publications

2026

1. COLM

   The Garden of Forking Prompts: How Users Explore Narrative Space in Story Generation

   Advait Deshmukh, 2 more authors, and Maria Antoniak

   In Proceedings of the Conference on Language Modeling (COLM), Oct 2026

   Preprint coming soon.
2. arXiv

   Narrative-UFET: Narrative Generation for Ultra-Fine Entity Typing

   Mreedul Gupta, Advait Deshmukh, 2 more authors, and Maria Leonor Pacheco

   Jun 2026

   Abs arXiv

   Ultra-fine entity typing (UFET) assigns highly specific types to entity mentions, but current approaches struggle with types in the long tail. We hypothesize that a key limitation is the reliance on sentence-level context, since disambiguating evidence is often spread across multiple sentences. We present Narrative-UFET, a controlled extension of UFET in which each entity mention is paired with an automatically generated short, coherent narrative. We experiment with two paired variants: one in which the entity’s type is held constant across the narrative (Maintain) and one in which it shifts (Change). Narrative context yields consistent improvements on long-tail types over sentence-level baselines, with the Change variant providing the stronger signal.

3. ACL

   A Structured Clustering Approach for Inducing Media Narratives

   Rohan Das, Advait Deshmukh, 3 more authors, and Maria Leonor Pacheco

   In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2026

   Abs DOI

   Media narratives wield tremendous power in shaping public opinion, yet computational approaches struggle to capture the nuanced storytelling structures that communication theory emphasizes as central to how meaning is constructed. Existing approaches either miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability. To bridge this gap, we present a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering. Our approach produces explainable narrative schemas that align with established framing theory while scaling to large corpora without exhaustive manual annotation.

2025

1. *SEM

   All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing

   Advait Deshmukh, 2 more authors, and Maria Leonor Pacheco

   In Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025), Nov 2025

   Abs DOI

   Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity typing tasks where the space of labels is extremely large. In this work, we explore the limitations of the knowledge acquired by PLMs by proposing a novel heuristic to approximate the pre-training distribution of entities when the pre-training data is unknown. Then, we systematically demonstrate that entity-typing approaches that rely solely on the parametric knowledge of PLMs struggle significantly with entities at the long tail of the pre-training distribution, and that knowledge-infused approaches can account for some of these shortcomings. Our findings suggest that we need to go beyond PLMs to produce solutions that perform well for infrequent entities.