arXiv subjects

Chimaobi Okite

Publications and source records attributed to Chimaobi Okite.

3 recordsLinked to original sources

arXiv2026

The Wrong Kind of Right: Quantifying and Localizing Misfired Alignment in LLMs

Warning: This paper studies stereotypes and biases, and contains potentially disturbing examples, used for illustration purposes only. Our findings should not be interpreted as an argument against alignment. Instead, this paper highlights the need for principled approaches to more advanced alignment. Alignment aims to ensure that large language models (LLMs) behave safely and reliably, including by avoiding unsafe inferences. However, we show that such safety-oriented behaviors can misfire: models may reject warranted conclusions even when they are explicitly supported by context. We call this failure mode misfired alignment, where alignment-induced changes cause LLMs to override explicit evidence. To quantify this phenomenon, specifically on stereotype-related alignment, we introduce VETO, a benchmark consisting of 2,032 BBQ-derived contrastive pairs, and define a new metric, Misfired Alignment Rate (MAR), which measures on a 0 to 100 scale how often a model fails on a stereotype-related question but succeeds on its contrastive counterpart. We benchmark 25 LLMs on VETO, and show that all LLMs, including the most recent ones, exhibit non-trivial (4.7 to 18.9%) MARs while all human participants achieve 0.0% MAR. Controlled priming experiments further show that alignment-induced cues can substantially amplify MAR across LLMs, indicating that these failures are not merely artifacts of individual examples but can be induced by safety-related framing. Mechanistic analyses on open-weight LLMs reveal late-layer suppression of evidence-supported answers, and comparisons between instruct and base LLMs suggest that this suppression emerges after instruction training. These findings show that current alignment methods can overgeneralize surface-level safety cues, to the point of overriding objective evidence, motivating more work on alignment objectives that better preserve contextual grounding.

cs.CL↗

arXiv2026

LUCid: Redefining Relevance For Lifelong Personalization

Work to date has mainly relied on semantic proximity to identify relevant content for lifelong personalization. However, situational relevance is often more important for determining which information is useful for a user's actual task and context. In this paper, we introduce the Proximity Advantage (PA) score, a metric for quantifying semantic proximity bias, and show that existing personalization benchmarks largely conflate semantic and situational proximity, leaving it unclear whether current systems truly capture situational relevance. To support this metric, we introduce LUCid, a diagnostic benchmark of 1,936 user queries paired with long interaction histories, designed to isolate situational relevance from semantic proximity. Our experiments across different stages of the modern personalization pipeline (retrieval, reranking, and generation) reveal significant performance collapse: retrieval recall drops to near zero on the hardest instances, and response alignment remains near 50\% even for state-of-the-art models such as Gemini-3-Flash, GPT-5.4, and Claude Haiku, highlighting a fundamental mismatch between the relevance encoded by current systems and what lifelong personalization demands.

cs.IR↗

arXiv2025

Benchmarking and Improving LLM Robustness for Personalized Generation

Recent years have witnessed a growing interest in personalizing the responses of large language models (LLMs). While existing evaluations primarily focus on whether a response aligns with a user's preferences, we argue that factuality is an equally important yet often overlooked dimension. In the context of personalization, we define a model as robust if its responses are both factually accurate and align with the user preferences. To assess this, we introduce PERG, a scalable framework for evaluating robustness in LLMs, along with a new dataset, PERGData. We evaluate fourteen models from five different model families using different prompting methods. Our findings show that current LLMs struggle with robust personalization: even the strongest models (GPT-4.1, LLaMA3-70B) fail to maintain correctness in 5% of previously successful cases without personalization, while smaller models (e.g., 7B-scale) can fail more than 20% of the time. Further analysis reveals that robustness is significantly affected by the nature of the query and the type of user preference. To mitigate these failures, we propose Pref-Aligner, a two-stage approach that improves robustness by an average of 25% across models. Our work highlights critical gaps in current evaluation practices and introduces tools and metrics to support more reliable, user-aligned LLM deployments.

cs.CL↗