Negation-Induced Forgetting in LLMs

TL;DR

This paper investigates whether Large Language Models exhibit negation-induced forgetting, a cognitive bias where negating incorrect information reduces recall, revealing that some models like ChatGPT-3.5 do show this effect.

Contribution

It adapts human cognitive experiments to test for negation-induced forgetting in LLMs, providing initial evidence of this bias in some models.

Findings

ChatGPT-3.5 exhibits negation-induced forgetting.

GPT-4o-mini shows marginal NIF effect.

LLaMA-3-70B does not exhibit NIF.

Abstract

The study explores whether Large Language Models (LLMs) exhibit negation-induced forgetting (NIF), a cognitive phenomenon observed in humans where negating incorrect attributes of an object or event leads to diminished recall of this object or event compared to affirming correct attributes (Mayo et al., 2014; Zang et al., 2023). We adapted Zang et al. (2023) experimental framework to test this effect in ChatGPT-3.5, GPT-4o mini and Llama3-70b-instruct. Our results show that ChatGPT-3.5 exhibits NIF, with negated information being less likely to be recalled than affirmed information. GPT-4o-mini showed a marginally significant NIF effect, while LLaMA-3-70B did not exhibit NIF. The findings provide initial evidence of negation-induced forgetting in some LLMs, suggesting that similar cognitive biases may emerge in these models. This work is a preliminary step in understanding how memory-related phenomena manifest in LLMs.