@inproceedings{nehring-etal-2024-large,
title = "Large Language Models Are Echo Chambers",
author = {Nehring, Jan and
Gabryszak, Aleksandra and
J{\"u}rgens, Pascal and
Burchardt, Aljoscha and
Schaffer, Stefan and
Spielkamp, Matthias and
Stark, Birgit},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.884/",
pages = "10117--10123",
abstract = "Modern large language models and chatbots based on them show impressive results in text generation and dialog tasks. At the same time, these models are subject to criticism in many aspects, e.g., they can generate hate speech and untrue and biased content. In this work, we show another problematic feature of such chatbots: they are echo chambers in the sense that they tend to agree with the opinions of their users. Social media, such as Facebook, was criticized for a similar problem and called an echo chamber. We experimentally test five LLM-based chatbots, which we feed with opinionated inputs. We annotate the chatbot answers whether they agree or disagree with the input. All chatbots tend to agree. However, the echo chamber effect is not equally strong. We discuss the differences between the chatbots and make the dataset publicly available."
}
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<abstract>Modern large language models and chatbots based on them show impressive results in text generation and dialog tasks. At the same time, these models are subject to criticism in many aspects, e.g., they can generate hate speech and untrue and biased content. In this work, we show another problematic feature of such chatbots: they are echo chambers in the sense that they tend to agree with the opinions of their users. Social media, such as Facebook, was criticized for a similar problem and called an echo chamber. We experimentally test five LLM-based chatbots, which we feed with opinionated inputs. We annotate the chatbot answers whether they agree or disagree with the input. All chatbots tend to agree. However, the echo chamber effect is not equally strong. We discuss the differences between the chatbots and make the dataset publicly available.</abstract>
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%0 Conference Proceedings
%T Large Language Models Are Echo Chambers
%A Nehring, Jan
%A Gabryszak, Aleksandra
%A Jürgens, Pascal
%A Burchardt, Aljoscha
%A Schaffer, Stefan
%A Spielkamp, Matthias
%A Stark, Birgit
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F nehring-etal-2024-large
%X Modern large language models and chatbots based on them show impressive results in text generation and dialog tasks. At the same time, these models are subject to criticism in many aspects, e.g., they can generate hate speech and untrue and biased content. In this work, we show another problematic feature of such chatbots: they are echo chambers in the sense that they tend to agree with the opinions of their users. Social media, such as Facebook, was criticized for a similar problem and called an echo chamber. We experimentally test five LLM-based chatbots, which we feed with opinionated inputs. We annotate the chatbot answers whether they agree or disagree with the input. All chatbots tend to agree. However, the echo chamber effect is not equally strong. We discuss the differences between the chatbots and make the dataset publicly available.
%U https://aclanthology.org/2024.lrec-main.884/
%P 10117-10123
Markdown (Informal)
[Large Language Models Are Echo Chambers](https://aclanthology.org/2024.lrec-main.884/) (Nehring et al., LREC-COLING 2024)
ACL
- Jan Nehring, Aleksandra Gabryszak, Pascal Jürgens, Aljoscha Burchardt, Stefan Schaffer, Matthias Spielkamp, and Birgit Stark. 2024. Large Language Models Are Echo Chambers. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 10117–10123, Torino, Italia. ELRA and ICCL.