Aug 2026· 2 citations· ⚡ 1 influential· 53 references
Computer Science
TL;DR
Interactions are introduced as a fine-grained tool to analyze prompt sensitivity of LLMs and it is discovered that subtle changes to prompts can trigger severe instability in interactions, even when the outputs of the LLM remain the same.
Abstract
The remarkable capabilities of large language models (LLMs) are often undermined by their instability. Even subtle and semantically irrelevant changes in prompts can cause dramatic fluctuations in performance, a phenomenon known as prompt sensitivity. Previous studies typically evaluate prompt sensitivity by comparing the LLM's final outputs when prompts change. However, such coarse-grained metrics fail to explain the internal reasons for prompt sensitivity. In this paper, we introduce interactions as a fine-grained tool to analyze prompt sensitivity of LLMs. Specifically, we decompose the output score of the LLM into a set of interactions. Each interaction represents a nonlinear relationship involving a set of input variables. We discover that subtle changes to prompts can trigger severe instability in interactions, even when the outputs of the LLM remain the same. To this end, we propose an Interaction-based Prompt Sensitivity (IPS) metric by quantifying changes in interactions when we introduce subtle changes to prompts. We apply the IPS metric to 50 open-source LLMs and uncover four factors that reduce the prompt sensitivity of LLMs, including supervised fine-tuning, increased model scales, dense architectures, and few-shot learning. More crucially, we discover a common mechanism by which these four factors reduce prompt sensitivity: all four factors tend to reduce the prompt sensitivity of low-order interactions (i.e., interactions involving few input variables).
Large Language Models (LLMs) are increasingly used for code smell detection tasks due to their ability to interpret program semantics. However, their reliability in this context remains poorly explored, particularly under varying prompt conditions where model predictions may be influenced by external cues rather than code characteristics. One such limitation is sycophancy bias, where models tend to align their outputs with user-provided assumptions instead of performing objective analysis. In this paper, we present the first systematic empirical study of sycophancy bias in LLM-based code smell detection. Using the MLCQ dataset, we evaluate how different prompt framings like confirmation bias, contradictory hints, and false premises affect model predictions. Our results show that LLMs are highly sensitive to prompt variations, with Decision Flip Rates reaching up to 72% and False Alignment Rates exceeding 90%, indicating substantial instability and agreement with misleading prompts. To address this issue, we propose Evidence-Guided Debiasing Prompting (EGDP), a structured prompting strategy that enforces evidence-first reasoning. EGDP reduces decision instability and improves robustness, lowering Decision Flip Rates to as low as 12% and False Alignment Rates to as low as 21%, while increasing reliance on structurally grounded evidence. Our findings demonstrate that sycophancy bias poses a critical threat to the reliability of LLM-based code smell detection, and that evidence-guided reasoning provides an effective and generalizable mitigation approach.
Istiaq Ahmed Fahad, K. Asif, Mohammad A. Tawhid· 0 citations
This tutorial provides a comprehensive and up-to-date overview of LLM mechanism discovery, validation, and editing, and surveys mechanistic editing techniques that leverage MI insights to modify behavior at varying granularity.
Yinhan He, Wendy Zheng, Tianyi Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
SPARK is introduced, which uses hidden-state response to diagnose whether a model internally enters an effective reasoning state and to guide lightweight test-time steering, and suggests that susceptibility can serve not only as a diagnostic signal for reasoning failures, but also as a practical guide for targeted test-time intervention.
Dongxu Zhang, Yiding Sun, Zihao Guo et al.· 0 citations
This work introduces a search-based approach that identifies and evolves a set of natural language transformation rules with strong downstream effects on coding performance, and proposes DUALFIX, a staged repair pipeline that combines the evolved transformation rules with execution-feedback repair, addressing both specification-level and implementation-level failures.
Amal Akli, Melissa Akli, Cedric Richter et al.· 0 citations
This work presents the first systematic study of inverse relation directionality in LLMs, using a benchmark consisting of 5,457 instances spanning 27 distinct inverse relation labels and reveals systematic asymmetries in inverse relation classification across LLMs.
A methodical investigation of temporal prompting techniques for LLM-based EL is presented, and it is demonstrated that explicit temporal prompting can reduce drift mistakes by up to 40% using a dataset of temporally-sensitive mentions linked with Wikidata snapshots.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
