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Oleg Rokhlenko

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AAAI Conference 2026 Conference Paper

Align to Structure: Aligning Large Language Models with Structural Information

Zae Myung Kim
Anand Ramachandran
Farideh Tavazoee
Joo-Kyung Kim
Oleg Rokhlenko
Dongyeop Kang

Generating long, coherent text remains a challenge for large language models (LLMs), as they lack hierarchical planning and structured organization in discourse generation. We introduce Structural Alignment, a novel method that aligns LLMs with human-like discourse structures to enhance long-form text generation. By integrating linguistically grounded discourse frameworks into reinforcement learning, our approach guides models to produce coherent and well-organized outputs. We employ a dense reward scheme within a Proximal Policy Optimization framework, assigning fine-grained, token-level rewards based on the discourse distinctiveness relative to human writing. Two complementary reward models are evaluated: the first improves readability by scoring surface-level textual features to provide explicit structuring, while the second reinforces deeper coherence and rhetorical sophistication by analyzing global discourse patterns through hierarchical discourse motifs, outperforming both standard and RLHF-enhanced models in tasks such as essay generation and long-document summarization.

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AAAI Conference 2021 Conference Paper

Continual Learning for Named Entity Recognition

Natawut Monaikul
Giuseppe Castellucci
Simone Filice
Oleg Rokhlenko

Named Entity Recognition (NER) is a vital task in various NLP applications. However, in many real-world scenarios (e. g. , voice-enabled assistants) new named entity types are frequently introduced, entailing re-training NER models to support these new entity types. Re-annotating the original training data for the new entity types could be costly or even impossible when storage limitations or security concerns restrict access to that data, and annotating a new dataset for all of the entities becomes impractical and error-prone as the number of types increases. To tackle this problem, we introduce a novel Continual Learning approach for NER, which requires new training material to be annotated only for the new entity types. To preserve the existing knowledge previously learned by the model, we exploit the Knowledge Distillation (KD) framework, where the existing NER model acts as the teacher for a new NER model (i. e. , the student), which learns the new entity types by using the new training material and retains knowledge of old entities by imitating the teacher’s outputs on this new training set. Our experiments show that this approach allows the student model to “progressively” learn to identify new entity types without forgetting the previously learned ones. We also present a comparison with multiple strong baselines to demonstrate that our approach is superior for continually updating an NER model.

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