denote 95% bootstrapped confidence intervals.
Refer to caption: x2.png

### 3.3 Value Classification and Similarity

We classified the values – understood here as the principles guiding moral judgment – expressed in each model’s explanation during deliberation. We leveraged Huang et al.’s Values in the Wild taxonomy (Huang et al., 2025). Values in the Wild contains over 3,000 AI values empirically derived from real-world interactions with Claude 3 and 3.5. Using an approach rooted in computational grounded theory (Nelson, 2020), we narrowed this taxonomy’s second-tier set of 276 values to a subset of 48 values $\mathcal{V}_{*}$ most relevant to everyday moral dilemmas (see Appendix [E](https://arxiv.org/html/2510.10002v1#A5) for further details).

We used Gemini 2.5 Flash (Comanici et al., 2025) as an external judge to classify each model response with up to five values. To focus on moral reasoning, we instructed the judge to select values used in “determining fault”, rather than values invoked when responding to other models’ explanations (see system prompt in Appendix [G](https://arxiv.org/html/2510.10002v1#A7)). Each response in a deliberation can thus be described by a set of values $\mathcal{V}$ where $\mathcal{V}\subset\mathcal{V}_{*}$ and $|\mathcal{V}|\leq 5$. These sets can be compared between models to assess value similarity. For two value sets $\mathcal{V}_{1}$ and $\mathcal{V}_{2}$, we defined the value similarity as their Jaccard index, which compares the intersection over the union of two sets and is robust to differences in set size:

$$ $\displaystyle\text{sim}(\mathcal{V}_{1},\mathcal{V}_{2})$ $\displaystyle=J(\mathcal{V}_{1},\mathcal{V}_{2})=\frac{|\mathcal{V}_{1}\cap\mathcal{V}_{2}|}{|\mathcal{V}_{1}\cup\mathcal{V}_{2}|}.$ (1) $$

### 3.4 Quantifying Model Inertia and Conformity in Deliberation

We aimed to measure how the deliberation format – including exposure to verdicts in prior rounds and within rounds – influenced a model’s verdict. We combined the results across all deliberations into a multinomial logistic model. For a given dilemma $d$, model $m$, and round $r$, we modeled the probability of obtaining a verdict $v$ as

$$ $\displaystyle\text{logit}\left[y=v\right]=\theta_{mv}+\phi_{dv}+\alpha_{m}\cdot\mathbf{1}\left[v=v_{m,r-1}\right]+\gamma_{\text{prev},m}\cdot n^{\text{prev}}_{vd}+\gamma_{\text{within},m}\cdot n^{\text{within}}_{vd,r}$ (2) $$

where $\theta_{mv}$ quantifies model $m$’s baseline preference for verdict $v$, $\phi_{dv}$ quantifies a fixed effect of dilemma $d$ on a verdict $v$, $\alpha_{m}$ is the “inertia,” or the increase in log-odds of choosing verdict $v$ if the model used that verdict in round $r-1$ ($v_{m,r-1}$), and $\gamma_{\text{prev},m}$ and $\gamma_{\text{within},m}$ measure “conformity,”
or the increase in log-odds of verdict $v$ based on its frequency in previous rounds $n^{\text{prev}}_{vd}$ or within the current round $n^{\text{within}}_{vd,r}$. Note that the latter will always be zero in synchronous settings, so $\gamma_{\text{prev},m}$ and $\gamma_{\text{within},m}$ are model-specific variables that separate the effects of synchronous and round-robin settings. We fit the model in PyTorch with weak $\ell_{2}$ regularization on the parameters.

Figure: Figure 3: Verdict distributions before and after deliberation. The proportion of dilemmas ($x$-axis) assigned a particular verdict ($y$-axis) for each of the three synchronous experiments. Verdict distributions after Round 1 (i.e., prior to deliberation) are indicated by colored points (see legend). Black arrows mark the proportion of dilemmas assigned a verdict after deliberation (i.e., achieving consensus). Red triangles denote the proportion of dilemmas not reaching consensus.
Refer to caption: x3.png

## 4 Results

We conducted four deliberation experiments with three large language models – GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.0 Flash – across 1,000 everyday dilemmas sourced from the AITA subreddit (Section [3.1](https://arxiv.org/html/2510.10002v1#S3.SS1)). First, we ran head-to-head synchronous deliberations for each model pair (Section [4.1](https://arxiv.org/html/2510.10002v1#S4.SS1)), analyzing the values invoked and comparing the dynamics of value alignment in deliberations that did and did not each consensus (Section [4.2](https://arxiv.org/html/2510.10002v1#S4.SS2)). Next, we ran round-robin deliberations in both pairwise (two models) and three-way (all three models), testing all possible orderings. Using these results, we assessed how order effects shape blame assignment (Section [4.3](https://arxiv.org/html/2510.10002v1#S4.SS3)). Finally, we evaluated model steerability by modifying system prompt goals to test their influence on consensus-seeking behavior (Section [4.4](https://arxiv.org/html/2510.10002v1#S4.SS4)).

### 4.1 Models exhibit different verdict revision patterns

We first conducted three head-to-head synchronous deliberations between GPT-4.1 (hereafter “GPT”), Claude 3.7 Sonnet (“Claude”), and Gemini 2.0 Flash (“Gemini”) over 1,000 AITA posts (Section [3.1](https://arxiv.org/html/2510.10002v1#S3.SS1)), each capped at four rounds. Results are reported in order: i) Claude vs. GPT, ii) Claude vs. Gemini, and iii) GPT vs. Gemini. See Appendix [H](https://arxiv.org/html/2510.10002v1#A8) for example deliberations. In most cases, dilemmas resolved after Round 1, with immediate agreement (Fig. [2](https://arxiv.org/html/2510.10002v1#S3.F2)a). Agreement was highest for Claude vs. GPT (66.1%), followed by GPT vs. Gemini and Claude vs. Gemini with 53.6% and 53.0% of dilemmas resolving in one round, respectively. Some dilemmas required additional rounds to reach consensus (GPT vs. Claude: 24.5%; Claude vs. Gemini: 38.5%; Gemini vs. GPT: 29.0%), while others never converged within the round limit (GPT vs. Claude: 9.4%; Claude vs. Gemini: 11.5%; Gemini vs. GPT: 17.4%).

Dilemmas resolved in later rounds involved one or more of the models revising their verdicts. We define the change-of-verdict (CoV) rate as the fraction of dilemmas in which an model changed its Round 1 verdict. CoVs can occur regardless of whether consensus was ultimately reached or whether the final verdict matched the initial one; they simply indicate that a model revised its blame assignment after being exposed to the other model’s explanation. CoV rates reveal striking inter-model differences: in the GPT vs. Claude deliberation, Claude’s CoV rate was 28.2% while GPT’s was only 3.1% (Fig. [2](https://arxiv.org/html/2510.10002v1#S3.F2)b: bottom). Gemini (33.3%) and Claude (34.1%) had nearly equal CoV rates (Fig. [2](https://arxiv.org/html/2510.10002v1#S3.F2)b: middle). Meanwhile, GPT’s CoV rate vs. Gemini was only 0.6% – only changing its verdict in six deliberations – while Gemini’s CoV rate was 41.2%. Overall, GPT showed far greater resistance to verdict revision than Claude and Gemini.

Figure: Figure 4: Values used and inherited during synchronous deliberation. Rows denote model pairs. Values are shown next to their corresponding bar. Up to 5 values reaching statistical significance are shown. a-c. The difference in value occurrences – the fraction of messages in which a model uses a value – between pairs of models. b. The fraction of deliberations where a specific value was inherited. Error bars denote bootstrapped 95% confidence intervals.
Refer to caption: x4.png

Next, we analyzed verdict distributions – the proportions of dilemmas assigned a particular verdict (NTA, YTA, ESH, NAH, INFO) – before and after deliberation (Fig. [3](https://arxiv.org/html/2510.10002v1#S3.F3)). Models produced notably distinct verdict distributions: GPT overwhelmingly favored NTA verdicts in the first round (78.8% and 84.9% for its two deliberations) while Claude (55.6%, 55.4%) and Gemini (51.9%, 50.9%) assigned fewer, though still a majority. Gemini leaned heavily on YTA (33.1%, 35.2%), far more than GPT or Claude. GPT drew more on NAH and ESH, while Claude was split between ESH and YTA. These verdict distributions are notably different from past work similarly examining AITA with older LLMs, suggesting shifts in alignment with newer models, data distribution, or sensitivity to system prompts (Sachdeva & van Nuenen, 2025).

### 4.2 Values invoked by models align in deliberations reaching consensus

During deliberation, models provide explanations for their blame assignments, often invoking particular values. For example, in Figure [1](https://arxiv.org/html/2510.10002v1#S1.F1), Model 1 (Claude) begins justifying its NTA verdict with “The issue at hand isn’t about the money itself, but about proper acknowledgment when using shared resources…” – an appeal to values of effective communication. Models may invoke and prioritize different values, and these can shift over the course of deliberation. Thus, value similarity between models may shift, and some values may drive verdict changes more effectively than others. We aimed to identify these values and trace their dynamics across deliberations.

We drew on a taxonomy of values empirically identified in AI-human conversations by (Huang et al., 2025). From the Values in the Wild taxonomy (Section [3.3](https://arxiv.org/html/2510.10002v1#S3.SS3)), we selected 48 values relevant to the types of morally-driven, everyday dilemmas featured in this community (see Appendix [F](https://arxiv.org/html/2510.10002v1#A6)). An external model (Gemini 2.5 Flash) identified up to five values present in each explanation across deliberations. Each response can thus be described by a set of values $\mathcal{V}$, which we used to analyze value dynamics and alignment between models during deliberation.

We first examined how models invoked values by measuring value occurrences – the fraction of messages containing a value (Fig. [4](https://arxiv.org/html/2510.10002v1#S4.F4)a-c). We found the values used more often by Claude often reflect thoughtful communication: Constructive dialogue, Conflict resolution and reconciliation, and Emotional intelligence and regulation (Fig. [4](https://arxiv.org/html/2510.10002v1#S4.F4)a: blue bars). GPT, by contrast, tends to use values that reflect personal liberty and direction communication more often: Consent and personal boundaries, Personal autonomy, and Honest communication (Fig. [4](https://arxiv.org/html/2510.10002v1#S4.F4)a, orange bars). Similar patterns emerge between GPT and Gemini, with GPT emphasizing personal liberty values and Gemini favoring empathetic communication, though with larger differences (e.g., GPT uses Consent and personal boundaries roughly 17% more often than Gemini).

Next, we examined alignment dynamics between two models during synchronous deliberation, as measured by their value similarity (Section [3.3](https://arxiv.org/html/2510.10002v1#S3.SS3)), or the Jaccard similarity between their two value sets $\mathcal{V}_{1}$ and $\mathcal{V}_{2}$. We first averaged value similarities across individual rounds where the two models agreed on the verdict (Fig. [5](https://arxiv.org/html/2510.10002v1#S4.F5)a: “Consensus”) and compared these to rounds where models disagreed. Across all three model pairs, we found significantly higher value similarity during verdict agreement compared to disagreement. This suggests that when models converge on blame assignment, they also align more closely on the values underlying that judgment. The average value similarities during agreement – roughly 0.4 to 0.5 – translates to approximately three shared values, assuming each model draws from five values per explanation.

We analyzed deliberations that began with disagreement (i.e., lasted more than one round). We split these between those that ultimately reached consensus (Fig. [5](https://arxiv.org/html/2510.10002v1#S4.F5)b, black points) and those that did not (gray points). We found that, for consensus-reaching deliberations, average value similarities significantly increased by 30-60%. In deliberations not reaching consensus, similarities only increased by 6-17%, with mild significance observed only for Gemini vs. GPT. Together, these results indicate a strong link between value convergence and consensus formation in model deliberation.

Figure: Figure 5: Values invoked by models align in deliberations with consensus. In all subplots, $y$-axis denotes the value similarity between the two models, averaged over dilemmas. a. Average value similarity for synchronous deliberation, with individual messages split by consensus and disagreement ($x$-ticks). b. Value similarities (for deliberations lasting more than one round) during Round 1 and the last round of deliberation, split between those reaching consensus, and those not (legend). Significance markers denote Mann-Whitney U tests ($***$: $p<10^{-3}$; $*$: $p<10^{-1}$; n.s.: no significance). Error bars denote bootstrapped 95% confidence intervals.
Refer to caption: x5.png

Building on this analysis, we identified inherited values – values a model adopted after a CoV that it had not invoked in the first round, but that its opponent did. We treat inherited values as a proxy for the most “convincing” values in deliberation (Fig. [4](https://arxiv.org/html/2510.10002v1#S4.F4)d-f). Several patterns emerged: Claude and Gemini often inherited GPT’s personal liberty values, while GPT most frequently inherited Empathy and understanding. As expected, GPT showed no statistically significant value inheritance from Gemini, reflecting its low CoV rate.

### 4.3 Round-robin deliberation reveals model-dependent order effects

Thus far, we have focused on synchronous deliberation. We next considered round-robin deliberation, where models provided verdicts sequentially (Fig. [1](https://arxiv.org/html/2510.10002v1#S1.F1): bottom). We conducted both head-to-head and three-way variants, testing all possible model orders. We hypothesized that round-robin deliberation would shape deliberation outcomes, since models are exposed to other verdicts within each round.

We found that round-robin deliberation substantially increased consensus rates (Fig. [2](https://arxiv.org/html/2510.10002v1#S3.F2)c). We further observed striking model-dependent order effects. For example, the Claude vs. GPT deliberation (Claude going first: Fig. [2](https://arxiv.org/html/2510.10002v1#S3.F2)c) ended in 1 round nearly 90% of the time, in contrast to the reverse order which only ended in the first round 40% of the time (exhibiting a lower consensus rate: (Fig. [2](https://arxiv.org/html/2510.10002v1#S3.F2)c, hatched bar). Thus, GPT was more likely to conform to Claude’s initial verdict, relative to vice versa, while showing higher CoV rates compared to the synchronous setting. We further observed that Gemini conformed to both Claude and GPT’s verdicts, with respective deliberations ending in one round roughly 90% of the time when Gemini went second. These findings demonstrate that deliberation format can strongly impact model behavior, with GPT exhibiting conformity that contrast its inertial behavior in the synchronous setting.

We next considered round-robin deliberations with all three models (Appendix [A](https://arxiv.org/html/2510.10002v1#A1)). Interestingly, consensus was achieved in virtually all dilemmas (Fig. [8](https://arxiv.org/html/2510.10002v1#A1.F8)). Similar to the head-to-head setting, GPT and Gemini were strongly conforming: deliberations beginning with Claude ended in the first round nearly 90% of the time for either ordering. We observed interesting order effects in both the consensus verdict distribution and CoV rates. For example, GPT steered over 70% of dilemmas towards “NTA” in the deliberation where it went first and Claude third, but this effect disappeared when Claude proceeded second (Fig. [9](https://arxiv.org/html/2510.10002v1#A1.F9)). Furthermore, CoV rates depended on both a model’s own deliberation position and its position relative to the other two models in the sequence (Fig. [10](https://arxiv.org/html/2510.10002v1#A1.F10)). For example, GPT’s CoV rate significantly increased when its round-robin position occurred right after Claude.

We repeated our value alignment analysis for the round-robin setting (Appendix [A](https://arxiv.org/html/2510.10002v1#A1): Fig. [6](https://arxiv.org/html/2510.10002v1#A1.F6), Fig. [7](https://arxiv.org/html/2510.10002v1#A1.F7)), finding similar results: models exhibited higher value similarity when they agreed (relative to disagreement). Furthermore, value similarity generally increased more when consensus was attained after initial disagreement (with some variance due to small sample sizes for specific experiments).

**Table 1: Model parameter estimates.**
| Parameter | Estimate | 95% CI | Odds Ratio |
| --- | --- | --- | --- |
| $\alpha_{\text{GPT}}$ | 2.11 | [2.01, 2.21] | 8.27 |
| $\alpha_{\text{Claude}}$ | 1.50 | [1.44, 1.55] | 4.49 |
| $\alpha_{\text{Gemini}}$ | 1.04 | [0.97, 1.11] | 2.83 |
| $\gamma_{\text{prev, GPT}}$ | 0.26 | [0.19, 0.32] | 1.29 |
| $\gamma_{\text{prev, Claude}}$ | 0.38 | [0.34, 0.42] | 1.47 |
| $\gamma_{\text{prev, Gemini}}$ | 0.01 | [-0.02, 0.05] | 1.01 |
| $\gamma_{\text{within, GPT}}$ | 2.16 | [2.08, 2.25] | 8.68 |
| $\gamma_{\text{within, Claude}}$ | 0.05 | [0.02, 0.09] | 1.05 |
| $\gamma_{\text{within, Gemini}}$ | 1.65 | [1.57, 1.74] | 5.21 |

We synthesized results across experiments by fitting a multinomial logistic model with three components: (i) fixed-effects for each model and dilemma, (ii) an inertia parameter $\alpha_{m}$ capturing a model $m$’s tendency to repeat a prior verdict, and (iii) conformity parameters $\gamma_{\text{prev},m}$ and $\gamma_{\text{within},m}$ quantifying how each model conforms to previously seen verdicts. The conformity parameters notably separate order effects of the round-robin settings (e.g., $\gamma_{\text{within},m}=0$ in the synchronous setting). The fitted estimates (Table [1](https://arxiv.org/html/2510.10002v1#S4.T1)) reveal marked differences in inertia: GPT was the most rigid, with an odds ratio of 8.27 for repeating its previous verdict, compared to 4.49 for Claude and 2.83 for Gemini. However, the conformity parameters reveal striking differences: GPT is by far the most conformist model (odds ratio equal to 8.68) followed by Gemini (5.21). Claude, meanwhile, is the most conformist model with respect to previous rounds (largely driven by its behavior in the synchronous setting). Together, these results demonstrate that the deliberation format creates fundamental tensions between a model’s inertia and conformity, with order effects dramatically shaping how models render judgments in moral dilemmas.

### 4.4 System prompt steering of models’ verdict flexibility

Synchronous deliberation stood out for its comparatively lower conformity. To test whether CoV rates could be steered, we modified the system prompt to balance consensus-seeking with selecting the correct verdict (Appendix [G](https://arxiv.org/html/2510.10002v1#A7)). Specifically, we instructed models to change their verdict for the sake of consensus, within reason. We then re-ran the head-to-head synchronous deliberation experiments with this revised prompt.

For each model, we compared its CoV rate under the balanced prompt to its rate in the original synchronous setting (Appendix [B](https://arxiv.org/html/2510.10002v1#A2): Fig. [13](https://arxiv.org/html/2510.10002v1#A2.F13)). GPT showed the largest change, with a fivefold increase relative to Claude and an eighteenfold increase relative to Gemini. Claude and Gemini also increased their CoV rates, but to a lesser degree. Even so, GPT’s CoV remained substantially lower than both Claude (by 40%) and Gemini (by 76%). Notably, consensus rates did not rise substantially despite the larger CoV rates (Claude vs. GPT: 90.6%$\rightarrow$93.8%; Claude vs. Gemini: 87.1%$\rightarrow$88.5%; Gemini vs. GPT: 82.6%$\rightarrow$92.7%). This suggests that models often shifted to different verdicts rather than converging, in some cases even swapping positions due to the simultaneous-response format.

## 5 Discussion

In this work, we used multi-turn deliberation of everyday dilemmas as a lens for understanding deliberation dynamics, value alignment, and order effects in language models.

Our findings highlight sharp differences in verdict revision. Although most deliberations reached consensus – as found in studies using multi-agent debate on verifiable tasks (Du et al., 2023; Estornell & Liu, 2024; Wang et al., 2024) – that consensus often arose from a mix of inertia and conformity. This reflects two opposing dynamics identified in prior work: over-agreeableness and sycophancy (Pitre et al., 2025; Cheng et al., 2025; Sharma et al., 2023), versus inertial confidence, where models persist in their initial stance despite counterarguments (Nguyen & Prasad, 2025). Future work could disentangle the drivers of these behaviors, likely shaped by an interplay of model capacity, alignment (Sharma et al., 2023; Zhang et al., 2024b), and protocol (e.g., system prompt specification) (Radharapu et al., 2025).

We further observed that revision behaviors – inertia, agreeableness, and sycophancy – are format-dependent. GPT, for example, shows the strongest inertia in synchronous settings but the strongest conformity in round-robin settings. This apparent contradiction reflects differences in behavior across deliberation formats. Sycophancy and inertia are not fixed model traits but interaction properties. While prior work links sycophancy to factors such as prompt context and rhetorical strength (Fanous et al., 2025), our results show that multi-model interaction formats also fundamentally shape how such tendencies manifest.

We identified distinct value patterns: GPT emphasized personal autonomy and liberty, while Claude and Gemini favored empathetic and communicative values, consistent with prior work (Chiu et al., 2024; Abdulhai et al., 2023; Ki et al., 2025). These differences in value usage across multi-turn interactions matter for how these models shape human values and behavior in sustained conversations. We also observed a tight coupling between value alignment and consensus, with certain values more effectively driving agreement. Our approach offers a framework to assess value usage in longer-horizon interactions. Future work could build on this by studying the values that drive alignment collapse (Becker, 2024), sycophancy, and hallucination.

Our work has several limitations. To manage API costs, we ran each experiment once, prioritizing breadth of experiments and dilemmas over repetition. While our sample of 1,000 dilemmas is large enough that aggregate results are likely robust, individual dilemmas could reach different outcomes if re-run. Second, the models we examined are already outdated by newer releases which use reasoning (GPT-5, Claude 4 Sonnet, and Gemini 2.5 Pro). Given that we observed different verdict distributions compared to prior work on older models (Sachdeva & van Nuenen, 2025), our findings may not generalize to newer model releases. Lastly, our controlled interaction design meant deliberation was scaffolded rather than emergent. This may reflect constrained convergence rather than genuine multi-agent interaction (La Malfa et al., 2025). Our aim, however, was to use deliberation as a structured testbed for examining value dynamics and consensus formation – an approach that can be applied to newer models as well.

The system prompt specified deliberation format, roles, and goals, explicitly situating models as debaters. Prior work shows that role specification – casting models as judges or debaters – encourages stronger stance-taking on ambiguous, “no-consensus” questions (Radharapu et al., 2025; Chen et al., 2024). Other studies demonstrate evaluation “awareness,” where models can reliably infer evaluation settings and adapt their behavior accordingly (Needham et al., 2025; Nguyen et al., 2025; Abdelnabi & Salem, 2025). Collectively, these findings suggest that prompt-imposed roles and evaluation framing can shift deliberative dynamics. Given the apparent steerability of verdict revision we observed, this raises the question of balance: in deployed settings, we may want models to exhibit a calibrated mix of agreeableness and confidence – flexible enough to adapt, but not so easily swayed that their values collapse. It is unclear whether these effects persist in everyday multi-turn use, where the primary concern is not experimental performance but how models shape human values, beliefs, and behavior.

#### Acknowledgments

We thank Jeffrey Sorensen and the Google Cloud Research Credits Program for providing Google Cloud credits that supported a portion of the experiments and analysis in this work.

#### Ethics Statement

Data. While we utilized publicly available posts from Reddit’s r/AmITheAsshole community, we recognize that users may not have anticipated their posts being used for research. To mitigate privacy concerns, we did not collect, analyze, or infer any personally identifiable information or demographic data. Our dataset was restricted to post content and associated verdicts.

LLM Usage. We used LLMs to (i) polish writing, (ii) finding related work, and (iii) to support code development necessary for the analysis. LLMs did not contribute significantly at the level of a contributing author. However, in the spirit of full transparency, we describe in detail the major components LLMs supported:

- •
LLMs were used to support word choice and offer suggestions on shortening sentences to comply with space requirements.
- •
LLMs were used to find additional relevant works that the authors did not find during typical literature searches at various points of the research.
- •
LLMs were used to accelerate the development of Figure 3 in Matplotlib. Specifically, while the authors aimed to find the best way to present the results, they used LLMs to quickly map out various figure presentations. The code output used for the final design was used as a foundation for the final figure development.
- •
Same as previous, but for Figure 4.
- •
LLMs were used to provide feedback on the development of the deliberation formats in autogen to ensure their accuracy.
- •
LLMs were used to help produce code to train variations of the multinomial model.

#### Reproducibility Statement

Our system message and default parameters for all models are specified in the Appendices. All code used to conduct the analyses and create the figures in this paper is publicly
available(^2^22[https://anonymous.4open.science/r/llm_deliberation_values-86E7/README.md](https://anonymous.4open.science/r/llm_deliberation_values-86E7/README.md)). Deliberation outputs will be made available on HuggingFace once the paper is deanonymized.

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Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen.
A Survey of Large Language Models, March 2025.
URL [http://arxiv.org/abs/2303.18223](http://arxiv.org/abs/2303.18223).
arXiv:2303.18223 [cs].

## Appendix A Extended Round Robin Deliberation Results

### A.1 Head-to-Head Round Robin Deliberation

Figure: Figure 6: Value similarity in head-to-head round-robin deliberation. Average value similarity ($y$-axis) for head-to-head round-robin deliberation, with individual messages split by consensus and disagreement ($x$-ticks). Each subplot denotes a different order-dependent pairing of models. Model pairings are consistent across columns. Significance markers denote Mann-Whitney U tests ($***$: $p<10^{-3}$; $*$: $p<10^{-1}$; n.s.: no significance). Error bars denote bootstrapped 95% confidence intervals.
Refer to caption: x6.png

Figure: Figure 7: Value similarity evolution in head-to-head round. Value similarities (for deliberations lasting more than one round) during Round 1 and the last round of deliberation, split between those reaching consensus, and those not (legend). Each subplot denotes a different order-dependent pairing of models. Model pairings are consistent across columns. Significance markers denote Mann-Whitney U tests ($***$: $p<10^{-3}$; $*$: $p<10^{-1}$; n.s.: no significance). Error bars denote bootstrapped 95% confidence intervals.
Refer to caption: x7.png

### A.2 Three-way Round Robin Deliberation

Figure: Figure 8: Number of rounds during three-way round-robin deliberation. The fraction of dilemmas ($y$-axis) concluding in a given number of rounds, or no consensus ($x$-axis), for each three-way deliberation (titles). The order of models in each round-robin is specified by the titles. Columns correspond to deliberations where each model proceeds first (GPT: a,d; Claude: b,e; Gemini: c,f).
Refer to caption: x8.png

Figure: Figure 9: Verdict distributions during three-way round-robin deliberation. The proportion of deliberations ($y$-axis) reaching a given verdict ($x$-axis) after three-way round robin deliberation, within a deliberation setting (colors). Round-robin order is denoted by legend. Deliberations not reaching consensus are omitted due to their relatively rarity.
Refer to caption: x9.png

Figure: Figure 10: Change-of-verdict rates during three-way round-robin deliberation. Change-of-verdict rates ($y$-axis) for each model (rows) during round-robin deliberation. Each experiment is denoted by the $x$-axis, which indicates what order the specific model went. The text annotations indicate the specific order for that round.
Refer to caption: x10.png

Figure: Figure 11: Value similarity in three-way round-robin deliberation. Average value similarity ($y$-axis) for the Gemini-Claude-GPT three-way round-robin deliberation, with individual messages split by consensus and disagreement ($x$-ticks). Each subplot denotes a pairwise combinations of value similarities within the three-way deliberation. Significance markers denote Mann-Whitney U tests ($***$: $p<10^{-3}$). Error bars denote bootstrapped 95% confidence intervals.
Refer to caption: x11.png

Figure: Figure 12: Value similarity evolution in three-way deliberation. Value similarities (for deliberations lasting more than one round) during Round 1 and the last round of deliberation, for the Gemini-Claude-GPT deliberation. Each subplot denotes a pairwise combinations of value similarities within the three-way deliberation. Significance markers denote Mann-Whitney U tests ($***$: $p<10^{-3}$). Error bars denote bootstrapped 95% confidence intervals.
Refer to caption: x12.png

## Appendix B Impacts of Establishing Goals in System Prompt

Figure: Figure 13: System prompt steerability of change-of-verdict ratio. Each panel corresponds to a different synchronous experiment. The change-of-verdict rate for the original prompt and balanced prompt are shown. Statistical tests refer to proportion z-test ($***$: $p<10^{-3}$; $*$: $p<10^{-1}$; n.s.: no significance). Colors denote models (legend).
Refer to caption: x13.png

## Appendix C Complete Preprocessing Pipeline

Our preprocessing pipeline was as follows:

- 1.
We obtained the 3,272 available AITA submissions from January 1, 2025 to March 30, 2025 using the Reddit API.
- 2.
For each submission, we obtained the top 100 “top-level” comments (i.e., those that are not replies to other comments). If there were fewer than 100 comments, we obtained all top-level comments.
- 3.
We filtered out meta posts, deleted posts, removed posts, or posts that were too short (less than 1,000 characters). We identified meta posts either by examining the username or the “flair” attached to the post.
- 4.
Reddit posts often contain “edits” or “updates” where the original post provides additional details or responses after their initial submission. For each post, we removed any portion of the text that was an “edit” or “update” using a regular expression.
- 5.
We used a regular expression to classify each comment, for each post, as “NTA,” “YTA,” “NAH,” “ESH,” and “INFO.” In cases where we could not cleanly extract a label, we used Gemma-9B to classify the comment.
- 6.
For each submission, we calculated the proportion of comments assigning each of the five verdicts. We then calculated as “disagreement rate” as the entropy of the verdict proportions.
- 7.
We extracted the top 1,000 dilemmas with the highest disagreements as the final dataset to use for deliberation.

## Appendix D Large Language Models

We used the following LLMs and corresponding parameters:

- •
GPT-4.1: Version gpt-4.1-2025-04-14; temperature 1; default parameters
- •
Claude 3.7 Sonnet: Version claude-3-7-sonnet-20250219; temperature 1; default parameters
- •
Gemini 2.0 Flash: Version gemini-2.0-flash; temperature 1; default parameters

## Appendix E Value Set Creation

Our process for creating the final list of 48 values (next section) was as follows:

- 1.
We began with the list of 267 values at the second tier of the Values in the Wild taxonomy. These values consisted of clusters grouped together from a more fine-grain list of values. We began here in order to control the number of values we classified the deliberation outputs with.
- 2.
We (the authors) and 3 LLM judges (Gemini 2.0 Flash, Claude 3.5 Haiku, GPT-4o) classified all 267 values within four categories: moral, epistemic, aesthetic, and instrumental. We chose the subset of values deemed “moral” by at least 4 of the 5 annotators. This produced a list of 110 values.
- 3.
Using Gemini 2.5 Flash, we classified model outputs from a random selection of 100 dilemmas 5 separate times, using the list of 110 values. We then examined the values the consistently appeared across repetitions.
- 4.
We then manually considered each value, coding it for inclusion or exclusion from the final set according to the following options 1) inclusion due to relevance and high occurrence in everyday dilemmas; 2) inclusion due to relevance and moderate occurrence in everyday dilemmas; 3) inclusion for relevance to everyday dilemmas, despite limited to no occurrence; 4) omit because of overlap with other values; 5) omit due to too large a scope or lack of appearances; 6) omit due to lack of appearances and lack of relevance.
- 5.
We then manually merged 16 values based on their co-occurrences in our trial set to produce the final set of 48 values.

## Appendix F List of Values

Values are listed in the same randomized order as provided to the LLM judge in a system prompt.

- •
Trust creation and maintenance
- •
Constructive dialogue
- •
Respect and dignity
- •
Professional ethics and integrity
- •
Social etiquette
- •
Religious respect and accommodation
- •
Linguistic respect and inclusivity
- •
Cultural understanding and respect
- •
Cultural heritage and tradition
- •
Financial wellbeing
- •
Sexual freedom and pleasure
- •
Protection of self and others from harm
- •
Environmental consciousness
- •
Authentic expression
- •
Workplace boundaries
- •
Parental care
- •
Consumer and client protection
- •
Child welfare
- •
Animal and pet welfare
- •
Worker welfare and dignity
- •
Workplace etiquette and respect
- •
Economic justice and fairness
- •
Healthcare equity and access
- •
Consent and personal boundaries
- •
Property rights protection
- •
Personal autonomy
- •
Emotional safety and support
- •
Mental health sensitivity and support
- •
Power dynamics values
- •
Privacy and confidentiality
- •
Religious and spiritual authenticity
- •
Emotional intelligence and regulation
- •
Emotional intimacy
- •
Prosocial altruism
- •
Honest communication
- •
Intergenerational respect and relationships
- •
Supportive and caring relationships
- •
Family bonds and cohesion
- •
Conflict resolution and reconciliation
- •
Public good and community engagement
- •
Accessibility
- •
Reciprocal relationship quality
- •
Environmental consciousness
- •
Empathy and understanding
- •
Personal growth
- •
Achievement and recognition
- •
Balance and moderation
- •
Physical health and wellbeing
- •
Personal accountability and responsibility

## Appendix G System Prompts

## Appendix H Example Deliberations

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 4
questions: N/A
soundness: 3
strengths: 1. The use of the AITA subreddit is extremely well motivated and shrewd, as it is a constantly updated stream of multi-value laden scenarios with lots of potential for disagreement.
2. Leveraging the "Values in the Wild" paper is also a smart choice, given the extensive work done therein.
3. The findings regarding consensus arising from a combination of inertia and conformity are quite interesting and definitely merit future study.
summary: The proposed approach leverages debate tactics to determine if deliberative dynamics in multi turn settings impact the socio-technical evaluation of LLMs. In particular, the authors leverage everyday situations from the Reddit AITA community as seed situations. Their findings report how deliberation impacts model behavior.
weaknesses: 1. The choice of 3 large scale models is a bit under-motivated. Specifically, why not also try the analyses on a popular open-weight model, such as the Qwen or DeepSeek variety? Seeing how well smaller models, as well as open-weight models, perform in this setting could be quite beneficial for the extensibility of the findings presented. This would also help alleviate the reproducibility concerns that the authors themselves bring up in the Discussion.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: Q1 why use Gemini-2.5 flash as judge? (line 197)

Q2 what are the arrows in figure 3
soundness: 2
strengths: S1 Important topic
The value alignment topic is important for socio-tech. alignment. The work (through may not be perfect) will help initiate more discussions on this theme.

S2 Useful data resource
The effort of clustering and cleaning the large corpus of values by Values in the Wild could help the community to continue build upon this line of research.
summary: This paper collect 1k everyday dilemmas from Reddit's r/AITA community as the basis for simulate LLM debates. They developed two settings for two models as a pair (synchronous setting: each comment its verdict; head-to-head: one by one between two models). They tested three models (GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.0 Flash) for order effects and verdict revision. They show some behavioural differences (e.g. Gemini 2.0 Flash prioirizied more on empathy).
weaknesses: [more important] W1 motivation of the experiments and practical importance
- motivation: why we need two settings (synchronous deliberation vs. head-to-head)
- any practical use cases to justify such debates can help solving the socio-tech. alignemnt as authors claimed.
- See some recommendations in w4.
- another recommendation: what about the performances of current reasoning models?

[important] W2 The raw source of values adopted by authors could lead to biased analysis. caution is needed.
- The Values in the Wild is the work from Anthropic researchers using their own tool to analyze the human users and AI. It may only cover what human users perceived and interact with an AI "chatbot assistant", rather than an AI (which could be many roles in the future applications). 
- also it is based on one model series (Claude) which could give indirect advantages on evaluating Claude series models.
- I recommend authors to note the reader early on that the values studied and the findings are only for human-favored values (in chatbot setting).

[more important] W3 Comparison of models experiments is hard to extend to more models due to the bad choice of metric
- Authors compared 3 models ( GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.0 Flash) by making pairs of two models (GPT vs Claude, GPT vs Gemini and Claude vs Gemini). The choice of metric (proportion of dilemmas) is bad since it is difficult for readers and researchers to compare three at the time (need to infer in each model pair e.g. line 252- 260).
- Recommend authors to use order-based metric/ rank-based metric (e.g., battles by elo ratings used in ChatbotArena). One recent value-based work (LitmusValues) [2] also adopt similar approach to allow an extensive scale of evaluation to offer more practical importance.

[more important] W4 Not generalizable findings
- followed by W3, authors only compared 3 models. It is insufficient and potentially misleading to compare them as model series difference (e.g. GPT-4.1 directly concludes as GPT, Claude 3.7 Sonnet directly concludes as Claude, and Gemini 2.0 Flash directly concludes as Gemini)
- several studies [2,3,4] already show that different models in the same model family could have different value preferences.
- similarly, the findings (line 367-368) between different model pairs are interesting but they could have confounding factors (e.g. the opponent effect on the model being tested). 
- recommended some stat tests to justify whether the findings can still hold when comparing across three (and ideally even more models).
- section 4 (steering system prompt) could have more in-depth analysis and experiment. For instance, try different prompt variations. Some prior work [3] has done some explorations on system prompts steerability on value preferences using model spec. Authors can take references of them to provide more in-depth analysis to benefit the community and provide more actionable advice to justify their study's practical importance [W1]

[1] ChatbotArena https://arxiv.org/abs/2403.04132
[2] LitmusValues https://arxiv.org/abs/2505.14633
[3] DailyDilemmas
[4] Multilingual trolley problem https://arxiv.org/abs/2407.02273
[5] https://arxiv.org/pdf/2402.06782

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: Please see Weaknesses, where each paragraph is a raised question, with decreasing importance.
soundness: 3
strengths: The paper studies an important problem: values elicitation of multi-turn, multi-agent debate. The conformity to or emergence of certain sets of values during conversations is indeed a phenomenon expected but not well-studied. The work improves our understanding of this problem with layered conclusions from observations of dynamics to quantified values inclination and effects of deliberation formatting.

The choice of AITA questions as dataset is a worthwhile novelty, as they are straight-forward to answer but varied in underlying values.

Experiments are carried out with satisfying scale and analysis.
summary: This work studies values elicited from multi-agent debate verdicts, arriving to interesting conclusions across multiple deliberating formats and models. Experiments are done on 1000 questions from the AITA reddit community, with debates from models in {GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.0 Flash}. Results cover aspects including consensus-forming, values orientations, effects of deliberation format and effects of system-prompt-steering.
weaknesses: The use of Reddit questions as datasets raises natural questioning on pre-trained bias on the tested models' verdicts. Would the results be different if using exclusive / synthetic questions?

Another field of results I feel lacking is the models' performances on their own. All results of the models' values / verdict inclination are drawn during debate with other models, but we also need the model's original inclination (i.e. verdict and inclination without debate) as baseline to determine effects of debate.

When comparing values' similarity, only the overlapping proportions are considered. But lack of overlapping doesn't necessarily mean lack of alignment between values: some values foundations are close to each other, e.g. "Family bonds and cohesion" and "Parental care". For example, if both value A and value B are important to question M and question N, it could be that GPT uses A for M, B for N while Gemini uses B for M and A for N. Some form of general analysis is needed

Experiments on steering seem a little lacking. We expect to see effects of more variables such as allowed conversation rounds.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 2
questions: - How sensitive are the value-similarity trends to the judge choice and temperature? Please report a small ablation with a different judge family and a lower temperature.
- You note that GPT steers toward NTA when first, but loses that effect when Claude is second. Can you quantify this steering as an estimated average treatment effect with bootstrapped CIs across orders?
- The “balanced goals” prompt increases revision but not consensus. Can you show per-value inheritance changes under that prompt, and whether revisions move toward the majority or create swaps?
soundness: 2
strengths: - Moves beyond accuracy-centric MAD papers by focusing on moral judgments in realistic, ambiguous dilemmas, aligning with calls to evaluate real-world impact and sociotechnical properties.
- The synchronous vs. round-robin comparison neatly surfaces inertia vs. within-round conformity. The multinomial modeling of α (inertia) and γ (conformity) is a clean abstraction that other groups can reuse.
- Adapting Anthropic’s “Values in the Wild” taxonomy for everyday dilemmas gives a concrete lens on which values drive alignment and which are “inherited” at revision. This bridges debate mechanics with value elicitation work.
summary: The paper studies how deliberation format shapes value expression and consensus in LLM-LLM debates over everyday moral dilemmas. Using 1,000 AITA cases, the authors run pairwise and three-way debates among GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.0 Flash in two settings: synchronous (parallel) and round-robin (sequential). They quantify model inertia and conformity via a multinomial model, analyze verdict change rates, and classify values in explanations using a pruned set of 48 values drawn from “Values in the Wild” (Anthropic) with a separate judge model. Prompt tweaks that explicitly encourage consensus increase revision but do not dramatically raise consensus rates. The paper argues that sociotechnical alignment depends on interaction protocol, not only on single-turn outputs.
weaknesses: - Value labels come from an LLM judge. This creates a risk that the same families of models define and then satisfy the value metric. There is no human audit of value annotations, no adjudication protocol, and no inter-judge agreement statistics.
- Most experiments appear to be one pass per case per setting. Debate outcomes can be stochastic with temperature 1. Report replicate variance on a random 10-20 percent subset, or do bootstrap resampling at the dialogue level to confirm the stability of α and γ estimates.
- Missing literature: https://arxiv.org/abs/2506.12657 
- The work argues that protocol choices shape social outcomes, but it does not measure helpfulness or user-centric welfare in the outputs, only verdict agreement and value similarity. Even a small human rating on perceived fairness, empathy, or harm reduction would make the claim more actionable.