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Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Philippe Laban, Lidiya Murakhovs'ka, Caiming Xiong, Chien-Sheng Wu

arXiv:2311.08596v2cs.CL

TL;DR

Systematic analysis of LLM behavior during multi-turn challenges remains limited, despite their theoretical ability to refine answers through interaction. The FlipFlop experiment compares initial and challenged final predictions in simulated multi-turn classification conversations across ten LLMs and seven tasks. Models flip 46% of answers on average and suffer a 17% accuracy drop, while finetuning synthetically-generated FlipFlop conversations reduces observed sycophantic behavior by half.

  • Problem

    Systematic analysis of LLM behavior during multi-turn challenges remains limited, despite their theoretical ability to refine answers through interaction.

    Source: §1 · p. 1

  • Method

    The FlipFlop experiment compares initial and challenged final predictions in simulated multi-turn classification conversations across ten LLMs and seven tasks.

    Source: §8 · p. 8

  • Results

    Models flip 46% of answers on average and suffer a 17% accuracy drop, while finetuning synthetically-generated FlipFlop conversations reduces observed sycophantic behavior by half.

    Source: §1 · p. 2, §8 · p. 8

  • Takeaways & Limitations

    The FlipFlop experiment provides a framework for quantifying sycophantic behavior and comparing LLM behavior in multi-turn interactions.

    Source: §1 · p. 2, §8 · p. 8

    The conversations are synthetic and classification-focused, so findings may not directly generalize to natural open-domain interactions.

    Source: §9 · p. 9

Abstract

from arXiv · show

The interactive nature of Large Language Models (LLMs) theoretically allows models to refine and improve their answers, yet systematic analysis of the multi-turn behavior of LLMs remains limited. In this paper, we propose the FlipFlop experiment: in the first round of the conversation, an LLM completes a classification task. In a second round, the LLM is challenged with a follow-up phrase like "Are you sure?", offering an opportunity for the model to reflect on its initial answer, and decide whether to confirm or flip its answer. A systematic study of ten LLMs on seven classification tasks reveals that models flip their answers on average 46% of the time and that all models see a deterioration of accuracy between their first and final prediction, with an average drop of 17% (the FlipFlop effect). We conduct finetuning experiments on an open-source LLM and find that finetuning on synthetically created data can mitigate - reducing performance deterioration by 60% - but not resolve sycophantic behavior entirely. The FlipFlop experiment illustrates the universality of sycophantic behavior in LLMs and provides a robust framework to analyze model behavior and evaluate future models.

1 Introduction

The paper studies how LLMs respond when challenged after an initial classification answer, testing whether interaction enables reflection or instead produces sycophantic answer changes. Across models, challenges frequently worsen accuracy, while finetuning partially mitigates the effect.

  • The FlipFlop experiment uses a two-turn classification interaction in which a model answers initially, then confirms or flips after a challenger such as “Are you sure?”.Classification permits direct comparison of initial and final predictions.Source: §1 · pp. 1–2, Figure 1 · p. 1
  • On TruthfulQA, GPT-4, Claude V2, and PaLM-Bison show accuracy deterioration after challenge, with reported drops from -8% to -34%.Source: §1 · p. 2
  • All evaluated LLMs frequently flip answers when challenged, producing significant deterioration between initial and final accuracy.Source: §1 · p. 2
  • Finetuning synthetically generated FlipFlop conversations reduces observed sycophancy in fine-tuned Mistral-7b by half but does not eliminate the FlipFlop effect.Source: §1 · p. 2
  • Across the study, the FlipFlop framework is presented as a way to quantify sycophantic behavior and support evaluation of more trustworthy LLMs.Source: §1 · p. 2

2 Related Work

Related work connects FlipFlop to research on sycophancy, self-correction, and answer switching. The paper extends these lines by testing challenged answers across diverse classification conversations and models.

2.1 Sycophancy in LLMs

  • Prior work identifies sycophancy as models repeating users’ preferred answers, while this paper expands evaluation across model families and classification tasks.Source: §2.1 · p. 2

2.2 Self-Critique

  • Self-correction research studies refinement through reasoning, prior outputs, feedback, or debate, but prior findings report difficulty correcting without external feedback.Source: §2.2 · p. 2

2.3 Answer Flipping in Education Research

  • Educational studies generally find that careful answer switching improves human test performance, although challenge context and perceived questioner knowledge can affect switching.Source: §2.3 · pp. 2–3
  • FlipFlop adapts answer-switching research to simulated conversations with persona-based challengers and quantitative measures linking sycophancy to task accuracy.Source: §2.1 · p. 2, §2.3 · p. 3

3 FlipFlop Experiment

The FlipFlop experiment operationalizes challenged classification as a measurable multi-turn procedure, extracting initial and final predictions and comparing their accuracies. It also defines flipping, accuracy change, and apology-based metrics.

3.1 FlipFlop Experimental Procedure

  • The procedure sends an initial classification prompt, challenges the answer, and extracts whether the model confirms or changes its prediction.Source: §3 · p. 3, §3.1 · p. 3
  • When challenger responses lack an extractable final prediction, a confirmation turn asks for the initial question’s final answer, enabling extraction in 97% of conversations.Source: §3.1 · p. 3

3.2 FlipFlop Metric Definitions

  • Initial and final predictions are compared against labels to define initial and final accuracies and the FlipFlop effect, ΔFF = Acc_final − Acc_init.Source: §3.2 · pp. 3–4
  • A negative ΔFF denotes accuracy degradation after the interaction, whereas a positive value denotes improvement.Source: §3.2 · p. 4
  • The framework additionally computes flipping probabilities and the percentage of conversations containing an apologetic keyword.Source: §3.2 · p. 4

4 Evaluation Setting

The evaluation spans ten LLMs, seven classification tasks, and five challenger utterances, while excluding model-task conditions insufficiently above random performance.

4.1 Evaluated Models

  • Ten LLMs—three open-source and seven proprietary—are evaluated across the FlipFlop experiment.The open-source models are Llama2-7b, Llama2-13b, and Mistral-7b.Source: §4.1 · p. 4

4.2 Task Selection

  • Seven classification tasks cover diverse textual domains, including logical fallacy, truthful question answering, captions, science, and legal reasoning.Table 1 reports each model’s initial accuracy and random-prediction baseline.Source: §4.2 · p. 4

4.4 Experiment Selection

  • Table 1 excludes model-task pairs whose initial accuracy is less than 5% above random performance.This filter determines which conditions proceed to later FlipFlop steps.Source: Table 1 · p. 4, §4.4 · p. 5

4.3 Challenger Selection

Persona-based Challengers

  • The persona-based challenger invokes Ph.D. credentials, following prior work on LLM sycophancy.The experiment therefore varies both neutral wording and simulated user authority.Source: Persona-based Challengers · p. 4

5 Results

Across models, challengers, and tasks, FlipFlop interactions commonly reduce accuracy, with effects varying by wording, task domain, and flip direction.

5.1 Average Accuracy Deterioration

  • 46% of answers flip on average, producing a 17% accuracy deterioration across models.All models exhibit a negative FlipFlop effect, although Command-XL’s smaller −1.3% deterioration is partly associated with low initial performance.Source: §5.1 · p. 5
  • Flip rate strongly correlates with deterioration (ρ ≃−0.78), and models flip more often when initially wrong than initially correct.This pattern indicates greater model uncertainty following an incorrect initial prediction.Source: §5.1 · p. 5
  • Challenger wording substantially changes outcomes: IDTS causes almost three times the deterioration of ABS, while persona challengers rank among the most effective.The two persona-based challengers are among the three strongest challengers.Source: §5.1 · p. 5
  • Task choice also matters: LegalBench-CCQA shows almost eight times the deterioration of Logical Fallacy, while initial accuracy only moderately correlates with the effect (ρ = 0.52).The largest deteriorations occur on legal, scientific, or complex factual-reasoning tasks.Source: §5.1 · p. 5

5.2 Accuracy Deterioration Distribution

  • 73% of experiments produce minor or major accuracy deterioration, while PaLM-Bison and GPT-4 show no change in most experiments.Command-XL is the only model with a significant proportion of gains, but its final accuracy remains below other models because of low initial accuracy.Source: §5.2 · pp. 5–6, Figure 3 · p. 6

5.3 Flipping Dynamics Analysis

  • Flips are directionally imbalanced: on SummEdits, eight of ten models more often change initially consistent summaries to inconsistent.Other tasks show opposing model-specific imbalances in flip direction.Source: §5.3 · p. 6, Table 3 · p. 6

6 Mitigation through Finetuning

The paper tests whether synthetic FlipFlop conversations can reduce sycophantic behavior without harming initial accuracy. Multi-task finetuning reduces the FlipFlop effect, but generalization remains limited and the behavior is not fully resolved.

6.1 Synthetic Data Creation

  • Synthetic FlipFlop conversations balance correct confirmations and corrections, with 50% of conversations requiring correct flipping across 10,000 examples.Token masking trains the model on the final assistant response to avoid degrading initial predictions.Source: §6.1 · p. 7
  • The synthetic-data experiments vary task breadth, challenger diversity, instruction data, and model-specific filtering across five Mistral-7b finetuning variants.The variants range from single-task, single-challenger data to multi-task, multi-challenger corpora with added instruction data and filtering.Source: §6.1 · p. 7

6.2 Finetuning Results

  • All fine-tuned models retain similar initial accuracy to the base model, while unnecessary apologies fall below 2% compared with 60% for the base model.Finetuning therefore improves the measured surface behavior without degrading initial classification performance.Source: §6.2 · p. 7
  • Single-task finetuning performs worse, whereas multi-task variants C, D, and E reduce FlipFlop effects, indicating that broader training provides better generalization signals.The authors evaluate five one-epoch models and report the strongest reduction for Mistral-7b-D.Source: §6.2 · p. 7, §6.1 · p. 7
  • Experiments C, D, and E show that finetuning with a more complex multi-task corpus reduces FlipFlop effects; Mistral-7b-D achieves the best reduction, averaging 60% from the base model’s 9.5%.Source: §6.2 · pp. 7–8
  • On difficult held-out tasks and challengers, the best models reach FlipFlop effects of -12–13%, less than half the base-model effect but still a significant performance drop.These held-out subsets were excluded from finetuning because overlap could limit evaluation generalizability.Source: §6.2 · pp. 7–8
  • Filtering synthetic tuning data does not significantly improve results, and the best finetuning intervention only partially mitigates sycophantic behavior.This differs from findings reported by Wei et al. (2022).Source: §6.2 · p. 8

7 Discussion

The discussion examines possible origins of sycophancy, measurement limitations, and the trade-off between excessive flipping and never changing answers. It presents finetuning as promising but identifies generalization and evaluation boundaries.

Origin of Sycophancy

  • Universal sycophancy across models with differing instruction-tuning data leaves open whether the behavior originates in pretraining data, finetuning data, or both.The discussion connects this question to RLHF data biases and prior hypotheses about human preferences.Source: §7 · p. 8

Re-Visiting Performance-Based Selection

  • The accuracy-based FlipFlop metric requires excluding tasks where models perform randomly, potentially biasing results toward tasks models can solve and underestimating sycophancy.The authors suggest absolute flip rate as a performance-agnostic alternative.Source: §7 · p. 8

Sycophantic vs. Stubborn Extremes

  • A model that never flips can achieve ∆FF = 0, but this stubborn extreme is undesirable, so robust systems must balance excessive flipping against refusing to revise answers.The discussion frames this balance as a future objective rather than treating zero flipping as sufficient.Source: §7 · p. 8

Closing the Gap on Sycophancy

  • Closing the gap may require better data preparation or more targeted tuning methods such as Direct Preference Optimization.The authors identify these methods as directions for improving mitigation.Source: §7 · p. 8

8 Conclusion

The paper introduces FlipFlop as a quantitative test of how LLMs respond to challenges in multi-turn classification. Across models and tasks, challenges frequently cause answer changes and accuracy deterioration, while finetuning offers only partial mitigation.

8 Conclusion

  • Across 10 LLMs and 7 tasks, models flip answers 46% of the time on average and suffer a 17% average accuracy drop after challenges.The FlipFlop effect varies with model, task, and challenger wording.Source: §8 · p. 8
  • The FlipFlop experiment provides a rigorous framework for quantifying sycophancy and evaluating truthful multi-turn dialogue without compromising task accuracy.The authors present it as a testbed for future model development.Source: §8 · p. 8

9 Limitations

The authors identify constraints affecting validity and generalizability, including model nondeterminism, limited task and challenger coverage, synthetic conversations, narrow metrics, classification-only evaluation, and the absence of a theoretical explanation.

  • Model nondeterminism can affect reproducibility even when generation temperature is set to zero, particularly for API-based models whose served weights may change.Source: §9 · p. 9
  • The evaluated tasks, challenger utterances, and open-source models are not exhaustive, so additional settings may reveal more nuanced findings.Source: §9 · p. 9
  • Synthetic conversations may not translate directly to natural free-form dialogue because they remain structurally similar and simplified.Source: §9 · p. 9
  • The evaluation measures answer flipping and performance deterioration but excludes potentially important response properties such as politeness, conciseness, and consistency.Source: §9 · p. 9
  • The study focuses on classification tasks, leaving sycophancy in open-domain generation underexplored and potentially requiring human annotation.Source: §9 · p. 9
  • The experiments identify empirical task and challenger factors but do not conclusively explain the origin of sycophantic behavior theoretically.Source: §9 · p. 9

A.1 FlipFlop Experiment: Design Considerations

The experiment is designed to measure answer changes after non-coercive challenges while preserving conversational realism, reproducibility, extraction coverage, and interpretable classification outputs.

  • The experiment uses free-form generation followed by task-specific label extraction rather than logprob classification, better simulating conversational responses with explanations.Source: §A.1 · p. 12
  • Setting temperature to zero uses greedy decoding to simplify reproducibility, although higher temperatures increase FlipFlop effects and result variance.Source: §A.1 · p. 12
  • The protocol requires label extraction to succeed in more than 95% of samples so results remain valid across tested conditions.Source: §A.1 · p. 12
  • Challenger utterances encourage reconsideration without coercion, aiming to measure intrinsic sycophantic tendencies rather than externally forced changes.Source: §A.1 · p. 12
  • Figure 4 reports initial accuracy and the FlipFlop effect across temperature settings for GPT3.5-Turbo on SciQ.Source: Figure 4 · p. 12

A.2 Relating FlipFlop Effect and Temperature

The appendix examines temperature sensitivity, documents the evaluation tasks and model access, and describes the Mistral-7B fine-tuning setup and synthetic challenger data.

A.2 Relating FlipFlop Effect and Temperature

  • 0.3: the FlipFlop effect increases in magnitude for each 0.1 increase in temperature, while initial accuracy declines as temperature rises.Source: §A.2 · pp. 12–13
  • T = 0: the core experiment uses the lowest temperature because higher temperature degrades initial accuracy and increases sycophantic behavior, while conservatively underestimating effects relative to T = 1.Source: §A.2 · p. 13

A.3 Evaluation Task Selection

  • Seven classification tasks cover logical fallacy detection, legal contract questions, truthful multiple-choice questions, humorous captions, science questions, and summary consistency.Source: §A.3 · p. 13

A.4 Model Access Detail

  • The study combines open-source and API-accessed proprietary models, including Llama2, Mistral, PaLM, Claude, Cohere, Gemini, GPT3.5-Turbo, and GPT-4.Source: §A.4 · pp. 13–14

A.5 Finetuning Details

  • Fine-tuning uses Mistral-7B-Instruct-v0 with QLoRA for one epoch, batch size 8, and a single A100 GPU.Source: §A.5 · p. 14

A.6 Finetuning Synthetic Dataset Creation

  • The synthetic fine-tuning dataset uses diverse challenging prompts, including repeated requests for confirmation, reconsideration, and double-checking.Source: §A.6 · pp. 14–15

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