6 papers · 1 filter
Are LLMs ready for HardChoices?
Dmitry Nikolaev
A lot of research attention has been devoted to checking whether large language models (LLMs) are politically biased. This work has largely focused on high-level ideological dimens…
Approximate Attributions for Off-the-Shelf Siamese Transformers
Lucas Möller, Dmitry Nikolaev, Sebastian Padó
Siamese encoders such as sentence transformers are among the least understood deep models. Established attribution methods cannot tackle this model class since it compares two inpu…
Beyond prompt brittleness: Evaluating the reliability and consistency of political worldviews in LLMs
Tanise Ceron, Neele Falk, Ana Barić +2
Due to the widespread use of large language models (LLMs), we need to understand whether they embed a specific "worldview" and what these views reflect. Recent studies report that,…
An Attribution Method for Siamese Encoders
Lucas Möller, Dmitry Nikolaev, Sebastian Padó
Despite the success of Siamese encoder models such as sentence transformers (ST), little is known about the aspects of inputs they pay attention to. A barrier is that their predict…
Multilingual estimation of political-party positioning: From label aggregation to long-input Transformers
Dmitry Nikolaev, Tanise Ceron, Sebastian Padó
Scaling analysis is a technique in computational political science that assigns a political actor (e.g. politician or party) a score on a predefined scale based on a (typically lon…
Investigating semantic subspaces of Transformer sentence embeddings through linear structural probing
Dmitry Nikolaev, Sebastian Padó
The question of what kinds of linguistic information are encoded in different layers of Transformer-based language models is of considerable interest for the NLP community. Existin…