Alexander Panchenko

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...

Aspect-Based Sentiment Analysis (ABSA) focuses on extracting sentiment at a fine-grained aspect level and has been widely applied across real-world domains. However, existing ABSA research relies on coarse-grained categorical labels (e.g., positive, negative), which limits its ability to capture...

Machine Unlearning is a valuable ability of LLMs, enabling the removal of unsafe, outdated, or private information.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 reli...

Frequent revisions of complex regulatory documents in large organizations often introduce inconsistencies and contradictions that are difficult for lawyers and auditors to detect manually. Existing tools rely on character-level diffs and therefore miss paraphrases and semantic shifts. We introduc...

Self-consistency improves large language models by sampling multiple reasoning paths and selecting the most frequent answer, but majority vote often fails to recover correct answers that are already present among samples. In this work, we reformulate answer selection in self-consistency as a rank...

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 Pytho...

Machine Unlearning (MU) enables Large Language Models (LLMs) to remove unsafe or outdated information. However, existing work assumes that all facts are equally forgettable and largely ignores whether the forgotten knowledge originates from pretraining or supervised fine-tuning (SFT). In this pap...

Frequent revisions of complex, lengthy regulatory documents in large organizations often create inconsistencies and contradictions that lawyers and auditors find difficult to detect manually. Existing tools rely on character-level diffs that fail to capture paraphrases and semantic shifts. We pre...

The presence of factual hallucinations in large language model (LLM) generations drives the development of retrieval-augmented factuality evaluation tools. However, existing implementations suffer from slow inference, outdated knowledge bases, and the use of paid search and LLM APIs, which hinder...