@inproceedings{alessa-etal-2025-quantifying,
title = "Quantifying Cognitive Bias Induction in {LLM}-Generated Content",
author = "Alessa, Abeer and
Somane, Param and
Lakshminarasimhan, Akshaya Thenkarai and
Skirzynski, Julian and
McAuley, Julian and
Echterhoff, Jessica Maria",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.155/",
pages = "2890--2910",
ISBN = "979-8-89176-298-5",
abstract = "Large language models (LLMs) are integrated into applications like shopping reviews, summarization, or medical diagnosis support, where their use affects human decisions. We investigate the extent to which LLMs expose users to biased content and demonstrate its effect on human decision-making. We assess five LLM families in summarization and news fact-checking tasks, evaluating the consistency of LLMs with their context and their tendency to hallucinate on a new self-updating dataset. Our findings show that LLMs expose users to content that changes the context{'}s sentiment in 26.42{\%} of cases (framing bias), hallucinate on 60.33{\%} of post-knowledge-cutoff questions, and highlight context from earlier parts of the prompt (primacy bias) in 10.12{\%} of cases, averaged across all tested models. We further find that humans are 32{\%} more likely to purchase the same product after reading a summary of the review generated by an LLM rather than the original review. To address these issues, we evaluate 18 mitigation methods across three LLM families and find the effectiveness of targeted interventions."
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<abstract>Large language models (LLMs) are integrated into applications like shopping reviews, summarization, or medical diagnosis support, where their use affects human decisions. We investigate the extent to which LLMs expose users to biased content and demonstrate its effect on human decision-making. We assess five LLM families in summarization and news fact-checking tasks, evaluating the consistency of LLMs with their context and their tendency to hallucinate on a new self-updating dataset. Our findings show that LLMs expose users to content that changes the context’s sentiment in 26.42% of cases (framing bias), hallucinate on 60.33% of post-knowledge-cutoff questions, and highlight context from earlier parts of the prompt (primacy bias) in 10.12% of cases, averaged across all tested models. We further find that humans are 32% more likely to purchase the same product after reading a summary of the review generated by an LLM rather than the original review. To address these issues, we evaluate 18 mitigation methods across three LLM families and find the effectiveness of targeted interventions.</abstract>
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%0 Conference Proceedings
%T Quantifying Cognitive Bias Induction in LLM-Generated Content
%A Alessa, Abeer
%A Somane, Param
%A Lakshminarasimhan, Akshaya Thenkarai
%A Skirzynski, Julian
%A McAuley, Julian
%A Echterhoff, Jessica Maria
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F alessa-etal-2025-quantifying
%X Large language models (LLMs) are integrated into applications like shopping reviews, summarization, or medical diagnosis support, where their use affects human decisions. We investigate the extent to which LLMs expose users to biased content and demonstrate its effect on human decision-making. We assess five LLM families in summarization and news fact-checking tasks, evaluating the consistency of LLMs with their context and their tendency to hallucinate on a new self-updating dataset. Our findings show that LLMs expose users to content that changes the context’s sentiment in 26.42% of cases (framing bias), hallucinate on 60.33% of post-knowledge-cutoff questions, and highlight context from earlier parts of the prompt (primacy bias) in 10.12% of cases, averaged across all tested models. We further find that humans are 32% more likely to purchase the same product after reading a summary of the review generated by an LLM rather than the original review. To address these issues, we evaluate 18 mitigation methods across three LLM families and find the effectiveness of targeted interventions.
%U https://aclanthology.org/2025.ijcnlp-long.155/
%P 2890-2910
Markdown (Informal)
[Quantifying Cognitive Bias Induction in LLM-Generated Content](https://aclanthology.org/2025.ijcnlp-long.155/) (Alessa et al., IJCNLP-AACL 2025)
ACL
- Abeer Alessa, Param Somane, Akshaya Thenkarai Lakshminarasimhan, Julian Skirzynski, Julian McAuley, and Jessica Maria Echterhoff. 2025. Quantifying Cognitive Bias Induction in LLM-Generated Content. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 2890–2910, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.