Work

Teaching People LLM’s Errors and Getting it Right

Year: 2026

Type: conference-paper

Abstract: People often rely on large language models (LLMs) in situations where they are ill-suited.This miscalibration is understandable: seeing LLMs compose poetry and answer complex questions can lead users to assume, incorrectly, that they will also handle simple tasks, such as basic arithmetic, without error.Prior work has attempted to address this issue by clustering instance embeddings to identify regions where an LLM is likely to fail, then automatically describing the patterns within those regions.These inferred "failure patterns" are taught to users to reduce overreliance.Yet, this approach has not been fully successful.In this paper, we investigate why.

Source: Workshop on Trustworthy NLP (TrustNLP)

DOI: 10.18653/v1/2026.trustnlp-main.11

Authors: Nathan Stringham, Fateme Hashemi Chaleshtori, Xinyuan Yan, Zhichao Xu, Bei Wang, Ana Marasovic

Institution: University of Utah

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Awards: Collaborative Research: SCALE MoDL: Advancing Theoretical Minimax Deep Learning: Optimization, Resilience, and Interpretability, Collaborative Research: SCH: Geometry and Topology for Interpretable and Reliable Deep Learning in Medical Imaging