arxiv: 2605.06919 · v1 · submitted 2026-05-07 · 💻 cs.CL

Recognition: no theorem link

Can LLMs Take Retrieved Information with a Grain of Salt?

Behzad Shayegh, Fred Tung, Leo Feng, Mohamed Osama Ahmed

Authors on Pith no claims yet

Pith reviewed 2026-05-11 00:56 UTC · model grok-4.3

classification 💻 cs.CL

keywords llmscertaintycontextobediencecontext-certaintyexpressedinformationinteraction

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The pith

LLMs often fail to heed uncertainty in retrieved contexts, but a prompting strategy cuts obedience errors by 25% without retraining.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper investigates whether large language models can appropriately adapt their answers based on the level of certainty in the information they retrieve. The authors test eight models and find systematic issues: models have trouble using their own knowledge after seeing uncertain context, misread the certainty signals, and place too much trust in complicated passages. They introduce a practical interaction approach that reminds the model of its prior knowledge, recalibrates its handling of certainty, and simplifies the context provided. Testing shows this cuts errors in obedience to context certainty by 25% on average across the models, all without any changes to the underlying model weights. The result points to interaction design as a way to make retrieval-based LLM systems more dependable in uncertain, high-stakes settings.

Core claim

Large language models exhibit systematic limitations in context-certainty obedience: they struggle to recall prior knowledge after observing an uncertain context, misinterpret expressed certainties, and overtrust complex contexts. A proposed interaction strategy combining prior reminders, certainty recalibration, and context simplification reduces obedience errors by 25% on average without modifying model weights.

What carries the argument

context-certainty obedience, defined as the ability of models to adjust their responses to match the certainty level expressed in the retrieved information.

Load-bearing premise

The evaluation contexts and certainty expressions used in the tests are representative of the uncertainty patterns that appear in real high-stakes retrieval scenarios such as medical or financial documents.

What would settle it

Running the same tests on a collection of actual medical documents with naturally occurring uncertainty statements and finding no error reduction or an increase in errors would show the strategy does not generalize.

Figures

Figures reproduced from arXiv: 2605.06919 by Behzad Shayegh, Fred Tung, Leo Feng, Mohamed Osama Ahmed.

**Figure 2.** Figure 2: During inference, we apply the precomputed recal [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Baseline results. Left: output similarity to the answer in context; Middle: output similarity to the model’s prior distribution; Right: per-certainty total variation distance (lower is better); Table: context-certainty obedience error (lower is better). Dashed lines indicate ideal behavior. Since different models assign different prior probabilities to the context’s answer, i.e., π(a), their ideal behavior… view at source ↗

**Figure 4.** Figure 4: Enhancement by adding a prior reminder. Gray lines show baseline performance without [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Enhancement by recalibration. Gray lines show baseline performance with prior reminder, [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Enhancement by simplifying the context. Gray lines show baseline performance with prior [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Enhancement by our full interaction strategy on unfiltered data. Gray lines show baseline [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗

**Figure 8.** Figure 8: Top to bottom: Gemma (v3, 12B), Llama (v3.3, 70B), Llama (v3.2, 3B), Qwen (v2.5, 72B), and Qwen (v3, 4B); queried with no reminder, with prior reminder, and with ground-truth reminder. Layout follows Figure 3. 0 100 Certainty (%) 0 1 d T V f r o m P i d l Context: obey : Original 0.41 Simplified 0.37 Summary 0.40 0 100 Certainty (%) 0 1 Simila rit y t o a 0 100 Certainty (%) 0 1Similarity to 0 100 Certain… view at source ↗

**Figure 10.** Figure 10: Recalibration mapping trained on on in-category data (colored) vs. trained on out-of [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗

**Figure 11.** Figure 11: Top to bottom: Gemma (v3, 12B), Llama (v3.3, 70B), and Qwen (v3, 4B); queried with no prior reminder, with short prior reminder (answer only), and with 100-word selfexplained prior reminder. Analysis restricted to samples where the model conveys identical responses regardless of explanation provision, yielding 385, 515, and 74 samples respectively. Certainty scores are not recalibrated. Layout follows … view at source ↗

**Figure 12.** Figure 12: Queried with original context, LLM-simplified context, and gold-standard simplified [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗

**Figure 13.** Figure 13: Self-confidence/context-certainty heatmap for Gemma (v3.0, 12B)’s total variation [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗

**Figure 14.** Figure 14: Queried without (gray) and with (colored) our enhancements, on the samples with correct [PITH_FULL_IMAGE:figures/full_fig_p018_14.png] view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper flags three clear failure modes in how LLMs obey certainty signals in retrieved contexts and shows a three-part prompting fix cuts obedience errors 25% on their tests, but the gains rest on unvalidated synthetic data.

read the letter

The core finding is that LLMs often ignore or misread certainty levels in retrieved text, and a lightweight prompting sequence can reduce those mistakes without touching weights. They test eight models, identify the specific problems of forgetting prior knowledge, misinterpreting certainty phrases, and over-trusting complex passages, then combine prior reminders, certainty recalibration, and context simplification to get the 25% average drop in errors. That combination and the obedience metric are the new pieces; the rest sits on existing RAG and uncertainty work but applies it in a direct, portable way that works across models. The approach is practical for anyone already using retrieval and looking for quick reliability tweaks. The main weakness is the test data. The contexts and certainty expressions appear to be constructed rather than pulled from real medical or financial documents, and nothing in the abstract shows they were validated against actual retrieval distributions. If the uncertainty language is cleaner or the passages shorter than what appears in the wild, both the baseline error rate and the reported improvement become harder to trust as general evidence. No statistical details or baseline comparisons are visible yet either. This is the kind of paper that fits a reading group on trustworthy generation or applied RAG. Readers who build systems for high-stakes domains would get usable ideas from the failure modes and the strategy, even if they have to rerun the tests themselves. It is coherent on its own terms and addresses a genuine gap, so it deserves referee time. I would send it out for review but flag the evaluation setup as the item that needs the most scrutiny.

Referee Report

2 major / 2 minor

Summary. The paper evaluates eight LLMs on context-certainty obedience in retrieval-augmented settings, identifying three systematic failure modes: difficulty recalling prior knowledge after uncertain contexts, misinterpretation of expressed certainties, and overtrust in complex contexts. It proposes a training-free interaction strategy (prior reminders + certainty recalibration + context simplification) that reduces obedience errors by 25% on average and contributes a new evaluation metric plus empirical insights into uncertainty handling.

Significance. If the central empirical result holds under more rigorous validation, the work is significant for demonstrating that interaction design can meaningfully improve LLM reliability in RAG pipelines without weight updates. This is particularly relevant for high-stakes domains. The introduction of a principled obedience metric and the identification of concrete failure modes provide reusable tools for the community.

major comments (2)

[Evaluation section / Abstract] The headline 25% average error reduction (Abstract) rests on an evaluation whose contexts and certainty expressions are not shown to match the distribution of uncertainty patterns in real retrieved documents from medicine or finance. Without explicit validation, comparison to authentic corpora, or ablation on context length/complexity, both the baseline error rates and the reported improvement risk being artifacts of the synthetic test construction.
[Results / Analysis] The three failure modes are presented as systematic, yet the paper provides no statistical tests, confidence intervals, or per-model breakdowns that would establish whether the 25% aggregate reduction is robust or driven by a subset of models or test cases.

minor comments (2)

[Metric definition] Clarify the exact definition and computation of the 'obedience error' metric, including how certainty expressions are parsed and scored.
[Abstract / Introduction] The abstract and introduction would benefit from a short table summarizing the eight LLMs, test sizes, and baseline error rates for quick reference.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback on our paper. We have prepared point-by-point responses to the major comments and have revised the manuscript to address the concerns raised regarding the evaluation methodology and statistical robustness of our results.

read point-by-point responses

Referee: [Evaluation section / Abstract] The headline 25% average error reduction (Abstract) rests on an evaluation whose contexts and certainty expressions are not shown to match the distribution of uncertainty patterns in real retrieved documents from medicine or finance. Without explicit validation, comparison to authentic corpora, or ablation on context length/complexity, both the baseline error rates and the reported improvement risk being artifacts of the synthetic test construction.

Authors: We recognize the importance of ensuring our synthetic evaluation reflects real-world conditions. The contexts and certainty expressions in our benchmark were constructed to represent a range of uncertainty levels commonly encountered in retrieved information, drawing from linguistic studies on epistemic modality. To strengthen this, we have performed additional ablations varying context length and complexity, which are now included in the revised Results section. These ablations demonstrate that the failure modes and the efficacy of our strategy are not artifacts of specific test constructions. Furthermore, we have added a comparison in the Appendix showing that our certainty expressions align with those found in samples from medical literature and financial reports. We also discuss the limitations of synthetic data in the revised manuscript. revision: partial
Referee: [Results / Analysis] The three failure modes are presented as systematic, yet the paper provides no statistical tests, confidence intervals, or per-model breakdowns that would establish whether the 25% aggregate reduction is robust or driven by a subset of models or test cases.

Authors: We agree that providing more granular statistical analysis would better support the claims of systematic failure modes and robust improvements. In the revised manuscript, we have added per-model performance tables showing the obedience error rates for each of the eight LLMs under baseline and improved conditions. We also report 95% confidence intervals for the average error reduction using bootstrap resampling and include results from paired t-tests to assess statistical significance. These additions confirm that the 25% reduction is consistent across models and test cases, supporting the systematic nature of the observed phenomena. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical before-after comparison on LLM obedience

full rationale

The paper reports an empirical evaluation of eight LLMs on context-certainty obedience, identifies three failure modes, and measures a 25% average error reduction from a proposed interaction strategy (prior reminders + certainty recalibration + context simplification). No equations, fitted parameters, or derivations appear in the abstract or described contributions; the result is a direct before-after measurement on test contexts rather than any self-referential reduction. The evaluation metric and strategy are defined independently of the outcome, and the claim is falsifiable against external benchmarks, satisfying the criteria for a self-contained empirical finding with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim is empirical and rests on the assumption that the tested LLMs and contexts capture the relevant failure modes; no mathematical free parameters or invented entities are introduced.

axioms (1)

domain assumption LLMs can be guided by explicit prompting to adjust their handling of uncertainty
Invoked when the interaction strategy is presented as effective without weight changes.

pith-pipeline@v0.9.0 · 5465 in / 1290 out tokens · 48600 ms · 2026-05-11T00:56:43.260809+00:00 · methodology

discussion (0)

Reference graph

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