To Tell The Truth: Language of Deception and Language Models (2024)

Abstract: Text-based false information permeates online discourses, yet evidence of people’s ability to discern truth from such deceptive textual content is scarce. We analyze a novel TV game show data where conversations in a high-stake environment between individuals with conflicting objectives result in lies. We investigate the manifestation of potentially verifiable language cues of deception in the presence of objective truth, a distinguishing feature absent in previous text-based deception datasets. We show that there exists a class of detectors (algorithms) that have similar truth detection performance compared to human subjects, even when the former accesses only the language cues while the latter engages in conversations with complete access to all potential sources of cues (language and audio-visual). Our model, built on a large language model, employs a bottleneck framework to learn discernible cues to determine truth, an act of reasoning in which human subjects often perform poorly, even with incentives. Our model detects novel but accurate language cues in many cases where humans failed to detect deception, opening up the possibility of humans collaborating with algorithms and ameliorating their ability to detect the truth.

## introduction deception is pervasive in conversational dialogues. individuals motivated by self-interest often feel compelled to embellish the truth to promote their interests at the expense of others. misleading communication, such as false testimony @xcite , fake news @xcite , identity fraud in dating sites @xcite , sock puppetry @xcite , and propaganda campaigns @xcite , abundant daily, impacts political, social, and economic outcomes. this exchange of information leading to the decision of who and what to believe necessitates the tacit development of truth detection capability during conversations @xcite . in what follows, we explore if textual cues may increase the likelihood of fraud detection even in the presence of more overt visual or aural indicators. consider the ceo scam, when fraudsters act as company executives to trick a victim into sending unauthorized wire transfers or divulging private information through email. in addition, textual cues may be crucial for an impartial observer to identify duplicity in social media conversations when audio and visual cues are often manipulated with little to no chance of face-to-face conversations @xcite . this paper examines linguistic cues in a conversational exchange between contestants and judges participating in the tv game show, to tell the truth. in the game, the three contestants, under pretenses, mislead the four judges who attempt to infer the real central contestant (cc) via back-and-forth questioning. first, the game show offers a highstake situation where contestants have a financial incentive to lie and deceive, and the judges are under pressure to perform in front of a crowd to detect the deception. second, this data provides factual information to aid in assessing the contestant-a vital and distinguishing aspect of deception detection from the text than other datasets. the study of deception detection using computational methods has traditionally focused on detecting 'what' is the truth using multimodal cues @xcite . the definition of truth is often convoluted and depends on context; hence computationally detecting 'what is the truth' is challenging unless defined otherwise @xcite . works that focused on exploring language cues @xcite mainly restricted their analysis to psycholinguistic and hand-engineered linguistic features, which may not extend to scenarios where such cues are missing. even though psycholinguistic features indicate the interlocutor's intention on a syntactic or token level, they may not demonstrate a deeper affidavit says you're from tennessee, memphis. which main highway goes from knoxville up to gatlinburg? the name or the number? the name of the main highway that goes up to gatlinburg from knoxville. i haven't spent much time in eastern pennsylvania, uhh, tennessee. you spend time painting? what type of painting? yes, i do. painting. what do you work in oils or watercolors? number two, what is the outstanding hotel in panama? in panama, well, we don't. i have never flown to panama, so i don't know. it says that she travels to panama in the affidavit. semantic understanding of the text in the discourse context. recent progress in language models' ability to understand text prompted us to benchmark large language models' (llms) performance for the first time in detecting deception. armed with two main questions: 1. do enough language cues exist to discern truth from deceptive conversations without other multimodal cues; and 2. can a class of algorithmic detectors identify these cues, compose them in a valid chain of reasoning, and identify the truth?-in this paper, we demonstrate a bottleneck framework that progressively scans a deceptive conversation, analyzes each snippet by verifying utterances against objective truth, semantically understanding complex indicators of deception such as ambiguous responses, half-truths, and overconfidence, can satisfactorily reason its prediction for detecting deception. we release a new conversational dataset, to tell the truth from text (t4text) 1 , unique to the previous datasets, that contains a verifiable objective truth, forming the basis of lie detection. our model can detect deception in cases where all judges failed to detect lies correctly, indicating its ability to uncover new reasoning chains that might be insightful to humans to learn better predictors for deception. our model sometimes fails, where judges could correctly identify deception, leaving room for researchers to advance the frontier of the model performance in deception detection. ## related work deception detection. deception is an act emerging since the beginning of time with the serpent and eve in the garden of eden: and the serpent said unto the woman, ye shall not surely die. however, humans are often not very good at spotting 1 code & data: https://github.com/sanchaitahazra/ t4text these lies @xcite , and are no better than making random decisions @xcite . this raises the question of whether there are valid indicators of deception. according to studies @xcite @xcite @xcite , employing non-verbal signals such as visual, facial, and aural cues might significantly help distinguish sincere and opportunistic communication. recent studies mention participants are significantly more accurate at spotting lies from both audio and videos (82%) or only videos (66%), compared to text (57%) @xcite . in this paper, we investigate if a computational model can instead detect deception in text using language cues. deception + nlp datasets. automated deception detection techniques so far predominantly utilized visual cues such as facial or eye movements to detect deception and time to response @xcite , or linguistic cues from transcriptions from court hearings @xcite , deceptive hotel reviews @xcite , news articles from buzzfeed datasets @xcite , and factchecked tweets @xcite . datasets that require assigning specific individuals roles (lier/truth-teller) include a multimodal conversational dataset box of lies @xcite , golden globes @xcite differ from diplomacy gameplay @xcite , and real or spiel @xcite where one can choose to lie. in contrast, our derived dataset from this game show is conversational, grounded in a real deceptive environment with the presence of objective truth to detect the deception not present in existing datasets. the only work, @xcite that investigated, to tell the truth gameshow neither built computational models nor analyzed cues from interactions. ## to tell the truth to examine conversational dynamics in deception detection, conversations among individuals laced with the intent to deceive given the prior unembellished truth is the key to our analysis. to tell the truth, season 1 foot_0 , is a game of deliberate misrepresentation and was aired on american tv weekly from 1956 to 1959. every episode comprises multiple independent sessions, each lasting for about 8 minutes. a regular session comprises a host, four judges, and three contestants. one of the three contestants is the central contestant (cc), while the other two were imposters. ## approach we investigate the possibility of using an llm to uncover informative cues from the language of deception. here we define the task, our base models, and a bottleneck model capable of reasoning the language cues for successful deception detection. ## experiments our experiments are driven by the following hypotheses, and results are analyzed accordingly: can few/zero-shot models detect deception? yes. base models perform worse than bottleneck models irrespective of the choice of the llm and yield accuracy the same as random. however, the cot models further decrease accuracy across all llms, primarily attributing to the cot models' attempt to reason the potentially irrelevant or distracting information present in the conversation. why bottleneck models are better than base models? are few-shot models better than zero-shot models? depends. for smaller or earlier llms (gpt-3, gpt-3.5), few-shot examples helps to improve their performance from zero-shot setup. however, for bottleneck models (and even in base/cot models), gpt-4 achieves similar performance both in few-shot and zero-shot setups. adding more demonstrations is often difficult due to llm's limited context length. further summarization @xcite or selection of few-shot examples @xcite are possible, but we leave it as a future work. are supervised models better than few/zero-shot models? no. despite training on almost the full dataset (leave-one-out), the state-of-the-art supervised models do not outperform our zero-shot bottleneck models, indicating the superior ability of the llms to derive better bottleneck controls and act as a better discriminator. can models explain their chain-of-reasoning to detect deception? yes. do self-consistency improve reasoning performance? not significantly. @xcite showed adding self-consistency prompt improves few-shot models reasoning performance. we implemented self-consistency in our best bottleneck model as well as in the cot baseline. selfconsistency improved the performance of the cot baseline. however, it marginally improved the performance of our bottleneck models (not statistically significant). this indicates that despite using sophisticated task-specific prompts and refinement techniques, the dataset still remains a challenging case for state-of-the-art techniques involving llms. are there any potential variations in performance across different language models? the choice of our language model (gpt-4 ) may significantly impact the results. a more comprehensive analysis of various models could strengthen the study. we extend our experiments by adding results from two more language models: llama-2 (touvron et al., 2023) and bard foot_1 . llama-2 is an open-source language model. while bard (now gemini) has access to google search and youtube, the measures taken to prevent data leaks make bard a fair candidate for a competing baseline. in figure 4b , the model predicts cc correctly, and the success for the judges is divided into half. here, the model accurately recognizes a mistakenly erroneous response provided by the real cc as well as an "overall consistent behavior," denoting honesty. the judges who were not duped might have noticed cc's constant behavior or the inconsistencies among the imposters, while the duped judges might have relied on cc's inaccurate response to draw their conclusions. one of the judges who was not deceived in this case reappeared in the majority of the sessions, which indicates possible learning to support her claim @xcite . can models detect deception better than humans? depends. note that the human judges had access to all potential audio-visual and language cues in the original setting. audio-visual cues are very important, when present, in deception detection primarily due to the ease and abundance of detecting them @xcite . indeed, judges consistently use visual cues to explain their guesses, such as detecting the real swimmer/climber observing sun tan on the contestants or picking on the twinkle in the eyes for detecting a real joker. considering this, our best model's performance is almost comparable with human performance-in absolute terms, our model incorrectly predicted 3/150 more examples compared to human judges. this is slightly different than results obtained in @xcite , where they show linguistic features are the weakest predictors. however, it might be due to the fact that their linguistic features (part of them are liwc features) were not expressive enough, or our dataset contains more linguistic cues than theirs. this also signifies the uniqueness of t4text with the existence of the objective truth (affidavit), the basis of deception detection, and cross-questions revealing important factual and language cues to detect the real cc. do models detect deception for a complementary set than humans? yes. among the 59/150 correct predictions by our best model, we analyze how human judges performed in those game sessions. figure 3a shows the histogram of the discrete variable-the number of judges deceived per session, considering only 59 sessions said above. the plot is skewed towards a higher number of deceived judges per session, indicating that our model performed better on a set where humans detect deception poorly. this implies our model discovered novel reasoning pathways, purely based on language cues, for correct predictions, which human judges failed at, even with the presence of multimodal cues and incentives. we qualitatively examine the explanations generated by the model where all judges got deceived (10/59) and discovered that our model generated more informative and plausible reasoning chains than explanations by human judges mentioned during the original sessions (see figure 4 ). number one: this contestant gave generic and incomplete answers, which is a characteristic of the real person according to the hints. (..) they also struggled with the question about the size limit for a toy poodle, which could indicate unfamiliarity with the american measurement system, consistent with the affidavit stating that maria is brazilian. however, they failed to answer the question about candling an egg, which is a basic veterinary practice. despite their failure to answer one question, number one's overall performance was most consistent. number two: this contestant gave detailed and confident answers, which is a characteristic of an imposter. they knew the correct (…) . however, they incorrectly stated (…). number three: this contestant gave some correct answers, (…). however, they also gave some incorrect and vague answers. for example, they (…) suggested that the cure for a common chicken disease is to break the chicken's neck, which is incorrect and unprofessional. their answers are generally detailed and confident. (c) model: deceived | judges: all not deceived | correct answer: number three number one: he seems to be truthful and not overly detailed in his responses. he doesn't know what a double clutch is, which is a bit suspicious for a driving champion, but it's possible he never encountered it. his answer about the gas station address is generic but could be true. (…) number two: he seems to be very detailed in his responses, which could indicate he's an imposter. (…) he also provides a specific address for his gas station, which could be made-up.. number three: he gives a random answer about parking near a hydrant, which could indicate he's making up facts. (…) his answer about double clutching is detailed and could be true, but it's also a bit overconfident. he provides a specific address for his gas station, which could be a made-up detail. based on these observations, i would rank the contestants in the following order of likelihood of not being an imposter: 1. number one, 2. number three, 3. number two. can bottleneck features enhance human detection rate? yes. we performed a correlation analysis between the presence of a bottleneck feature and human prediction jointly using a multivariate regression model on cases where having these features significantly helped the computational model to predict correctly. on the intersection where humans were wrong but the bottleneck model was correct, we find either no correlation (@xmath0.05 for entailment and ambiguity) or negative correlation (@xmath1.05 for overconfidence and half-truths), indicating humans when poor at detecting such linguistic cues suffers significantly in detecting lies. are some sessions more difficult than others? to the model, yes; to humans, mostly no. banerjee et al. (2023) confirm no selection bias among the contestants across the sessions in the original game, indicating similar difficulty levels for the judges across sessions. they also highlight weakly significant selection bias among the judges, indicating better performance via learning and possible further selection as a judge in the session might have influenced their performance. qualitatively, we see models' errors clustered around sessions where judges use non-language cues successfully; however, model predictions among themselves were highly correlated (ρ = 0.7, @xmath2.05) for each underlying llm variant. figure 4c shows a case where the model fails to recognize the cc, but none of the judges were deceived. the model hallucinates in its reasoning to decide between number one and number three as cc. it provides reasons for both to have incomplete, somewhat more detailed, a few factually accurate responses, and however forth-making the prediction at random. however, the real cc exists in the top two choices from the models, indicating judges picked up cues beyond language. indeed, in this case, judges recognized the southern accent of the cc and verified via the affidavit, a critical aural cue that was not available to our model. ## conclusion and outlook in this paper, we first showed the existence of a class of algorithmic detectors based on llms that can successfully identify language cues of deception without the presence of other visual or audio cues. we contribute a novel dataset t4text for deception detection in the presence of objective truth and achieve a model performance comparable to human performance. we further find that our best model performs well in cases where humans perform poorly and discover novel language models that could augment human reasoning to detect deception, opening up the possibility of human-llm collaborations to combat misinformation. this paper advocates for human-ai collaboration, emphasizing the need for additional evidence on human dependence on algorithms in detecting textual deception. using the methodology of incentivized decision-making from behavioral economics, our ongoing work seeks to observe and categorically elucidate human reliance on our ai model in different conditions (e.g., black box, glass box). obtaining predictive features from our best model and those that drive human performance can provide us with a comprehensive understanding of why humans perform poorly in text-based deception detection tasks and whether ai tools can aid humans in such a crucial task that has lasting social, economic, and ecological impacts.