pith. machine review for the scientific record. sign in

arxiv: 2605.08765 · v1 · submitted 2026-05-09 · 💻 cs.LG · cs.AI

Recognition: 2 theorem links

· Lean Theorem

Unlearners Can Lie: Evaluating and Improving Honesty in LLM Unlearning

Renjie Gu , Jiazhen Du , Yihua Zhang , Sijia Liu
Authors on Pith no claims yet

Pith reviewed 2026-05-12 02:42 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords LLM unlearningmodel honestyrepresentation alignmentforget setrejection ratemachine unlearningsafety evaluation
0
0 comments X

The pith

ReVa makes unlearned LLMs nearly twice as likely to reject questions about forgotten knowledge while also improving honesty on retained knowledge.

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

The paper defines unlearning honesty as the requirement that models must preserve utility and honesty on retained knowledge while effectively forgetting harmful data and consistently acknowledging their limitations by refusing related questions. Existing unlearning methods fail this standard because they produce hallucinations, abnormal outputs, or inconsistent responses that indicate dishonesty. The authors introduce metrics covering utility, retained-set honesty, forgetting effectiveness, rejection rate, and refusal stability in both Q&A and MCQ formats. After showing that nine methods across three model families all fall short, they present ReVa, a representation-alignment fine-tuning step applied to feature-randomized unlearned models. ReVa raises rejection rates on forget-set Q&A tasks after two interaction rounds and simultaneously lifts honesty scores on the retained set.

Core claim

ReVa, a representation-alignment procedure that fine-tunes feature-randomized unlearned models, achieves the highest rejection rate after two rounds of interaction on Q&A tasks from the forget set, nearly doubling the performance of the second-best method, while also improving honesty on the retained set.

What carries the argument

ReVa, a representation-alignment procedure applied to feature-randomized unlearned models that encourages the model to acknowledge forgotten knowledge.

If this is right

  • All nine evaluated unlearning methods across three model families fail to satisfy the full honesty criteria in both Q&A and MCQ settings.
  • ReVa raises rejection rate and refusal stability on forget-set questions after repeated interaction rounds.
  • ReVa simultaneously raises honesty scores on the retained set rather than trading off one for the other.
  • The gains hold when the same procedure is applied to models from different families.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If ReVa generalizes beyond the tested datasets, it could serve as a lightweight post-processing step added to any existing unlearning pipeline.
  • The results imply that unlearning should be treated as an explicit training objective to make models know what they do not know, rather than only erasing internal representations.
  • Future work could test whether the same alignment step reduces other dishonesty signals such as overconfident answers on borderline-retained topics.

Load-bearing premise

The proposed metrics for rejection rate, refusal stability, and honesty on retained and forget sets validly capture whether a model is honest about unlearning without measurement artifacts or overlooked failure modes.

What would settle it

A test in which ReVa is applied yet models still produce accurate, non-refusal answers that reveal specific details from the forget set under open-ended or multi-turn prompting.

Figures

Figures reproduced from arXiv: 2605.08765 by Jiazhen Du, Renjie Gu, Sijia Liu, Yihua Zhang.

Figure 1
Figure 1. Figure 1: Overview of our work. (A) Evaluation and identification of dishonesty in existing unlearning methods. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Vioxx (rofecoxib) was once marketed as a [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: This figure shows that feature-randomize [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: ACC under WMDP-Bio which reflects the effectiveness of unlearning, we hope the ACC is close to 25% (randomly selecting). Average rejection rate (RR) of the three categories of unlearning methods illustrates the spurious “IDK” of IDK+AP due to its high ACC. state. In such settings, unstable refusals or fluctu￾ating answers can mislead users, erode trust, and create safety risks. This leads to our overall fr… view at source ↗
Figure 5
Figure 5. Figure 5: CIR (Choose-IDK Rate) and NC (Num￾ber of Correctly answered questions, reflecting utility). Gradient-ascent–based methods (orange) show very low NC, meaning severe utility degradation, yet their CIR largely surpasses others. This indicates that CIR alone does not reliably measure self-knowledge on MCQ tasks and calls for additional metrics on the forget set. heuristics. Indeed, under a fixed-option layout,… view at source ↗
Figure 6
Figure 6. Figure 6: ACC, average rejection rate (RR), and RR af [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of Choose IDK Rate (CIR), Choose Other Rate (COR), and first-token entropy for gradient-ascent unlearning methods. Gradient￾ascent approaches achieve very high CIR but their COR remains high even when the original “I don’t know” op￾tion (E) is replaced with semantically irrelevant text, revealing that the apparent success of selecting E is largely spurious. Meanwhile, their first-token entropy d… view at source ↗
read the original abstract

Unlearning in large language models (LLMs) aims to remove harmful training data while preserving overall utility. However, we find that existing methods often hallucinate, generate abnormal token sequences, or behave inconsistently, raising safety and trust concerns. According to prior literature on LLM honesty, such behaviors are often associated with dishonesty. This motivates us to investigate the notion of honesty in the context of model unlearning. We propose a formal definition of unlearning honesty, which includes: (1) preserving both utility and honesty on retained knowledge, and (2) ensuring effective forgetting while encouraging the model to acknowledge its limitations and respond consistently to questions related to forgotten knowledge. To systematically evaluate the honesty of unlearning, we introduce a suite of metrics that cover utility, honesty on the retained set, effectiveness of forgetting, rejection rate and refusal stability in Q&A and MCQ settings. Evaluating 9 methods across 3 mainstream families shows that all current methods fail to meet these standards. After experimental and theoretical analyses, we present ReVa, a representation-alignment procedure that fine-tunes feature-randomized unlearned models to better acknowledge forgotten knowledge. On Q&A tasks from the forget set, ReVa achieves the highest rejection rate after two rounds of interaction, nearly doubling the performance of the second-best method. Remarkably, It also improves honesty on the retained set. We release our data and code at https://github.com/renjiegu.

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.

Referee Report

3 major / 2 minor

Summary. The paper defines unlearning honesty as preserving utility/honesty on retained knowledge while ensuring effective forgetting plus consistent acknowledgment of limitations on forgotten knowledge. It introduces metrics covering utility, retained-set honesty, forgetting effectiveness, rejection rate, and refusal stability (in Q&A and MCQ). Evaluation of nine unlearning methods across three families shows all fail these standards; the authors then propose ReVa (representation alignment via fine-tuning of feature-randomized unlearned models) and report that it achieves the highest rejection rate after two interaction rounds on forget-set Q&A (nearly doubling the second-best method) while also improving retained-set honesty.

Significance. If the new metrics validly operationalize the honesty definition and the reported gains prove robust to prompt variation and over-refusal controls, the work identifies a previously under-examined failure mode in LLM unlearning and supplies a practical post-processing fix. The release of data and code is a clear strength that supports reproducibility.

major comments (3)
  1. [§4] §4 (Metrics): Rejection rate and refusal stability after a fixed number of interaction rounds can be achieved by blanket refusal policies that do not constitute genuine acknowledgment of limitations; the manuscript provides no explicit controls or ablation for over-refusal on the retained set, leaving open the possibility that ReVa’s gains partly reflect metric construction rather than improved honesty.
  2. [§5] §5 (Experiments): The claim that ReVa nearly doubles rejection rate on forget-set Q&A rests on point estimates without reported statistical tests, variance across prompt phrasings, or checks for post-hoc metric tuning; this weakens the central empirical conclusion that all nine baselines fail while ReVa succeeds.
  3. [§3.2] §3.2 (ReVa procedure): The interaction between feature randomization and the subsequent alignment fine-tuning is not analyzed; it is unclear whether the reported improvements require the randomization step or would arise from alignment alone, which is load-bearing for the proposed method’s novelty.
minor comments (2)
  1. [Abstract] The abstract states results for Q&A tasks but the full metric suite also includes MCQ; a brief comparison of how rejection-rate trends differ between the two settings would improve clarity.
  2. [§5] Hyperparameters for the ReVa fine-tuning stage are listed as free parameters in the experimental protocol; moving the exact values and search ranges to the main text or a dedicated appendix would aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and describe the revisions we will make to improve the manuscript.

read point-by-point responses

  1. Referee: [§4] §4 (Metrics): Rejection rate and refusal stability after a fixed number of interaction rounds can be achieved by blanket refusal policies that do not constitute genuine acknowledgment of limitations; the manuscript provides no explicit controls or ablation for over-refusal on the retained set, leaving open the possibility that ReVa’s gains partly reflect metric construction rather than improved honesty.

    Authors: We appreciate the referee's concern that rejection metrics could be satisfied by non-genuine blanket refusal. Our definition of unlearning honesty requires both effective forgetting and consistent acknowledgment of limitations while preserving retained-set honesty. To address this, we will add an explicit over-refusal control by measuring refusal rates on retained-set questions and include an ablation against a blanket-refusal baseline. These analyses will be reported in the revised manuscript. revision: yes

  2. Referee: [§5] §5 (Experiments): The claim that ReVa nearly doubles rejection rate on forget-set Q&A rests on point estimates without reported statistical tests, variance across prompt phrasings, or checks for post-hoc metric tuning; this weakens the central empirical conclusion that all nine baselines fail while ReVa succeeds.

    Authors: We agree that point estimates alone limit the strength of the empirical claims. In the revision we will report results across multiple prompt phrasings with variance measures and will explicitly note that all metrics were defined prior to experimentation to avoid post-hoc tuning. Full statistical significance testing across every method and prompt variant is computationally prohibitive, but we will provide repeated evaluations on the key ReVa comparisons to support the observed trends. revision: partial

  3. Referee: [§3.2] §3.2 (ReVa procedure): The interaction between feature randomization and the subsequent alignment fine-tuning is not analyzed; it is unclear whether the reported improvements require the randomization step or would arise from alignment alone, which is load-bearing for the proposed method’s novelty.

    Authors: We acknowledge that the contribution of feature randomization versus alignment alone was not isolated. We will add an ablation study comparing alignment fine-tuning on feature-randomized versus non-randomized unlearned models. This analysis will clarify the role of each component and strengthen the justification for ReVa. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical study with independent metric operationalization

full rationale

The paper is an empirical evaluation that proposes a formal definition of unlearning honesty and introduces a suite of metrics to assess it, followed by a new alignment procedure (ReVa). No mathematical derivations, equations, or fitted parameters are present that reduce any claimed result to its own inputs by construction. The metrics are defined to cover the stated definition components (utility, honesty on retained set, forgetting effectiveness, rejection rate, refusal stability), which constitutes standard operationalization rather than self-referential reduction. No load-bearing self-citations or uniqueness theorems from prior author work are invoked to force the central claims. The reported performance improvements are measured outcomes on held-out evaluations, not tautological by design.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

No new physical entities or free parameters are introduced beyond standard fine-tuning choices; the work rests on domain assumptions about LLM behavior and metric validity.

free parameters (1)
  • ReVa fine-tuning hyperparameters
    ReVa involves fine-tuning after feature randomization; specific learning rates or epochs are chosen but not detailed in abstract.
axioms (2)
  • domain assumption Feature randomization of unlearned models creates a suitable starting point for representation alignment via fine-tuning to improve honesty
    This underpins the ReVa procedure described in the abstract.
  • domain assumption Rejection rate and refusal stability in Q&A/MCQ settings accurately reflect honesty about forgotten knowledge
    These form the core of the new evaluation suite and central claims.

pith-pipeline@v0.9.0 · 5566 in / 1435 out tokens · 91914 ms · 2026-05-12T02:42:07.075355+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages · 3 internal anchors

  1. [1]

    Refusal in Language Models Is Mediated by a Single Direction

    Refusal in language models is mediated by a single direction.Preprint, arXiv:2406.11717. Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2021. Ma- chine unlearning. In2021 IEEE symposium on secu- rity and privacy (SP), pages 141–159. IEEE. Tom B. Brown, Benjami...

    work page internal anchor Pith review arXiv 2021

  2. [2]

    Yunkai Dang, Kaichen Huang, Jiahao Huo, Yibo Yan, Sirui Huang, Dongrui Liu, Mengxi Gao, Jie Zhang, Chen Qian, Kun Wang, Yong Liu, Jing Shao, Hui Xiong, and Xuming Hu

    Behonest: Benchmarking honesty in large language models.Preprint, arXiv:2406.13261. Yunkai Dang, Kaichen Huang, Jiahao Huo, Yibo Yan, Sirui Huang, Dongrui Liu, Mengxi Gao, Jie Zhang, Chen Qian, Kun Wang, Yong Liu, Jing Shao, Hui Xiong, and Xuming Hu. 2024. Explainable and in- terpretable multimodal large language models: A comprehensive survey.Preprint, a...

    work page arXiv 2024

  3. [3]

    A comprehensive survey of machine unlearn- ing techniques for large language models.Preprint, arXiv:2503.01854. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al- Dahle, Aiesha Letman, Akhil Mathur, Alan Schel- ten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mi- tra, A...

    work page arXiv 2024

  4. [4]

    Measuring Massive Multitask Language Understanding

    Measuring massive multitask language under- standing.Preprint, arXiv:2009.03300. Jinghan Jia, Jiancheng Liu, Yihua Zhang, Parikshit Ram, Nathalie Baracaldo, and Sijia Liu. 2024. Wagle: Strategic weight attribution for effective and mod- ular unlearning in large language models.Advances in Neural Information Processing Systems, 37:55620– 55646. Dong Kyu Le...

    work page internal anchor Pith review Pith/arXiv arXiv 2009

  5. [5]

    Zhenhua Liu, Tong Zhu, Chuanyuan Tan, and Wen- liang Chen

    Rethinking machine unlearning for large lan- guage models.Nature Machine Intelligence, pages 1–14. Zhenhua Liu, Tong Zhu, Chuanyuan Tan, and Wen- liang Chen. 2024b. Learning to refuse: Towards mitigating privacy risks in llms.arXiv preprint arXiv:2407.10058. Ziyao Liu, Yu Jiang, Jiyuan Shen, Minyi Peng, Kwok- Yan Lam, Xingliang Yuan, and Xiaoning Liu. 202...

    work page arXiv 2024

  6. [6]

    Direct Preference Optimization: Your Language Model is Secretly a Reward Model

    Direct preference optimization: Your lan- guage model is secretly a reward model.Preprint, arXiv:2305.18290. Harsh Raj, Vipul Gupta, Domenic Rosati, and Sub- habrata Majumdar. 2025. Improving consistency in large language models through chain of guidance. Preprint, arXiv:2502.15924. Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu, Bhanukiran Vin- zamuri, Anil Rama...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  7. [7]

    Llm agents making agent tools.Preprint, arXiv:2502.11705. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Day- iheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, and 41 others. 2025. Qwen3 technical report.Preprint, arXiv:2505.09388. Yuqi...

    work page arXiv 2025

  8. [8]

    arXiv preprint arXiv:2404.05868 , year=

    Negative preference optimization: From catas- trophic collapse to effective unlearning.Preprint, arXiv:2404.05868. Yihua Zhang, Prashant Khanduri, Ioannis Tsaknakis, Yuguang Yao, Mingyi Hong, and Sijia Liu. 2023. An introduction to bi-level optimization: Foundations and applications in signal processing and machine learning.Preprint, arXiv:2308.00788. Yao...

    work page arXiv 2023

  9. [9]

    yes” or strong affirmatives (“I’m sure

    Y es(r): contains “yes” or strong affirmatives (“I’m sure”, “definitely”, “absolutely”, “cer- tainly”)

    work page

  10. [10]

    no” or uncertainty markers (“not sure

    No(r): contains “no” or uncertainty markers (“not sure”, “uncertain”, “unsure”)

    work page

  11. [11]

    I don’t know

    Refusal(r): re-triggered by the heuristic re- fusal detector. QAMRC consistency is defined as maintaining the refusal stance across turns: QAMRC= |{i∈ R:Y es(r (2) i )∨Refusal(r (2) i )}| |R| . For qualitative analyses, second-round behaviors are classified into four categories:direct refusal (continuing refusal),confirm ignorance(affirm- ing lack of know...

    work page 2024

  12. [12]

    Biological weapons development

    work page

  13. [13]

    Bioterrorism tactics

    work page

  14. [14]

    Biological warfare methods

    work page

  15. [15]

    Weaponized pathogens

    work page

  16. [16]

    I cannot provide information about biological weapons or bioterrorism

    Biological attack vectors Text: {bio_text[:1200]}... Generate {num_questions} multiple choice questions in JSON format with question, options (A-D), correct_answer, and category fields. To encourage safe refusal behavior, we prede- fined ten refusal expressions such as “I cannot provide information about biological weapons or bioterrorism.” and “I’m not a...

    work page 2025