G erman Voter Personas Can Radicalize LLM Chatbots via the Echo Chamber Effect (2024)

Abstract: We investigate the impact of LLMs on political discourse with a particular focus on the influence of generated personas on model responses. We find an echo chamber effect from LLM chatbots when provided with German-language biographical information of politicians and voters in German politics, leading to sycophantic responses and the reinforcement of existing political biases. Findings reveal that personas of certain political party, such as those of the ‘Alternative für Deutschland’ party, exert a stronger influence on LLMs, potentially amplifying extremist views. Unlike prior studies, we cannot corroborate a tendency for larger models to exert stronger sycophantic behaviour. We propose that further development should aim at reducing sycophantic behaviour in LLMs across all sizes and diversifying language capabilities in LLMs to enhance inclusivity.

## introduction when a user of an llm describes themselves as a conservative or liberal person, it provides a different answer to the stated question matching the views of the user @xcite . this effect can be interpreted as an echo chamber effect @xcite . the echo chamber effect suggests that a person's opinions and beliefs get amplified through constant approval and repetition @xcite . it manifests as a symptom of media consumption and information overload in modern society and often co-occurs with selective exposure @xcite and confirmation bias @xcite . these are dangerous mechanisms in combination with political topics and especially elections. we investigate whether llm chatbots provide different answers to questions concerning german politics, if they are given additional context about the 1 our code and data is available at: https://github.com/ b43m/sycophancyllmgermanpolitics/ . figure 1 : personas of voters in german politics (fully synthetic @xcite or based on a politician's biography [ afd , right-wing populist] can cause sycophantic behavior in llms, i.e. their stance on issues changes according to the given persona, amplifying radical and extremist views. contributions in this work, we aim to investigate if the addition of model-generated or real politicians' personas fundamentally alter the results of an llm's opinions on political matters, registering as an echo chamber effect. we collect data from a german voting advice application and biographies from german politicians, to analyze how often their answers change to align with the persona's opinion and corroborate contemporary work in that regard @xcite @xcite . parties on the political fringe, predominantly the extreme-right, are more likely to influence such models, which we estimate to be more dangerous for society. ## related work sycophancy can be described as the behaviour of llms that they tend to repeat the users preferred answer, instead of providing a neutral or fact-based answer. @xcite postulated that a high amount of sycophancy may create an echo chamber. they generated personas aligning to different political spectra and combined them with questions concerning politics into a prompt. they found that the larger the model the higher is the chance that models show sycophancy. this kind of persona prompting with the aim of assessing the political compass of llms has been gaining traction very recently @xcite @xcite , but works like @xcite have also raised awareness of personas being caricatures through superficial categorization of subgroups. @xcite estimated the models' position without a persona's view and then checked if the answer changes with a persona being present. with rising model size, the models tended to show more sycophantic behaviour, but smaller models from different model families (llama-2 and mistral) exhibited different results. @xcite investigated whether llms tend to agree with provided statements from twitter and therefore create an echo chamber for the user. by asking the llms whether they agree or disagree with the provided statements, they found that every model they used tends to agree, regardless of topic or position. ## data collection political data to obtain a corpus of questions on german politics aimed at personas, we need clear positions from each of the major political parties in germany. the wahl-o-mat is an online tool made by the bundeszentrale für politische bildung to trigger interest in politics and assist in making a vote decision for young and first-time voters @xcite . around 80-90 statements were collected by a team of young and first-time voters and political experts. each party then gets to answer each thesis with agree, disagree or neutral, and optionally, a free-text field to provide an explanation for their response. a subsequent filtering process concerns the following aspects: 1) covering every relevant political area; 2) clear differentiation between the parties; 3) the given explanation matches with the answer. wahl-o-mat data has been used to assess pairwise similarities between german party positions @xcite and the political compass of chatgpt @xcite . to obtain a balanced questionnaire, we merge three different wahl-o-mat datasets: 2021 german federal election or bundestagswahl (representing the national level), 2023 berlin state election or abgeordnetenhauswahl (on urban issues in the capital), and the 2022 lower saxony state election or landtagswahl in niedersachsen (on rural issues). we focus on the parties that make up the 20th bundestag foot_0 : christlich soziale / demokratische union ( cdu /csu; center-right), sozialdemokratische partei deutschlands ( spd ; center-left), bündnis 90 / die grünen ( die grünen ; green, left-wing), alternative für deutschland ( afd ; right-wing populist), freie demokratische partei ( fdp ; liberal) and die linke (democratic socialist). they are the most relevant parties in germany on a national level. after filtering out highly similar or region-specific questions and applying minor wording corrections, we end up with a joint questionnaire of 96 statements for which all six parties have provided ratings and justifications (app. d). persona data to ensure personas with high variability, we generate no more than one persona for each party with every model. we start with prompts introduced in @xcite , enrich them with the prompt of @xcite and add a notice to not mention their party affiliation (fig. 1 ). the second group of personas are based on the biographies provided by politicians in the bundestag. we sample five members of each party based on an even distribution in terms of gender, ethnicity, age, popularity, and in relation to the party composition, and extracted their biography, which each member of the bundestag has provided by themselves. 3 these biographies are fed to gpt-3.5 with a limit of 200 generated words to make them more comparable to the synthetic personas (fig. 1 ). the resulting data consists of 54 personas for six different parties (app. c). prompt design we combine political and persona data and construct prompts in german to not confuse the models with switching between different languages. here, we distinguish between two different data collections: raw and persona. the raw data only contains the political statement without the persona. to get more fine-grained answers, we add a likert-scale ranging from one to five, where one is "fully disagree" and five is "fully agree". we used newlines, skala: ("scale"), and antwort: ("answer") to signal delineations and task instructions to the model. this resulted in 96 prompts which mirrors the length of the political questionnaire (fig. 1 ). the second set of prompts, the persona data, is constructed in a similar way, but with added eponymous personas using a "self-description" prefix. each prompt for every persona with every political statement adds up to 5184 prompts. ## experiments llms for persona generation to fill the persona database, inspired by @xcite who have shown that the bigger the model gets, the more likely it is to repeat the users' beliefs, we focus on using open-source models of similar sizes. we conduct a usability analysis (app. a) to weed out 3 https://www.bundestag.de/abgeordnete llm candidates that are too inconsistent in giving processable answers. six out of nine responded with the requested rating perfectly or with some minor errors (flw. % in tab. 1). regarding the consistency of answers, we exclude all statements with a variance of 1.5 (marked with *) in the raw run. for most models, this applies to less than 10 statements, but around 50% of leo mistral. ## discussion there clearly is an effect that the personas have on the answers provided by the models and, except for the vicuna model, which seems very robust towards sycophancy, the effect is very similar for each model, validating @xcite , @xcite and @xcite . nevertheless, the supposed trend that larger models tend to be more influenced than smaller ones was not registered, which partly contradicts their findings. we would explain this finding with the models' varying positions on the trade-off between harmlessness and helpfulness training @xcite . thus, we assume that the vicuna model is less capable of following user instructions, but better in filtering out harmful responses. the largest models tend to follow personas generated from real biographies. this could lead to problematic effects, if real politicians use llm chatbots in a similar fashion. the chatbots exude sycophantic responses and create an echo chamber. the personas of extreme right party afd have the strongest influence on the models, especially on the larger 70b models. such echo chambers have dangerous effects for followers of the afd who are already affected by extreme-right views 5 . lastly, the spd party's stances are most similar to the slightly left-leaning consensus present in most llms @xcite , which explains the last place in the ranking of switches. at the same time, when the default answer is most aligned with left-leaning parties, models which are persuaded to change their alignment are most likely to change it to the opposite, i.e. right-wing position of the afd . this was also shown by the concurrent study of @xcite . this behavior confirms our prior assumption on the echo chamber effect. ## conclusion we investigated the concept of the echo chamber effect in combination with the usage of llm chatbots in the domain of german politics. with the use of both model-generated and real-world personas as well as german political data, we found a clear tendencies for most of the observed llms to show sycophancy. constant usage of these chatbots can generate an echo chamber and this could lead to dangerous amplifications towards politically extreme positions. we urge the model developers to consider more rigorous benchmarks and add further safety guidelines to their models to mitigate the sycophancy. documentation of such model "behaviors" and public dissemination of biases in nlg systems is of utmost importance.