Papers by Karin De Langis
How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs (2025.acl-long)
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| Challenge: | Large language models exhibit increasingly sophisticated linguistic capabilities, yet the extent to which these models reflect human-like cognition versus advanced pattern recognition remains an open question. |
| Approach: | They conduct a series of targeted experiments to assess whether LLMs construct semantic representations and pragmatic inferences in a human-like manner. |
| Outcome: | The proposed framework can be used to assess the cognitive and linguistic capabilities of large language models (LLMs). |
Mary, the Cheeseburger-Eating Vegetarian: Do LLMs Recognize Incoherence in Narratives? (2026.eacl-long)
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Karin De Langis, Püren Öncel, Ryan Peters, Andrew Elfenbein, Laura Kristen Allen, Andreas Schramm, Dongyeop Kang
| Challenge: | Contemporary models of (human) reading comprehension characterize comprehension as a dynamic process in which the reader continually builds and updates representations to maintain coherence and integrate new information with prior knowledge. |
| Approach: | They use a paired narrative dataset to examine the extent to which large language models can reliably separate incoherent and coherent stories. |
| Outcome: | The proposed models do not eliminate the deficits in the model internal state and behavior. |
Dynamic Multi-Reward Weighting for Multi-Style Controllable Generation (2024.emnlp-main)
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| Challenge: | Prior work explored the domain of controlled style generation, a task in which a generative language model aims to generate text with a specified style 2 . however in practice, text often contains not only a single style, but a combination of styles. |
| Approach: | They propose to use calibrated outputs from discriminators and dynamic weighting by discriminator gradient magnitudes to combine multiple styles in a reward function. |
| Outcome: | The proposed dynamic weighting outperforms static weighting approaches with respect style control while maintaining linguistic quality. |