Papers by Moa Johansson

5 papers
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.
Benchmarking Debiasing Methods for LLM-based Parameter Estimates (2025.emnlp-main)

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Challenge: Large language models (LLMs) are expensive yet powerful ways to annotate text, and can be inconsistent when compared with experts.
Approach: They propose to combine LLM annotations with a limited number of expensive expert annotations to produce valid estimates.
Outcome: The proposed methods produce consistent estimates under theoretical assumptions, but they are not comparable across finite datasets.
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.
Recursive numeral systems are highly regular and easy to process (2026.eacl-long)

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Challenge: Existing studies on linguistic efficiency have focused on the systematicity of forms, a key property of natural language.
Approach: They propose to incorporate regularity across sets of forms in studies of efficiency in language . they use the Minimum Description Length approach to measure regularity and processing complexity .
Outcome: The proposed method shows that recursive numeral systems are more efficient with respect to regularity and processing complexity.

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