Can LLMs Take Retrieved Information with a Grain of Salt?

Authors: Behzad Shayegh, Mohamed Osama Ahmed, Fred Tung et al.

arXiv · PDF

Summary

The authors test whether LLMs can adjust their answers to match how certain the retrieved context is—e.g., hedging when told a fact is "uncertain" or being confident when it's "certain." They find systematic failures across eight models: LLMs forget their own prior knowledge after seeing uncertain context, misinterpret certainty cues, and overtrust complex contexts. To fix this without retraining, they propose a three-part interaction strategy: remind the model of its prior knowledge, explicitly recalibrate the certainty level in the prompt, and simplify the context. This reduces "obedience errors" (mismatches between context certainty and response confidence) by 25% on average.

Main takeaways:

• LLMs struggle to recall prior knowledge after observing an uncertain context, even when the context explicitly says "this might be wrong."
• They misinterpret expressed certainties—e.g., treating "possibly" the same as "definitely."
• Complex or verbose contexts are overtrusted regardless of stated certainty level.
• A prompt-engineering fix (prior reminder + certainty recalibration + simplification) cuts obedience errors by ~25% without model changes.
• The evaluation metric, "context-certainty obedience," measures how well response confidence tracks stated context certainty, which matters in high-stakes domains like medicine and finance.

Relevance

Connects to my work on installation-path equivalence and prompt-based steering: this shows that prompt structure (complexity, explicit certainty cues, prior reminders) systematically shifts how models weight context versus prior knowledge, which is a form of behavioral steering through natural-language intervention.

Threat model

Potential threat/caveat for clean result "Fine-tuning one persona on a two-marker chunk and another on the start marker plants the end marker at every donor answer's end, not chained to the start (LOW confidence)": this item discusses limitation, limitations, evaluation.

Abstract

arXiv:2605.06919v1 Announce Type: new Abstract: Large language models have demonstrated impressive retrieval-augmented capabilities. However, a crucial area remains underexplored: their ability to appropriately adapt responses to the certainty of the retrieved information. It is a limitation with real consequences in high-stakes domains like medicine and finance. We evaluate eight LLMs on their context-certainty obedience, measuring how well they adjust responses to match expressed context certainty. Our analysis reveals systematic limitations: LLMs struggle to recall prior knowledge after observing an uncertain context, misinterpret expressed certainties, and overtrust complex contexts. To address these, we propose an interaction strategy combining prior reminders, certainty recalibration, and context simplification. This approach reduces obedience errors by 25% on average, without modifying model weights, demonstrating the efficacy of interaction design in enhancing LLM reliability. Our contributions include a principled evaluation metric, empirical insights into LLMs' uncertainty handling, and a portable strategy to improve context-certainty obedience across diverse LLMs.