Papers by Denitsa Saynova
Sudden Semantic Shifts in Swedish NATO discourse (2023.acl-srw)
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| Challenge: | Using word embeddings, we study sudden semantic shifts that occur when a sudden event radically changes public opinion on a topic. |
| Approach: | They use word embeddings to study how Twitter associations evolve . they find domain knowledge and data selection are of prime importance when using word embeds to understand semantic shifts. |
| Outcome: | The proposed method validates associations on Twitter with NATO in real-world events but is difficult to distinguish between noise and real-time signals. |
The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are useful interfaces to factual knowledge, but their usefulness is limited by their tendency to deliver inconsistent answers to semantically equivalent questions. |
| Approach: | They evaluate the effectiveness of up-scaling and augmenting the LM with a passage retrieval database to reduce inconsistency. |
| Outcome: | The proposed models reduce inconsistency but retrieval augmentation is more efficient. |
Fact Recall, Heuristics or Pure Guesswork? Precise Interpretations of Language Models for Fact Completion (2025.findings-acl)
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| Challenge: | Language models (LMs) can make a correct prediction based on many possible signals in a prompt, but not all corresponding to recall of factual associations. |
| Approach: | They propose a model-specific recipe for constructing datasets with examples of four different prediction scenarios: generic language modeling, guesswork, heuristics recall and exact fact recall. |
| Outcome: | The proposed model-specific recipe yields distinct results for each scenario. |