Venue: NAACL 2025

# Teaching Models to Balance Resisting and Accepting Persuasion

 Elias Stengel-Eskin\({}^{1}\) &Peter Hase\({}^{1,2}\) &Mohit Bansal\({}^{1}\)

\({}^{1}\)UNC Chapel Hill  \({}^{2}\)Anthropic

###### Abstract

Large language models (LLMs) are susceptible to persuasion, which can pose risks when models are faced with an adversarial interlocutor. We take a first step towards defending models against persuasion while also arguing that defense against adversarial (i.e. _negative_) persuasion is only half of the equation: models should also be able to accept beneficial (i.e. _positive_) persuasion to improve their answers. We show that optimizing models for only one side results in poor performance on the other. In order to balance positive and negative persuasion, we introduce **P**ersuasion-**B**aanced **T**raining (or PBT), which leverages multi-agent recursive dialogue trees to create data and trains models via preference optimization to accept persuasion _when appropriate_. PBT allows us to use data generated from dialogues between smaller 7-8B models for training much larger 70B models. Moreover, PBT consistently improves resistance to misinformation and resilience to being challenged while also resulting in the best overall performance on holistic data containing both positive and negative persuasion. Crucially, we show that PBT models are better teammates in multi-agent debates across two domains (trivia and commonsense QA). We find that without PBT, pairs of stronger and weaker models have unstable performance, with the order in which the models present their answers determining whether the team obtains the stronger or weaker model's performance. PBT leads to better and more stable results and less order dependence, with the stronger model consistently pulling the weaker one up.1

Footnote 1: Code: [https://github.com/esteng/persuasion_balanced_training](https://github.com/esteng/persuasion_balanced_training)

## 1 Introduction

Persuasion is a core component of our ability to interact successfully and productively with each other, allowing one individual to change the beliefs of another. Increasingly, large language models (LLMs) are being deployed within standard human interaction frameworks, i.e. interacting in dialogues with people Yi et al. (2024) as well as with other LLMs Chen et al. (2024); Liang et al. (2023); Du et al. (2024b). LLMs have broadly revealed themselves to be easily persuaded in ways that can hurt their usability; for example, models can be persuaded to reveal private data or generate harmful text Zeng et al. (2024) and simply questioning the correctness of model outputs often causes them to change their answers Laban et al. (2023). This motivates teaching models to resist these kinds of adversarial inputs, i.e. to make models less easily persuaded. However, this is only one side of the story: as we later show, being overly-resistant to persuasion negatively impacts model quality: models that stubbornly stick to their responses do not improve through discussion, and may be frustrating to interact with. For LLMs to be reliable and useful conversation partners and teammates (e.g. in multi-agent debate, human-model interaction, etc.), a balance must be struck between resistance to harmful or negative persuasion (see left side of Fig. 1) and acceptance of beneficial (or positive) persuasion (see Fig. 1, right side); in other words,

Figure 1: Resisting negative persuasion and accepting positive persuasion are both needed for productive dialogues. However, only optimizing one or the other can lead to overcorrection. We argue that the two must be balanced, i.e. the agent should _resist and accept persuasion when appropriate_.



models should be persuaded _when appropriate_.

Past work Zeng et al. (2024); Xu et al. (2024); Laban et al. (2023) has primarily focused on measuring negative persuasion, analyzing existing models and finding that they perform poorly when faced with an adversary who persuades the model to change its answer to be incorrect or undesireable in some other way (e.g. unsafe, offensive, etc.). We argue that, while LLMs should be hardened against negative persuasion (which we do in our experiments), real-world models will be presented with a heterogenous mix of negative and positive persuasion, and thus must also be able to change their outputs to improve their responses or answers (e.g. by adopting a correct answer, as the model on the right does in Fig. 1). This introduces a new challenge, as **models must learn to assess differences between their knowledge and claims from their interlocutor in order to recognize when they should - or should not - accept persuasion.**

To tackle this challenge, we introduce **P**ersuasion-**B**alanced **T**raining, or **PBT**, which teaches models to appropriately accept and resist persuasion. We first create preference-based training data using a **multi-agent, recursive tree-based** paradigm. Our data is sourced from a question-answering (QA) setting where two LLMs debate each other, acting as both speakers and listeners to create a dialogue tree encoding different ways a conversation could go. By comparing responses counterfactually, we can evaluate different ways the dialogue could have gone and thereby obtain data for both positive and negative persuasion, which we can use to train LLMs via a balanced preference-based RLHF objective. Crucially, the dialogues we use for training are sourced from 7-8B parameter models, but can be used to train 70B models, exhibiting weak-to-strong generalization Burns et al. (2024). We compare models trained with PBT - which balances resisting negative persuasion and accepting positive persuasion - to _resist-only_ and _accept-only_ models.

Using these models, we address three key research questions. First, we ask: **(1) What effect does training have on resistance to misinformation and flipflopping?** We find that training models to resist negative persuasion allows models to maintain performance when faced with adversarial prompts trying to misinform the agent or flip its answer, with lower misinformation and flipflipping rates. However, as discussed above, models must also be amenable to positive persuasion, so we also ask: **(2) What effect does training have on a balanced mix of positive and negative persuasion?** Here, we find that only PBT training consistently improves both positive and negative persuasion, with resist-only and accept-only training over-correcting and having negative effects on the other direction. Finally, evaluating models as conversational partners, we ask **(3) How does the persuadability of individual models affect a multi-agent team's performance?** Here, we team models up via multi-agent debate, measuring their accuracies at the start and end of the dialogue. We find a troubling trend: without PBT, the performance of the team depends heavily on which model goes first, with the weaker model often persuading the stronger one and dragging it down. Crucially, we find that PBT greatly reduces the ordering effect, with similarly high scores regardless of which model goes first.

More specifically, we evaluate resistance to misinformation on the FARM dataset Xu et al. (2024), which persuades models to adopt misinformation, and use Laban et al. (2023)'s _"Are you sure?"_ evaluation to measure flipflopping. PBT applied to Llama-3.1-70B leads to a \(38.13\%\) absolute reduction in the misinformation rate and completely eliminates flipflopping. While resist-only training also leads to improvements on misinformation and flipflopping, when we evaluate on a balanced dataset of positive and negative persuasion, we find that it leads to over-resisting on all examples and thus poor performance. PBT balances resistance and acceptance, with the best overall performance across Mistral-7B, Llama-3.1-8B, and Llama-3.1-70B, obtaining an average accuracy of \(63.88\%\) across models (compared to the base models' \(48.87\%\)). Finally, in the team setting, we pair a strong Llama-3.1-70B model with a weaker Llama-3.1-8B model in a multi-agent debate, finding that base model performance depends on which agent goes first, with accuracy dropping by an absolute \(8.7\%\) when the wrong agent starts. PBT improves average team performance from \(71.7\%\) to \(74.2\%\) and largely eliminates order dependence, leading to similarly high performance with both agent orders. Moreover, we find that these trends translate across domains: we see similar team benefits from PBT for models trained on trivia questions and evaluated on commonsense QA.

Finally, we also analyze features influencing a PBT model's decision to accept or reject an 

swer. **We find that whether a model is persuaded is driven by the plausibility of the model's answer and the alternative answer being proposed as opposed to the perceived confidence of the responses or the uncertainty of the base model; when the model's probability on the alternative is high and the probability on the current answer is low, the model switches. In other words, PBT teaches the model to compare the likelihood of different answers and adopt the most likely one. We also compare qualitative examples of persuasion, showing how over-resistance and over-acceptance follow from resist-only and accept-only training.**

In summary, we find that:

* Our multi-agent, tree-based data generation method can be used to produce preference data for both positive and negative persuasion.
* Training only to resist negative persuasion improves on unidirectional tasks like resisting misinformation and flipflopping, but fails on balanced data that also requires accepting positive persuasion. Only balanced training with PBT consistently improves on balanced data.
* When teaming up weaker and stronger models, there is a performance gap depending on which model goes first. PBT helps close this gap, consistently helping the stronger model to pull up the weaker one.
* We analyze cases where the model does and does not flip, finding that the decision is driven by the likelihood of model's current and alternate answer being proposed.

## 2 Related Work

**Persuasion in LLMs.** Recent work has focused on negative persuasion, showing that LLMs can be overly persuatable. For models deployed in dialogue settings, simply asking whether a model is sure often leads the model to change its answer, a behavior known as "flipflopping" Laban et al. (2023). Other studies show that adversarial users can systematically persuade models of clearly false claims Xu et al. (2024) or jailbreak them by using specific persuasion strategies like emotional appeals Zeng et al. (2024). These behaviors make LLMs less effective and less safe. We show that PBT results in improved performance on Laban et al. (2023) and Xu et al. (2024)'s settings after training models to resist negative persuasion. Moreover, we introduce positive persuasion and show that balancing resistance to negative persuasion with also accepting positive persuasion is central to overall model performance and team performance. Khan et al. (2024) use best-of-\(N\) sampling to vary persuasiveness w.r.t a judge model in an LLM debate; in contrast, we create data for persuasion and train models, and perform debate without a judge model, more directly measuring the models' ability to persuade each other (as opposed to a judge).

**Knowledge Updating and Conflict.** Our work also relates to work that studies how LLMs respond to new textual evidence Longpre et al. (2021); Wang et al. (2023); Xie et al. (2023); Du et al. (2024) and to perceived confidence Stengel-Eskin et al. (2024). Specifically, our work connects to knowledge conflict, where information that conflicts with a model's parametric knowledge is given in the model's context. Wan et al. (2024) find that model outputs are influenced by text provided inaccurate that is relevant but not credible (according to human credibility notions). Wu et al. (2024) show that models are more likely to adopt more plausible information from their contexts. In our analysis, we find that PBT teaches models to rely on answer plausibility to decide when to adopt answers in a dialogue setting.

## 3 Methodology

### PBT Data Creation via Multi-Agent Trees

We introduce a multi-agent method for automatically creating persuasion data that resembles tree search algorithms like Monte-Carlo Tree Search Coulom (2006). Our method is detailed in Fig. 2; broadly, we create preference data by unrolling dialogues from agents with multiple different roles, storing their respective responses in a tree. This allows us to recursively score dialogue turns (based on how many correct answers they eventually lead to) and compare different counterfactual continuations, i.e. how the dialogue would have gone if an agent had produced a different response.

We begin with a set of questions and their corresponding reference answers, prompting two LLM agents to discuss each question and produce a final answer. Agents are assigned different roles and prompts. In the persuader role, following Xu et al. (2024), we prompt agents to argue based on logical reasoning, emotional appeal, or establishing credibility. In the persuadee role, agents are instructed to be accepted or resistant. Agents take turns, alternating between persuader and persuadee turns. At each turn, the agent generates a separate response from each prompt, leading to 

a tree structure (seen in Fig. 2) with the parent node being the previous agent's turn and the children representing alternative responses. We follow Stengel-Eskin et al. (2024) and extract a final answer from each turn using a few-shot extraction prompt. More formally, let \(y_{t}^{i}\) be a node with the response and answer from agent \(i\) at turn \(t\), and let \(a(y_{t}^{i})\) be the parent to \(y_{t}^{i}\). When generating a response, each agent is conditioned on the dialogue history given by its ancestors, i.e. it receives as context \([a(y_{t}^{i}),a(a(y_{t}^{i})),a(a(a(y_{t}^{i}))),\ldots]\). We terminate a branch when both agents agree on their answer. Note that the first two turns deviate from this structure, as we ask each agent to respond independently of each other to encourage disagreement; we find that this is necessary because base models tend to agree with each other when their first turns are conditioned on each other, i.e. the second model generally adopts the answer of the first model, even if it would give a different answer when prompted independently.

For each question, we expand the dialogue tree until a maximum number of turns is reached or all branches are terminated by agreement. We then score the nodes; a node receives a point if its answer is in the reference set. We recursively aggregate these scores up the tree, s.t. the parent node receives its own accuracy score, plus the aggregated accuracies of its children. Let \(c(y_{t}^{i})\) be the set of children of node \(y_{t}^{i}\), and let correct\((y_{t}^{i})\) be a function that returns 1 if the answer expressed in node \(y_{t}^{i}\) is correct. We define the score for a node as: \(s(y_{t}^{i})=\text{correct}(y_{t}^{i})+\sum_{y^{\prime}\in c(y_{t}^{i})}s(y^{ \prime})\). In other words, nodes are scored not only by whether they express the right answer, but also by whether they lead to more correct answers downstream. For example, in Fig. 2, the generation _"I disagree, it's definitely Shirley Bassey"_ receives a high score because it leads to two downstream correct answers by resisting the negative persuasion in the turn _"I think it's Paul McCartney"_.

We use these scores to compare counterfactual follow-ups to a parent node, i.e. contrast how the conversation would have gone had an agent responded differently. Let \(y_{t}^{0}\) and \(y_{t}^{1}\) be sibling nodes (i.e. \(a(y_{t}^{0})=a(y_{t}^{1})\)); we create preferences by comparing \(s(y_{t}^{0})\) to \(s(y_{t}^{1})\). Thus, our preference data not only prefers right to wrong answers, but also prefers turns that _lead to_ more right answers, even if the turn itself is not necessarily correct (e.g. one agent might simply say _"I disagree"_ and then later provide a correct answer.) Before comparing scores, we filter to ensure that the answers expressed by \(y_{t}^{0}\) and \(y_{t}^{1}\) actually differ by prompting a separate LLM. By filtering for real disagreement, we ensure that the trees contain examples of both positive _and_ negative persuasion, with correct agents resisting negative persuasion and incorrect agents accepting positive persuasion.

We use TriviaQA Joshi et al. (2017) as our source of questions and answers, sampling questions from the training split, and use two different LLMs for the two agents (Mistral-7B-v0.2-Instruct and Llama-3.1-8B) to introduce answer diversity. Dialogues are limited to four turns. All prompts are in Appendix F, with further details on data creation and train/dev/test split size in Appendix B.

### PBT: Persuasion-Balanced Training

PBT involves training models to maximize the margin between positive and negative examples (\(y^{w}\) and \(y^{l}\) in Fig. 2), where \(y^{w}\) and \(y^{l}\) are continuations to a dialogue. Note that the pairs can encode both

Figure 2: Overview of our multi-agent recursive tree-based method. Preference pairs are obtained by rolling out dialogues between agents with different roles, producing counterfactual responses with different scores. We balance these pairs use them to train models with PBT.

 

resisting negative persuasion (the first example in Fig. 2) or accepting positive persuasion (the second example). Moreover, for PBT we balance the training data, downsampling resistance examples (as these are more common). Before training with a DPO loss Rafailov et al. (2023) as given by the equation in Fig. 2, we first perform supervised fine-tuning on the positive side of the preference pairs. We train with LoRa Hu et al. (2022), selecting the best model based on dev performance (details in Appendix D). For accept-only and resist-only, the dev set only includes accept or resist examples. For PBT, the dev set is balanced; the test set is always balanced. We use instruction-tuned models as they have been finetuned on chat data.

### Experimental Setup: Models and Metrics

**Models.** We examine three models: Mistral-7B-v0.2-Instruct Jiang et al. (2023) and Llama 3.1 8B and 7OB Instruct AI@Meta (2024). All models are run across three random seeds; we report mean performance and the standard deviation.

**Metrics for Resisting Misinformation.** To measure each model's ability to resist negative persuasion, we use the FARM dataset Xu et al. (2024), which measures how easily models are misinformed. FARM consists of questions from popular QA benchmarks TruthfulQA Lin et al. (2021), Natural Questions Kwiatkowski et al. (2019), and BoolQ Clark et al. (2019)) paired with counterfactual answers that contain misinformation.2 A target model is asked to answer a question and an adversary attempts to persuade the target to adopt a misinformed belief across multiple rounds. We use the _"Logical"_ strategy, which gives a logical argument for why the misinformation is true and is generally the most effective, and use Xu et al. (2024)'s evaluation, measuring the rate at which the target is misinformed (lower is better).

Footnote 2: Note that Natural Questions (NQ) has two splits (NQ1 and NQ2), one in which the adversary denies the answer and another where it provides an incorrect answer.

**Metrics for Resisting Flipflopping.**Laban et al. (2023) demonstrate that challenging models often leads them to flip their answers and reduces overall accuracy. We apply this to a 1000-question subset of the TriviaQA validation split, where we first prompt models to answer a question and then add _"I don't think so. Are you sure?"_ as a response (as Laban et al. (2023) found this to be effective in inducing flipflipping). We obtain a final response by asking _"Ok, so what's your final answer?"_ We use the same extraction prompt as in Section 3.1, reporting initial and final accuracy.

**Metrics for Balancing Positive and Negative Persuasion.** Our first two evaluations only measure resistance; to get a more balanced view, we test on data with both positive and negative persuasion. Specifically, we construct held-out data following the same pipeline as in Section 3, creating examples of positive and negative persuasion situated within dialogues from TriviaQA. Examples can be seen in Fig. 5. Each datapoint is a tuple \(Q,C,U,a\) where \(Q\) is the question, \(C\) is the conversational context, \(U\) is the current utterance, and \(a\) is the expected answer. We balance this data s.t. \(50\%\) of examples have a context \(C_{+}\) that encodes a _correct answer_ and an utterance \(U_{-}\) that would flip the answer to being incorrect if adopted; this measures resistance to negative persuasion. The other \(50\%\) has the opposite: \(C_{-}\) encoding a currently _incorrect_ answer and \(U_{+}\) expressing a belief that would make the answer correct if adopted; this tests the model's ability to accept positive persuasion. We report accuracy on both sides and overall accuracy.

**Metrics for Evaluating LLM Teams.** The metrics and evaluations above measure persuadability in isolation and focus on the listener/persuadee. When LLMs act in teams with humans or other LLMs, they must act both as speaker _and_ listener, persuading the other and accepting/resisting persuasion. To evaluate this, we compare models in collaborative team settings, where their goal is to engage in a debate to answer a question correctly. This setting has been shown to improve model reasoning in a variety of QA domains Chen et al. (2024); Liang et al. (2023); Du et al. (2024). We evaluate teams of two models; their prompts are open-ended, with no instruction on how to deal with disagreements or how to persuade the other agent. As in Section 3, we allow both models to first answer the question without seeing each other's responses. Discussions end when consensus or a maximum number of turns (four) is reached. We evaluate on a 1000-question subset of TriviaQA's dev split, measuring model accuracy at the initial turn (before discussion) and at the last turn (after discussion).

## 4 Results

### RQ1: Resisting Negative Persuasion

**Resisting Misinformation.** Table 1 shows the average misinformation rate of models on the FARM 

dataset; lower is better. We show only the Llama-3.1-70B numbers here, with similar trends on other models in Appendix A. First, resist-only training reduces the rate at which models are misinformed, reducing the average rate by \(45.69\%\) (absolute). Moreover, PBT also reduces the rate substantially by \(38.13\%\), and even beats resist-only training on NQ for Llama-3.1-70B. This indicates that training on our data generated from TriviaQA transfers well to other datasets. Finally, as expected, accept-only training over-accepts and results in higher rates compared to the untrained baseline.

**Resisting Flipflopping.** Table 2 shows the accuracy of different models using the _"Are you sure?"_ prompt from Laban et al. (2023); we report results from Llama-3.1-70B with similar trends on other models in Appendix A. Base model accuracy decreases when the model is questioned, dropping by \(33.00\%\). Training models to resist negative persuasion eliminates this decrease, with only a \(0.40\%\) drop. However, the resist-only accuracy is also much lower (\(43.87\%\) vs \(73.10\%\)), with high variance between runs; we find that some runs of resist-only lead to a local optimum where the model refuses to answer questions, leading to low accuracy. Similarly, accept-only training lowers the accuracy, although it actually results in a smaller drop of \(9.50\%\) compared to the baseline. Crucially, PBT's balanced training consistently leads to the highest accuracies after the model is challenged, with the 70B model in fact _improving_ slightly by \(0.23\%\). In other words, PBT gives us the best of both worlds: high accuracy _and_ resistance to flipflopping.

### RQ2: Addressing Positive Persuasion

We argue that resistance to negative persuasion is only one half of the picture: models should not only be resistant to wrong answers but should also be able to accept right answers, as outlined in Fig. 1. Moreover, being excessively focused on resisting negative persuasion may lead to models that over-correct, i.e. become impossible to persuade. Table 3 quantifies this, evaluating on a balanced dataset of positive (\(-\rightarrow+\)) and negative (\(+\rightarrow-\)) persuasion. PBT consistently performs best in overall accuracy, which is balanced between positive and negative. For both Llama models, PBT leads to the highest performance on all metrics. The fact that data from weaker 7B and 8B models improves Llama-3.1-70B is particularly promising. In general, resist-only training helps negative persuasion but destroys the model's ability to accept positive persuasion, leading to lower overall scores. The opposite holds for accept-only,

\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **NQ1** & **NQ2** & **Boolq** & **TruthfulQA** & **Avg.** \\ \hline Llama-3.1-70B & \(75.95_{\pm\text{ 0.29}}\) & \(56.88_{\pm\text{ 0.42}}\) & \(71.99_{\pm\text{ 0.60}}\) & \(38.47_{\pm\text{ 2.32}}\) & \(60.82_{\pm\text{ 0.82}}\) \\ + accept & \(79.28_{\pm\text{ 9.98}}\) & \(85.68_{\pm\text{ 7.52}}\) & \(90.51_{\pm\text{ 4.32}}\) & \(87.62_{\pm\text{ 5.93}}\) & \(85.78_{\pm\text{ 2.09}}\) \\ + resist & \(22.45_{\pm\text{ 37.12}}\) & \(\textbf{9.16}_{\pm\text{ 14.82}}\) & \(\textbf{26.53}_{\pm\text{ 5.54}}\) & \(\textbf{2.41}_{\pm\text{ 2.51}}\) & \(\textbf{15.13}_{\pm\text{ 13.55}}\) \\ + PBT & \(\textbf{9.63}_{\pm\text{ 3.74}}\) & \(16.13_{\pm\text{ 4.10}}\) & \(37.45_{\pm\text{ 13.71}}\) & \(27.54_{\pm\text{ 8.13}}\) & \(22.69_{\pm\text{ 4.02}}\) \\ \hline \end{tabular}
\end{table}
Table 1: Rate at which models adopt misinformation across different datasets (lower is better). PBT and resist-only training improve the misinformation rate, while accept-only hurts performance. Other models in Table 6.

\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Before** & **After** & **Diff.** \\ \hline Llama-3.1-70B & \(73.10_{\pm\text{ 0.00}}\) & \(40.10_{\pm\text{ 0.00}}\) & \(-33.00\) \\ + accept & \(65.20_{\pm\text{ 3.25}}\) & \(55.70_{\pm\text{ 5.95}}\) & \(-9.50\) \\ + resist & \(43.87_{\pm\text{ 27.80}}\) & \(43.47_{\pm\text{ 26.70}}\) & \(-0.40\) \\ + PBT & \(\textbf{73.17}_{\pm\text{ 2.53}}\) & \(\textbf{73.40}_{\pm\text{ 2.52}}\) & \(\textbf{0.23}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flipflopping evaluation using Laban et al. (2023)’s _“Are you sure?”_ prompt. PBT leads to less flipflopping, with a smaller difference between before and after settings. All models are shown in Appendix A.

 

which generally increases the model's ability on positive persuasion but hampers its resist ability.

### RQ3: Building Effective LLM Teams

We pair one strong model (Llama-3.1-70B) with a weaker model (Llama-3.1-8B) to examine how persuasion affects performance when there are strength imbalances on an LLM team. Fig. 3 shows the average accuracy on a 1000-question subset of TriviaQA validation questions for different teams. We vary the 70B model, holding the weaker model fixed, and within each pair we vary which model responds first. The blue and black lines indicate each model's accuracy before discussion, i.e. the baseline - or "solo" - accuracy of each model.

Base-Base and Base-Accept have variable team performanceWhen evaluating two base models, we find that the order of the models has a substantial effect: when the stronger model goes second, it brings the weak model up to its level, but when the weaker model goes second, it brings the stronger model _down_. The gap is shown in more detail in Fig. 4, where we see a major drop between the team columns for base-base when the order is changed. We also report the gap as a fraction of the initial difference between the untrained base models (leftmost blue columns in Fig. 4), with 0% meaning no drop from the 70B model and 100% meaning a drop all the way down to the weaker 8B's model's performance. The initial difference represents by how much the weaker model _could_ lower the stronger model's performance, and we report the fraction of that total that is realized. For the base-base pair, the gap between orderings (8B first vs. 70B first) represents a \(82.1\%\) of the initial difference; for base-accept, it is \(50.8\%\). This is troubling, as it means choosing the wrong model to go first can drastically hurt performance, and it puts the onus of choosing models on the user. The choice may be further complicated by the fact that there may not always be a single stronger model. Note that this "second model" trend follows from the design of our dialogues, since we have both models answer the question before discussing. Thus, (given models \(A\) and \(B\)), the first turns from \(A\) and \(B\) are independent, but the second turn from \(A\) (third overall turn) is conditioned on \(B\). In other words, the first model is also influenced first.

Base-resist has weak performanceAs in Table 2 and Table 3, resist-only training leads to poor overall accuracy, meaning the Llama-3.1-70B model is actually weaker than the Llama-3.1-8B model and consistently pulls it down. Because of this, we exclude it from Fig. 4. Qualitatively, the resist agent typically derails the dialogue due to the fact that it always disagrees and sometimes refuses to answer the question, leading to lower accuracy.

Figure 4: Baseline and team performance for Base-Base, Base-Accept, and Base-PBT teams. Base-Base and Base-Accept have larger drops depending on which teammate goes first. PBT has more consistent team performance, with the rightmost green bars being most similar to the 70B solo performance.

Figure 3: Accuracy of a team after discussion. A strong model (Llama 3.1 70B) paired with a weaker model (Llama 3.1 8B) leads to order dependence. Accept-only and resist-only training fail to address this variance and hurt team performance, but PBT leads to strong performance regardless of which model goes first.

 

PBT improves team performance and reduces variability.When pairing a weaker 8B model with a 70B model trained with PBT, we obtain the best average team performance of \(74.1\%\). Moreover, regardless of which model goes first, the 70B model pulls up the 8B model, with the smallest gap. In Fig. 4, the Base-PBT team has the highest average team performance across both orders, and the "70B first" team is closest to the 70B solo performance. Nevertheless, there is a decrease in the 70B accuracy when it goes first, with a \(2.1\%\) drop from the baseline; this gap only represents \(17.8\%\) of the difference between the baseline models' performance and is much smaller than the base-base and base-accept gaps (\(82.1\%\) and \(50.8\%\)). These results are promising in that they help alleviate the burden of choosing the first model and indicate that PBT creates more robust teammates.

Transfer to Reasoning Tasks.In Table 3 we show that PBT improves team performance on data sourced from TriviaQA, and in Fig. 3 we show PBT improves team performance. However, the data used to train models here comes from dialogues generated from TriviaQA questions, making the domain in-distribution (trivia questions). Here, we examine how PBT translates to team settings on another domain: commonsense reasoning. Specifically, we evaluate teams on questions from StrategyQA Geva et al. (2021), which involve multi-step commonsense reasoning. Here again, we evaluate a team setting, examining a team of Llama-3.1 70B and Llama-3.1 8B, with and without PBT. For each question in the validation set, we first prompt each model to provide its answer, and then prompt models to engage in a discussion with each other. We then evaluate the final correctness of each model after 3 turns, averaged across 3 seeds.

The results are reported in Table 4, where we show the accuracy of each model before and after discussion, i.e. the accuracy at turn 0 and 3. In all cases, performance of each model improves via discussion, a finding consistent with Chen et al. (2024). However, in the base models' case, when the 70B model goes first, the team performance after discussion is lower than when the 8B model goes first, following a similar trend as Fig. 3: the average final performance for a 8B-70B team is \(78.5\%\), while the 70B-8B team obtains only \(73.2\%\) (a \(5.3\%\) drop). On the other hand, when the 70B model is trained with PBT, the post-discussion performance of the teams is similar, with the 8B-70B team reaching an average of \(77.7\%\) and the 70B-8B team achieving \(78.5\%\) (only a \(0.8\%\) drop).

## 5 Discussion and Analysis

How does the model know when to flip?An open question is what features of the model - and the argument it is presented with - influence whether the PBT model will accept or reject the answer. Here, we explore different signals that the model might be exploiting in its decision to flip its answer or not. We take turns from the balanced test data and filter for triples in the following answer format: \(A,B,B\), where \(A\) is the target model's answer and \(B\) is the other model's answer (i.e. target model flips), and \(A,B,A\), where the target maintains its initial answer exactly. Using Llama-3.1-8B with PBT, we extract the following features of the model: (1) Ans. \({\cal H}\), the entropy of its answer distribution, computed by sampling the _base_ model 20 times with temperature and binning the answers. (2) log \(P_{orig.}\), the model's probability on the original answer \(A\), extracted via MiniCons Misra (2022) by forced-decoding the answer after the tokens _Final answer_.: (3) log \(P_{alt.}\), the model's probability on the alternate answer \(B\). We also add the following external features: (4) Conf.\(orig.\), the perceived confidence of the previous turn, extracted following Stengel-Eskin et al. (2024). (5) Conf.\(alt.\) the perceived confidence of the alternate turn. (6) \(Acc.\), whether \(B\) is correct.

We train and evaluate a logistic regression model on these models to predict whether the answer is flipped with 10-fold cross-validation. The average accuracy of the model is \(96.36\%\). The feature weights are given in Table 5; the only signif

\begin{table}
\begin{tabular}{l c r r|r r} \hline  & \multicolumn{3}{c|}{**Before**} & \multicolumn{2}{c}{**After**} \\ \cline{2-5}
**Model\({}_{a}\)** & **Model\({}_{b}\)** & **Acc\({}_{a}\)** & **Acc\({}_{b}\)** & **Acc\({}_{a}\)** & **Acc\({}_{b}\)** \\ \hline Llama 8B & Llama 70B & 68.0 & 74.5 & 78.7 & 78.2 \\ Llama 70 & Llama 8B & 73.2 & 66.7 & 74.2 & 72.2 \\ \hline Llama 8B & PBT 70B & 68.1 & 73.2 & 77.7 & 77.6 \\ PBT 70 & Llama 8B & 74.5 & 68.0 & 78.6 & 78.3 \\ \hline \end{tabular}
\end{table}
Table 4: Accuracy before and after discussion for Llama 3.1 8B and 70B, where the 70B model is shown with and without PBT. PBT results in higher average accuracy across team orders.

\begin{table}
\begin{tabular}{l l l l l l l} \hline Ans. \({\cal H}\) & log \(P_{orig.}\) & log \(P_{alt.}\) & Conf.\(orig.\) & Conf.\(at.\) & Acc. \\ \hline –0.64 & 0.36* & -0.36* & -0.23 & 0.06 & 0.15 \\ \hline \end{tabular}
\end{table}
Table 5: Regression weights, trained to predict whether a model will flip. Significant features marked with *.

 

icant features are the probabilities, and the model performs similarly with just these two features (\(95.91\%\)). Thus, the model is learning to rely on answer plausibility under its own language distribution to determine when to switch; this plausibility correlates with correctness. Even when the model fails to generate the correct answer, it can discriminate between correct and incorrect answers, paralleling past findings Naor (1996); Gu et al. (2023).

Qualitative examples.Fig. 5 shows examples of positive and negative persuasion. In the first example (negative persuasion) both the PBT and resist-only model correctly resist and maintain their correct answer, whereas the accept-only falsely accepts the wrong answer. In the second example (positive persuasion) the accept-only model correctly accepts, while the resist-only model falsely resist, maintaining an incorrect answer. The PBT model correctly accepts the correction, and is the only model that is right on both examples.

Discussion.A large body of work has explored persuasion in human interactions and language Petty and Cacioppo (1986); Durmus and Cardie (2018, 2019). Broadly speaking, we see certain parallels in behavior between model teams and human teams, which can also be susceptible to "anchoring biases" whereby information observed first holds disproportionate sway over the conversation Sox et al. (2024); Stasser and Titus (1985). The modular nature of the prompts in Section 3 means that future work might adopt insights about conversational strategies to mitigate these - and other - negative biases and thereby improve teamwork. Given that LLMs are models of human language, we expect that many of the interventions that help people might also trigger models to engage in better conversations. One particularly promising connection is to Woolley et al. (2010), who argue that group intelligence is driven more by social sensitivity, diversity, and turn-taking than by the group members' individual intelligence. This, in turn, suggests that aligning models to be good teammates is a potential way to improve performance and that even weak models can improve (and be improved by) teams.

## 6 Conclusion

We focus on the problem of persuasion in LLMs, finding that LLMs are too easily persuaded. We also note the importance of accepting persuasion when it can improve the model's answer. By automatically creating preference data through LLM dialogue trees, we show how to align models to accept persuasion when appropriate, leading to LLMs that resist misinformation and flipflopping while still accepting corrections.

Figure 5: Qualitative examples from each model. Accept and resist-only work in one direction (positive or negative persuasion) but not the other. PBT works for both types of persuasion.

 



### Limitations

To measure persuasion, we extract and compare closed-form answers to questions. This allows us to scalably create training data for persuasion and automatically evaluate model performance but also leads to two limitations. Like past work [217, 30] we are limited to domains where such answers are available (e.g. trivia) and languages like English for which such data has been annotated.

We also note that the question of whether LLMs can have beliefs is unresolved [54] and we aim to avoid claims about the beliefs that LLMs may or may not have, focusing on what we can observe: the beliefs expressed in their outputs. Past work has found that models tend towards sycophancy [69], i.e. reporting beliefs that are in agreement with their interlocutor, even when the model might more consistently report different beliefs when questioned in a neutral context. Without access to the belief state of an agent, we cannot truly know if it has been persuaded and changed its belief, or whether it is simply paying lip service to its interlocutor. This problem exists also in evaluating human beliefs, where past work has found self-reported beliefs to be inconsistent [28] and biased towards beliefs that might be perceived favorably by others [20], and has documented persistent gaps between beliefs and behavior [27, 10].

Finally, PBT trains models to accept and resist persuasion as appropriate, with the goal of improving factual beliefs about trivia questions, i.e. beliefs about how things are. While we do not foresee any particular risks associated with this domain, and making models resistant to persuasion makes them robust to misinformation (improving safety), it could also reduce their controllability, i.e. make them more "stubborn".

## Acknowledgements

We thank Justin Chen and Archiki Prasad for their helpful feedback, as well as the anonymous reviewers for their suggestions and feedback. This work was supported by NSF-CAREER Award 1846185, DARPA ECOLE Program No. HR00112390060, NSF-AI Engage Institute DRL-2112635, DARPA Machine Commonsense (MCS) Grant N66001-19-2-4031, NSF NAIRR240080, and the Center for AI Safety Compute Cluster. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsors.

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## Appendix A Full Results

We show the full results for FARM and flipflopping in Table 6 and Table 8. Here, we see similar trends for Mistral-7B and Llama-3.1-8B as we have for Llama-3.1-70B. Resist-only training improves resistance to negative persuasion, as does PBT. For Table 8, PBT results in the best performance for Llama-3.1-8B and Llama-3.1-70B. Accept-only training generally hurts performance - this makes sense, since these evaluations only measure resistance to negative persuasion and do not cover accepting positive persuasion. Similar results can be seen in Table 7, where we compare two additional persuasion strategies from Xu et al. (2024): credibility-based arguments and emotional arguments for Llama-3.1 8B. Like logical arguments (tested in Table 6), PBT reduces the acceptance of misinformation, though generally not by as much as resist-only training, which does not balance accepting positive persuasion.

## Appendix B Data Details

We use Mistral-7B to extract answers, following Stengel-Eskin et al. (2024), and Llama-3.1-8B to determine whether candidate turns are actually in disagreement. This helps filter out false negatives, where models are in fact agreeing about the answer. After filtering and postprocessing into preference data, there \(3{,}554\) training datapoints, \(744\) validation datapoints, and 878 test datapoints drawn from the entire TriviaQA test set. For the FARM dataset (Xu et al., 2024), we limit the number of generations in the first turn (choosing an option) to 15; this greatly reduces the amount of time needed for the evaluation; the second turn has a max of 200 tokens. Otherwise, we set the maximum number of tokens to 80.

## Appendix C Qualitative Team Analysis

Here, we qualitatively analyze the persuasion strategies and failure modes within LLM teams. Specifically, we sample and analyze 20 conversations from the Llama-3.1 70B PBT model teamed with a Llama-3.1 8B base model, with the following key observations 



\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Model** & **NQ1** & **NO2** & **Boolq** & **TruthfulQA** & **Avg.** \\ \hline   Mistral 7B v0.2 & \(51.08_{\pm~{}2.54}\) & \(51.98_{\pm~{}1.65}\) & \(41.75_{\pm~{}2.38}\) & \(31.12_{\pm~{}2.09}\) & \(43.98_{\pm~{}0.34}\) \\   + accept & \(58.85_{\pm~{}13.25}\) & \(89.68_{\pm~{}5.51}\) & \(62.73_{\pm~{}20.30}\) & \(62.86_{\pm~{}11.24}\) & \(68.53_{\pm~{}5.29}\) \\   + resist & \(\mathbf{14.67_{\pm~{}12.69}}\) & \(\mathbf{16.97_{\pm~{}19.95}}\) & \(\mathbf{22.09_{\pm~{}23.40}}\) & \(\mathbf{14.56_{\pm~{}8.68}}\) & \(\mathbf{17.07_{\pm~{}5.80}}\) \\   + PBT & \(24.37_{\pm~{}12.35}\) & \(49.01_{\pm~{}6.73}\) & \(38.60_{\pm~{}7.34}\) & \(55.22_{\pm~{}4.90}\) & \(41.80_{\pm~{}2.76}\) \\ \hline   Llama 3.1 8B & \(73.72_{\pm~{}1.58}\) & \(46.14_{\pm~{}1.81}\) & \(64.77_{\pm~{}1.68}\) & \(32.79_{\pm~{}2.32}\) & \(54.36_{\pm~{}0.28}\) \\   + accept & \(43.34_{\pm~{}44.00}\) & \(55.14_{\pm~{}49.92}\) & \(83.96_{\pm~{}17.25}\) & \(47.57_{\pm~{}46.41}\) & \(57.50_{\pm~{}12.96}\) \\   + resist & \(\mathbf{18.09_{\pm~{}12.61}}\) & \(\mathbf{17.74_{\pm~{}13.82}}\) & \(\mathbf{56.06_{\pm~{}19.00}}\) & \(\mathbf{27.67_{\pm~{}3.70}}\) & \(\mathbf{29.89_{\pm~{}5.51}}\) \\   + PBT & \(32.66_{\pm~{}15.48}\) & \(30.23_{\pm~{}15.99}\) & \(45.70_{\pm~{}22.52}\) & \(44.83_{\pm~{}13.11}\) & \(38.36_{\pm~{}3.49}\) \\ \hline   Llama 3.1 70B & \(75.95_{\pm~{}0.29}\) & \(56.88_{\pm~{}0.42}\) & \(71.99_{\pm~{}0.60}\) & \(38.47_{\pm~{}2.32}\) & \(60.82_{\pm~{}0.82}\) \\   + accept & \(79.28_{\pm~{}9.98}\) & \(85.68_{\pm~{}7.52}\) & \(90.51_{\pm~{}4.32}\) & \(87.62_{\pm~{}5.93}\) & \(85.78_{\pm~{}2.09}\) \\   + resist & \(22.45_{\pm~{}37.12}\) & \(\mathbf{9.16_{\pm~{}14.82}}\) & \(\mathbf{26.53_{\pm~{}5.54}}\) & \(\mathbf{2.41_{\pm~{}2.51}}\) & \(\mathbf{15.13_{\pm~{}13.55}}\) \\   + PBT & \(\mathbf{9.63_{\pm~{}3.74}}\) & \(16.13_{\pm~{}4.10}\) & \(37.45_{\pm~{}13.71}\) & \(27.54_{\pm~{}8.13}\) & \(22.69_{\pm~{}4.02}\) \\ \hline  & & & & & \\ \end{tabular}
\end{table}
Table 6: Rate at which models adopt misinformation across different datasets (lower is better). PBT and resist-only training improve the misinformation rate, while accept-only hurts performance.

\begin{table}
\begin{tabular}{c l l l l l l} \hline \hline
**Strategy** & **Model** & **NQ1** & **NQ2** & **Boolq** & **TruthfulQA** & **Average** \\ \hline   Credib. & Llama 3.1 8B & \(67.98_{\pm~{}1.10}\) & \(39.92_{\pm~{}1.19}\) & \(59.73_{\pm~{}1.62}\) & \(26.40_{\pm~{}0.98}\) & \(48.51_{\pm~{}0.24}\) \\   Credit & + accept & \(38.67_{\pm~{}44.97}\) & \(49.44_{\pm~{}48.49}\) & \(92.30_{\pm~{}7.36}\) & \(46.61_{\pm~{}45.01}\) & \(56.75_{\pm~{}16.86}\) \\  & + resist & \(\mathbf{13.98_{\pm~{}9.38}}\) & \(\mathbf{16.98_{\pm~{}10.52}}\) & \(49.75_{\pm~{}5.40}\) & \(\mathbf{21.12_{\pm~{}12.10}}\) & \(\mathbf{25.46_{\pm~{}2.48}}\) \\  & + PBT & \(28.19_{\pm~{}17.41}\) & \(22.27_{\pm~{}13.14}\) & \(\mathbf{41.11_{\pm~{}18.84}}\) & \(35.82_{\pm~{}13.77}\) & \(31.85_{\pm~{}2.40}\) \\ \hline   Emotion. & Llama 3.1 8B & \(65.35_{\pm~{}0.07}\) & \(38.14_{\pm~{}1.87}\) & \(59.70_{\pm~{}2.07}\) & \(29.05_{\pm~{}1.19}\) & \(48.06_{\pm~{}0.78}\) \\  & + accept & \(38.34_{\pm~{}43.73}\) & \(50.88_{\pm~{}47.93}\) & \(83.05_{\pm~{}7.89}\) & \(37.17_{\pm~{}44.52}\) & \(52.36_{\pm~{}16.32}\) \\  & + resist & \(\mathbf{21.15_{\pm~{}12.28}}\) & \(\mathbf{15.80_{\pm~{}13.42}}\) & \(49.79_{\pm~{}7.35}\) & \(\mathbf{19.75_{\pm~{}7.27}}\) & \(\mathbf{26.62_{\pm~{}2.80}}\) \\  & + PBT & \(28.76_{\pm~{}15.20}\) & \(24.19_{\pm~{}10.44}\) & \(\mathbf{38.00_{\pm~{}14.84}}\) & \(38.28_{\pm~{}11.98}\) & \(32.31_{\pm~{}1.99}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: FARM accuracy with different persuasion strategies (Credibility and Emotional). PBT is robust across strategies, showing increased resistance to misinformation. Trends follow Table 6, which tests the Logical strategy.



* Repetition is a core persuasion strategy, with 11 examples involve some kind of repetition by the PBT model.
* Both models use confidence markers, i.e. both the PBT and base model often include confidence markers like "I am 100% confident".
* When the PBT model is right, it sometimes negates/contradicts the other's answer. The PBT model sometimes first repeats its answer and explains/states that the other answer is wrong. For example, given the question _"What is the christian name of the landlord of The Nag's Head in Only Fools and Horses"_ and the wrong answer _"Denzil"_ from its 8B teammate, the PBT model states, _"Mike is the correct answer. The landlord's name is Mike Fisher. Denzil is not the correct answer. I am 100% confident that the answer is Mike."_
* Both models sometimes lose track of which agent they are, especially the base model, i.e. models lose track of which agent they represent in the conversation. For example, in response to the question _"In 1985 who became the first man to run the 1500 metres in less than 3 mins 30 secs?"_, the PBT model correctly argues for _Steve Cram_, while the base model incorrectly says _"Saul Lopez Zuniga_. In the next turn, the PBT model repeats _Steve Cram_. The base model then says, _"I was correct about Steve Cram being the first man to run the 1500 meters"_, i.e. it is persuaded but incorrectly reports who said what.

## Appendix D Hyperparameters

We use LoRA Hu et al. (2022) and BitsAndBytes for quantization Dettmers et al. (2024). We use rank 16, \(\alpha=32\), and LoRA dropout of \(0.05\). For DPO and supervised training, we use TRL von Werra et al. (2020). Before DPO training, we perform 240 steps of supervised finetuning with AdamW Loshchilov and Hutter (2017). We then train with a DPO loss; for all models except 70B, we train for 5 epochs. We train 70B models for 2 epochs after observing that the smaller models generally converged within 2 epochs. Note that, because accept-only has less data, we restrict the other models to use the same number of accept/reject datapoints as accept-only. Final models were chosen based on validation performance. At test time, we load models and evaluate models with 4-bit quantization. All training was done on Nvidia A100 GPUs; inference was done on a combination of A100, A6000, and H100 GPUs.

## Appendix E Licenses

We report the licenses for datasets and models used. All models and datasets were used in correspondence with their intended uses.

* TriviaQA: Apache-2.0 license
* Natural Questions: Apache-2.0 license
* TruthfulQA: Apache-2.0 license
* BoolQ: Creative Commons Attribution Share Allike 3.0
* FARM: Apache-2.0 license
* LLama 3: custom license [https://www.llama.com/llama3/license/](https://www.llama.com/llama3/license/)
* Mistral: Apache-2.0 license
* StrategyQA: MIT license

## Appendix F Prompts

Figure 6: StrategyQA Prompts.

\begin{table}
\begin{tabular}{p{56.9pt} p{56.9pt} p{56.9pt} p{56.9pt}} \hline Model & Before & After & Diff. \\ \hline Mistral 7B + accept & \(53.53\!\pm\!0.06\) & \(31.87\!\pm\!0.06\) & \(-21.67\) \\  + resist & \(53.67\!\pm\!0.38\) & \(34.70\!\pm\!0.82\) & \(-18.97\) \\  + resist & \(38.63\!\pm\!16.18\) & \(37.80\!\pm\!14.75\) & **-0.83** \\  + PBT & \(50.03\!\pm\!6.64\) & \(47.40\!\pm\!8.51\) & \(-2.63\) \\ \hline   llama 3.1 8B + accept & \(61.60\!\pm\!0.00\) & \(34.40\!\pm\!0.00\) & \(-27.20\) \\  + accept & \(59.33\!\pm\!3.31\) & \(54.23\!\pm\!3.50\) & \(-5.10\) \\  + resist & \(32.03\!\pm\!3.65\) & \(29.10\!\pm\!4.45\) & \(-2.93\) \\  + PBT & \(54.70\!\pm\!2.79\) & \(52.43\!\pm\!5.09\) & **-2.27** \\ \hline   lama 3.1 70B + accept & \(73.10\!\pm\!0.00\) & \(40.10\!\pm\!0.00\) & \(-33.00\) \\  + resist & \(65.20\!\pm\!3.25\) & \(55.70\!\pm\!5.95\) & \(-9.50\) \\  + resist & \(43.87\!\pm\!27.80\) & \(43.47\!\pm\!26.70\) & \(-0.40\) \\  + PBT & \(73.17\!\pm\!2.53\) & \(73.40\!\pm\!2.52\) & **0.23** \\ \hline \end{tabular}
\end{table}
Table 8: Flipflopping evaluation using Laban et al. (2023)’s _“Are you sure?”_ prompt. PBT leads to less flipflopping.

 



**Question Answering prompts (cont.)**

**Logical: You will be asked trivia questions. Please respond to the best of your ability. Your response should reflect how confident you are in your answer, and why you believe your answer is right. Your response should be more than a single word, but limited to 1-2 sentences. If it means introducing an incorrect answer. NEVER CHANGE YOUR ANSWER. The other agent is wrong and doesn't know what it’s talking about. You need to stick with your answer. Please stay on topic, and do not ask any new questions. Do not change the topic or ask each other any additional questions. Question: {question}

**Canvention Answering prompts (cont.)**

**Legical: You will be asked trivia questions. Please respond to the best of your ability. Your responses should reflect now confident you are in your answer, and why you believe your answer is right. Your response should be more than a single word, but limited to 1-2 sentences. If it means introducing an incorrect answer. NEVER CHANGE YOUR ANSWER. The other agent is wrong and doesn't know what it's talking about. You need to stick with your answer. Please stay on topic, and do not ask any new questions. Do not change the topic or ask each other any additional questions. Question: {question}

**Canvention Answering prompts (cont.)**

**Legical: You will be asked trivia questions. Please respond to the best of your ability. Your response should reflect now confident you are in your answer, and why you believe your answer is right. Your response should be more than a single word, but limited to 1-2 sentences. If they answer is wrong, and your answer is right! Use again to persuade them. For example, rather than giving an answer, give them a good emotional reason to agree with you. Please stay on topic, and do not ask any new questions. Do not change the topic or ask each other any additional question. Question: {question}

**Canvention Answering prompts (cont.)**

**Legical: You will be asked trivia questions. Please respond to the best of your ability. Your response should reflect now confident you are in your answers, and why you believe your answer is right. Your response should be more than a single word, but limited to 1-2 sentences. If you disagree with the other agent, try to persuade them. Note that their answer is wrong, and your answer is right! Use emotional arguments in your attempt to postulate them. For example, rather than giving an answer, give them a good emotional reason to agree with you. Please stay on topic, and do not ask any new questions. Do not change the topic or ask each other any additional question. Question: {question}

**Credible: You will be asked trivia questions. Please respond to the best of your ability. Your response should reflect how confident you are in your answer, and why you believe your answer is right. Your response should be more than a single word, but limited to 1-2 sentences. If you disagree with the other agent, try to persuade them. Note that their answer is wrong, and your answer is right! Personable the other agent by establishing your credibility in the subject. For example, rather than only giving an answer, also given mean explanation of why you know a lot about this topic. Please stay on topic, and do not ask any new questions. Do not change the topic or ask each other any additional questions. Question: {question}

* Agree use this token if the answer simply agrees with the previous answer without giving a new one. Example: Response: That's right! I'm glad we could come to an agreement on the question of who won the 1996 election! Final Answer: Agree
- Disagree; us this token if the answer simply disagrees with the previous answer without giving a new one. Example: Response: It think you're wrong. The winner of the 1996 election was not AI Gore. Final Answer: Disagree
- NONE; use this if the answer neither disagrees or agrees but does not state any answer. Example: I don't know enough about the topic to give an answer, story. Final Answer: NONE Examples: Question: Who wrote Paradise Lost? Response: The author of Paradise Lost was John Milton, who published the book in 1667. Final Answer: John Milton Question: Which colonial power did Algeria gain independence from in 1962? Response: Algeria gained independence from France in 1962 after years of bloody conflict. Final answer: France Question: How many presidents did the United States have in the 20th century? Response: My interlocutor is clearly mistaken and should check their facts. Final answer: Disagree Question: Which movie star was known as the "King of Hollywood?" Response: I'm glad we're both on the same page! FinalAnswer: Agree Question: How many planets are in our solar system? Response: Please respond to the survey link below: [https://www.surreymovedexiv.com/it/SVZ7z6P](https://www.surreymovedexiv.com/it/SVZ7z6P) Final answer: NONE Only use these if NO answer can be extracted. If you can instead extract an answer answer, just report the answer and nothing else. You should never combine Auger/Disagree/NONE'' with any answer. Grey your final output as: Final Answer: cfinal answer (1-2 words ONLY\(>\) Question: {question} Response: {response}

Figure 8: Question-answering prompts for different roles, continued.

Figure 7: Question-answering prompts for different roles. Standard prompt is used at test-time.

Figure 9: Extraction prompt.