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Recent large audio language models (LALMs) have extended their capabilities beyond lexical speech understanding to tasks requiring paralinguistic and non-linguistic audio understanding, such as emotion recognition. However, these models still struggle to reliably utilize acoustic information, particularly when it co-oc...
A challenging task in which an LLM predicts the relation between two glosses (definitions of senses) presented in two different orders, suggesting limitations in current LLMs's semantic relation knowledge, both in identifying relations and in respecting asymmetry.
This work introduces a reliable evaluation approach for DDG, based on newly proposed evaluation criteria and powered by an LLM-as-a-judge, and proposes an LLM-based LDDG approach that employs iterative simplification.
Experimental results show that FRC-based fine-tuning substantially improves 7B LLMs over direct Doc2Doc fine-tuning and outperforms existing DocMT methods on IWSLT2017.
This work proposes CAROT (Cross-Lingual Alignment of Representations in a Language-Agnostic Space via Optimal Transport), which consists of identifying language-specific representations in LLMs'internal states and aligning language-agnostic representations across languages at the token level by optimal transport, while...
The results empirically show that relation geometry is not equally well-represented for all relations in semantic space, suggesting that there is a difference in how well semantic relations might be learned from distributional information alone.
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