3 papers
cs.CL2025
Factors affecting the in-context learning abilities of LLMs for dialogue state tracking
Pradyoth Hegde, Santosh Kesiraju, Jan Švec +5
This study explores the application of in-context learning (ICL) to the dialogue state tracking (DST) problem and investigates the factors that influence its effectiveness. We use…
eess.AS2025
Approaching Dialogue State Tracking via Aligning Speech Encoders and LLMs
Šimon Sedláček, Bolaji Yusuf, Ján Švec +4
In this work, we approach spoken Dialogue State Tracking (DST) by bridging the representation spaces of speech encoders and LLMs via a small connector module, with a focus on fully…
cs.CL2024
Aligning Pre-trained Models for Spoken Language Translation
Šimon Sedláček, Santosh Kesiraju, Alexander Polok +1
This paper investigates a novel approach to end-to-end speech translation (ST) based on aligning frozen pre-trained automatic speech recognition (ASR) and machine translation (MT)…