3 accepted papers
Collecting instruction fine-tuning (IFT) data is a resource and time intensive task especially in multilingual setting where finding proficient native speakers is challenging. Moreover, traditional data collection is prone to privacy risks, toxicity and lacks scalability. While, fully synthetic data…
Multilingual LLMs have achieved remarkable benchmark performance, but we find they continue to underperform on non-Latin script languages across contemporary LLM families. This discrepancy arises from the fact that LLMs are pretrained with orthographic scripts, which are dominated by Latin character…
Modeling interlocutor information is essential towards modeling multi-party conversations to account for the presence of multiple participants. We investigate the role of including the persona attributes of both the speaker and addressee relevant to each utterance, collected via 3 distinct mock soci…