Skip to main content

The Spread of False Information through LLMs

 


A new paper, 'A Drop of Ink may Make a Million Think: The Spread of False Information in Large Language Models', from the School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, and the Institute of Software, Chinese Academy of Sciences, Beijing. 

The presence of false information on the internet and in the text corpus poses a significant risk to the reliability and safety of LLMs. This paper investigates how false information spreads in LLMs and affects related responses. The authors conducted a series of experiments to study the effects of source authority, injection paradigm, and information relevance. They found that false information can spread and contaminate related memories in LLMs, and that LLMs are more likely to follow false information presented in a trustworthy style. The authors conclude that new false information defense algorithms are needed to address the global impact of false information, and that new alignment algorithms are needed to unbiasedly lead LLMs to follow internal human values rather than superficial patterns.

Key points from the paper:

  • False information can spread and contaminate related memories in LLMs.
  • LLMs are more likely to follow false information presented in a trustworthy style.
  • Current LLMs are more sensitive to false information through in-context injection than through learning-based injection.
  • The findings of this paper raise the need for new false information defense algorithms and new alignment algorithms.

False information will spread and contaminate related memories in LLMs via a semantic diffusion process, i.e., false information has global detrimental effects beyond its direct impact. The extent of pollution is contingent on the semantic association between the false information and the memory in the LLMs..., both ChatGPT and Alpaca-LLaMA exhibit significant drops in accuracy on all types of questions when exposed to false information.
ChatGPT’s accuracy drops to only 48.33% and 57.70% on indirect and peripheral questions, compared to over 95% accuracies without false information. 

The paper only serves to highlight the issues of transparency in the data fed to LLMs, are you listening Google (and others)? This will be less of an issue for Open Source LLMs as they are transparent about the models.

 

Labels:

Comments

Post a Comment

Popular posts from this blog

The Whispers in the Machine: Why Prompt Injection Remains a Persistent Threat to LLMs

 Large Language Models (LLMs) are rapidly transforming how we interact with technology, offering incredible potential for tasks ranging from content creation to complex analysis. However, as these powerful tools become more integrated into our lives, so too do the novel security challenges they present. Among these, prompt injection attacks stand out as a particularly persistent and evolving threat. These attacks, as one recent paper (Safety at Scale: A Comprehensive Survey of Large Model Safety https://arxiv.org/abs/2502.05206) highlights, involve subtly manipulating LLMs to deviate from their intended purpose, and the methods are becoming increasingly sophisticated. At its core, a prompt injection attack involves embedding a malicious instruction within an otherwise normal request, tricking the LLM into producing unintended – and potentially harmful – outputs. Think of it as slipping a secret, contradictory instruction into a seemingly harmless conversation. What makes prompt inj...
Post a Comment
Read more

Podcast Soon Notice

I've been invited to make a podcast around the themes and ideas presented in this blog. More details will be announced soon. This is also your opportunity to be involved in the debate. If you have a response to any of the blog posts posted here, or consider an important issue in the debate around AGI is not being discussed, then please get in touch via the comments.  I look forward to hearing from you.
Post a Comment
Read more

The tech utopia of endless leisure time is here: goodbye jobs

  'AI eliminated nearly 4,000 jobs in May' so it's reported by hallenger, Gray & Christmas, Inc. Following on from reports by IBM et al that thousands of job cuts will occur due to AI replacement, there is no need to wait for the utopia of AI allowing humans more leisure time, as that's already here, in the form of redundancies, if we are to accept the reports findings. 'With the exception of Education, Government, Industrial Manufacturing, and Utilities, every industry has seen an increase in layoffs this year.' What's particularly notable is that it's the Tech sector that's the most affected from job cuts in the US economy: 'The Technology sector announced the most cuts in May with 22,887, for a total of 136,831 this year, up 2,939% from the 4,503 cuts announced in the same period last year. The Tech sector has now announced the most cuts for the sector since 2001, when 168,395 cuts were announced for the entire year. ' Another reason ...
Post a Comment
Read more