Nikita Afonin, Nikita Andriianov, Vahagn Hovhannisyan, Nikhil Bageshpura, Kyle Liu Algoverse AI Research, Kevin Zhu Algoverse AI Research, Sunishchal Dev RAND Corporation and Algoverse Coding Academy LLC, Ashwinee Panda TogetherAI and University of Maryland, College Park 等 12 位作者
Recent work has shown that narrow finetuning can produce broadly misaligned LLMs, a phenomenon termed emergent misalignment (EM). While concerning, these findings were limited to finetuning and activation steering, leaving out in-context learning (ICL). We therefore ask: does EM emerge in ICL? We find that it does: across four model families (Gemini, Kimi-K2, Grok, and Qwen), narrow in-context examples cause models to produce misaligned responses to benign, unrelated queries. With 16 in-context examples, EM rates range from 1% to 24% depending on model and domain, appearing with as few as 2 examples. Neither larger model scale nor explicit reasoning provides reliable protection, and larger models are typically even more susceptible. Next, we formulate and test a hypothesis, which explains in-context EM as conflict between safety objectives and context-following behavior. Consistent with this, instructing models to prioritize safety reduces EM while prioritizing context-following increases it. These findings establish ICL as a previously underappreciated vector for emergent misalignment that resists simple scaling-based solutions.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Mikhail Seleznyov AXXX, Daniil Korbut Amazon, Viktor Moskvoretskii EPFL - EPF Lausanne, Oleg Somov DS-NLP Group, Moscow Independent Research Institute of Artificial Intelligence and AXXX, Alexander Panchenko S-NLP group, Elena Tutubalina
Uncertainty quantification (UQ) methods for large language models are predominantly designed by hand based on domain knowledge and heuristics, limiting their scalability and generality. We apply LLM-powered evolutionary search to automatically discover unsupervised UQ methods represented as Python programs. On the task of atomic claim verification, our evolved methods outperform strong manually-designed baselines, achieving up to 6.7% relative ROC-AUC improvement across 9 datasets while generalizing robustly out-of-distribution. Qualitative analysis reveals that different LLMs employ qualitatively distinct evolutionary strategies: Claude models consistently design high-feature-count linear estimators, while Gpt-oss-120B gravitates toward simpler and more interpretable positional weighting schemes. Surprisingly, only Sonnet 4.5 and Opus 4.5 reliably leverage increased method complexity to improve performance – Opus 4.6 shows an unexpected regression relative to its predecessor. Overall, our results hint that LLM-powered evolutionary search is a promising paradigm for automated, interpretable hallucination detector design.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表
Pavel Tikhonov, Ivan Oseledets Institute of Numerical Mathematics, Elena Tutubalina
In-context learning (ICL) enables Large Language Models (LLMs) to adapt to new tasks using few examples, with task vectors, defined as specific hidden state activations hypothesized to encode task information. Existing studies are limited by small-scale benchmarks, restricting comprehensive analysis. We introduce QᴜɪᴛᴇAFᴇᴡ, a novel dataset of 3,096 diverse few-shot tasks, each with 30 input-output pairs derived from the Alpaca dataset. Experiments with Llama-3-8B on QᴜɪᴛᴇAFᴇᴡ reveal: (1) task vector performance peaks at an intermediate layer (e.g., 15th), (2) effectiveness varies significantly by task type, and (3) complex tasks rely on multiple, subtask-specific vectors rather than a single vector, suggesting distributed task knowledge representation.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表
Anna Borisiuk DS-NLP Group, Andrey Savchenko HSE University, Alexander Panchenko S-NLP group, Elena Tutubalina
Machine Unlearning is a valuable ability of LLMs, enabling the removal of unsafe, outdated, or private information. Existing unlearning methods, however, are often evaluated under the assumption that all facts are equally challenging to forget. Controllable knowledge removal is essential for reliable NLP systems. In this paper, we investigate whether fact popularity influences the efficiency of LLM unlearning. To answer this question, we build UNLamb benchmark designed to systematically investigate this relationship. It consists of 11.6k question-answer pairs derived from real-world knowledge in Wikidata, explicitly partitioned into rare and popular facts. Using this benchmark, we perform a comprehensive evaluation of state-of-the-art unlearning algorithms on a set of models of different sizes. We conduct a comprehensive analysis of four unlearning methods across three validation sets and two LLMs. We show that larger models struggle more to forget popular entities, often damaging related knowledge in the process. In contrast, it is much easier to remove rare facts without side effects.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表