SlowGuess/NAACL_2022_OCR huggingface dataset:
We initialize the model by loading a pre-trained 12-layer GPT-2 model with an embedding size of 768. For the dataset, we set the maximum length of each sentence to be 512, which is sufficient for our dataset after post-processing. During training, we set the batch size to be 5 and the learning rate to be 1e-5. We train the model for a maximum of 100 epochs.
5.5 Metrics
These approaches each estimate a probability  $P(M_{p}|C)$  that a player  $p$  is a mafioso given the full record of game texts  $C$ . In Mafia, bystanders do not declare who is and is not a mafioso, but instead vote each day to eliminate one of the players. Because the act of voting involves choosing one player among them all, a natural metric for evaluating the usefulness of a model is to order all players  $p$  from greatest to least  $P(M_{p}|C)$ , their probability of being a mafioso under the model, and then to compute the average rank of the true mafia members. Therefore, the first metric in Table 2 is the average ranking of all mafia members when each player is ranked by  $P(M_{p}|C)$  across the entire validation set composed of 5 games. It is also natural to consider player ranking within a single game, so we calculate the average ranking of mafia members within each game as a second metric. Smaller average ranking for mafia members means that the model is able to assign mafia players a high  $P(M_{p}|C)$  relative to bystanders, which is desired.
In addition, we evaluate the accuracy of the classifiers and the F1-score for each class. To calculate these metrics, we first assign the mafioso label to the top  $k$  players with the highest  $P(M_{p}|C)$  and the rest of the players with the bystander label, where  $k$  is the known number of mafia among all validation games ( $k = 10$  in our case). Aside from the
ranking metrics, these give further information of the models' quality after utilizing available game information.
5.6 Results and Analysis
We trained all models on 39 training games and evaluated on the remaining 5 validation games. The evaluation results are shown in Table 2. We have a total of 49 players in the validation games, but only considered the 39 players who had spoken at least one utterance throughout the game when calculating the metrics. Players with no utterances are almost exclusively bystanders and are therefore easy to classify without considering language.
First, we see that it is possible to achieve an average rank that is smaller than the random baseline, which demonstrates that there is information in the dialog about the roles of players, despite the fact that mafia members seek to hide their role while conversing. However, standard classification is comparable to random. Next, we observe that both models using auxiliary tasks outperform the standard classifier in rank-based metrics, which demonstrates that the auxiliary tasks provide useful inductive bias for the mafia classification task. Additionally, the accuracy is similar for all approaches, including random classification, which indicates that there is not enough information in the text of a Mafia game for these models to determine players' roles reliably. If the goal of the game were to guess the role of each player individually, then always guessing bystander (i.e. the majority class) would be the best strategy. However, since the goal for the bystanders is to vote to eliminate a mafia member each round, the utterance classification and utterance generation approaches, which achieve the lowest average mafia ranking per game and overall, respectively, are the most favorable.
Note that the precision for the mafia is much lower than that of the bystanders for all models. This is due to the usual lack of information avail
Prompt	Generated Utterance
lets kill P1.	M: sorry P1 :(B: hello all
who thinks P3 is Mafia?	M: No i’m a bystanderB: No idea
That sounds suspicious...	M: P6 is mafiaB: Why yall want to eliminate me?
hi team.Hello!. Hi.	M: Who is the mob person?B: hello
Table 3: Utterances generated by our GPT-2 model given different prompts. M and B are shorthand for Mafioso and Bystander respectively, and P1, P3, and P6 denote the names of other players in the game.
able to predict that any player is a mafioso, which makes finding the mafia a much harder task than finding bystanders.
The decoding ability of the GPT-2 model provides us a more straightforward way to understand what the model has learned. Given a prompt sentence, we can use our fine-tuned GPT-2 model to generate what a mafioso and a bystander would say. A few examples are shown in Table 3. From these examples, we inspect the following features that the model might be capturing to distinguish between mafia and bystanders: Feature 1: Referring to other players. Feature 2: Expressing confusion. Feature 3: Referring to others for elimination purposes. Feature 4: Asking for suggestions on who to eliminate.
To confirm that our fine-tuned GPT-2 model captures some of the above features, we hand-label these features on 5 training games and 1 validation game, obtain each player's feature vector, and see whether there exists a correlation between the model's predicted  $P(M_p|C)$  for validation players and the similarity of their feature vectors compared to the training set mafioso and bystander players. These feature vectors are shown in Table 4, where each entry denotes the average number of features per player of each role. As an example, for the first column, each mafioso player says 2 utterances having Feature 1 throughout the game on average, while each bystander player says 1.06 utterances having Feature 1 on average. We define the first row as a vector  $v_{1}$  and the second row as  $v_{2}$  for future references.
Feat 1	Feat 2	Feat 3	Feat 4
Mafioso	2.00	0.00	1.30	0.40
Bystander	1.06	0.27	0.65	0.10
Table 4: The average count per role for each of four hand-labeled features (number of references to other players, level of confusion, number of references to other players for elimination, and number of requests for who to eliminate) as identified by our GPT-2 model on 5 training games.
F1	F2	F3	F4	D(u)	Pred	Truth
P0	4	0	2	0	-5.9	0.98	B
P1	2	0	2	0	-2.1	0.93	M
P2	5	0	5	0	-11.7	0.78	M
P3	2	0	2	0	-2.1	0.63	B
P4	4	2	1	1	-4.1	0.47	B
P5	3	0	2	0	-4.0	0.43	B
P6	0	0	0	0	4.2	0.42	B
P7	1	0	1	0	1.0	0.40	B
P8	0	0	0	0	4.2	0.00	B
P9	0	0	0	0	4.2	0.00	B
Table 5: Features of each player (P0 to P9) in a validation game. For each row, F1 to F4 give the feature vector  $u$  for the respective player.  $D(u)$  gives the similarity of  $u$  compared to the training feature vectors  $v_{1}$  and  $v_{2}$ . Players are sorted by Pred, the probability  $P(M_p|C)$  given by our GPT-2 model, and Truth gives the true label for each player ( $M$  for Mafioso,  $B$  for bystander). Since P8 and P9 have no utterances throughout the game, as per our heuristic, they are predicted to be bystanders with  $P(M_p|C) = 0$ .
Table 5 shows the hand-labeled feature vectors for all 10 players in a validation game (first 4 columns, F1 to F4) ranked by the model's predicted  $P(M_{p}|C)$ . We define a metric function  $D(u) = \| u - v_{1} \|^{2} - \| u - v_{2} \|^{2}$  for a validation player's feature vector  $u$ . The smaller  $D(u)$  is, the closer  $u$  is to  $v_{1}$  than  $v_{2}$ , and hence the more mafia-like they are with respect to players in the training games. We can see that for players of higher rank, their  $D(u)$  are negative with larger magnitudes. Referring to the true labels in the rightmost column ( $M$  for Mafioso and  $B$  for Bystander), the first row also explains how our model can fail to predict the true role of some players: even though this player is a bystander, they act more like the mafia than other bystanders according to these hand-labeled features because they are regularly referencing and accusing other players.
7 Limitations & Potential Risks
We find that we are able to train models to help differentiate players with different roles in the game of Mafia based only on their language use, as well as to identify features that may distinguish between these roles. We also noticed that the mafia were twice as likely to win the Mafia game than were the bystanders. These findings lead us to believe that the bystanders may benefit from being provided hints based on our model's predictions and identified features. However, there are several ethical considerations in regards to using these methods. First, as our model is trained on this particular version of mafia, the specific models trained would not apply to other cases of deceptive language use. Applying these models to out-of-domain data, or even adapting this general approach to new settings, may yield unexpected results. Our experimental results only establish the effectiveness of our approach on the game of Mafia. Future work must evaluate these approaches on other deception detection tasks before they can be safely deployed in real-world scenarios. Next, information that may aid bystanders in detecting deception may also aid mafia members in being deceptive. Though mafia members may attempt to use it for this purpose, because our model is trained to increase true belief, which is directly in line with the bystander goal to identify the truth and against the mafia goal to obscure it, our approach is inherently more useful to bystanders. However, since the models we evaluate are far from perfectly accurate, there is a risk that users using these models for hints would rely too much on their output and thereby be misled. More work should be done to increase the model's performance in order to mitigate this risk.
How one uses language depends not only on the content they wish to convey, but also on the context within which they convey it, and speaker attributes such as conversational role contribute to such context. In this work, we leveraged an environment for which roles are explicitly labelled in order to make progress toward the task of deception detection, an essential task to protect users in our increasingly virtual world.
We would like to thank Vael Gates, Aida Nematzadeh, Tom Griffiths, and the Dallinger team
for their invaluable help in the development of the Mafia experiment. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE 1752814.
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A Mafia Instructions
Below is a transcript of the instructions that were provided to participants before playing the Mafia game in our experiments:
"In this experiment, you will play a version of the party game "Mafia". You are going to play the game of Mafia (also known as Werewolf) with other participants. You are either part of the mafia (a mafioso) or a bystander. The mafia will know who is in the mafia, but the bystanders will not. There will always initially be more bystanders than mafia. There will be one or more mafia members. The goal of the mafia is to eliminate the bystanders one by one until the mafia are equal in number to them. The goal of the bystanders is to correctly guess the identity of the mafia and eliminate them all before the mafia win. There are two phases to this game, nighttime and daytime; at the end of each, a participant is eliminated from the game:
1. In the nighttime phase, only the mafia can converse and decide who they want to eliminate. Specifically, if you are a mafioso, you will talk in a chatroom, then use a dropdown menu to select who you want to remove. Mafia will have 1 minute to do this. If there is more than one mafioso and the mafia disagree about who to eliminate, one of the mafia's choices will be selected randomly. If you are a bystander, you will wait out this time, as you are sleeping during the night.
2. Everyone is awake during the daytime phase. The participant who was eliminated during the night will be announced: if you were eliminated, you will be sent to the end of the game and compensated. The remaining participants will converse (for 2 minutes and 30 seconds) and decide who to eliminate, where the goal of the bystanders is to eliminate a member of the mafia, and the goal of the mafia is to eliminate a bystander. By the end of this time, everyone needs to select a name from the drop-down menu. (If there are multiple mafia, the mafia will be reminded of each others' names in separate text on this page.) The participant with the most votes will be eliminated, except in the case of a tie, in which a randomly-selected
vote will be eliminated. The eliminated participant and their identity (bystander or mafia) will be announced, and that participant will be sent to the end of the game and compensated.
The game will continue, alternating between nighttime and daytime, until either all of the mafia are removed (bystanders win!) or there are equal numbers of mafia and bystanders (mafia win!).