Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA
The development of large language models (LLMs) has led to an increased focus on their adaptation to specialized doma...
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The development of large language models (LLMs) has led to an increased focus on their adaptation to specialized doma...
Neurosymbolic semantics is fragmented: classical, fuzzy, probabilistic and neural systems each define truth by their ...
When social chatbots make mistakes, and they do, how they recover determines whether users trust them again. Social c...
A longstanding goal of research on interpretable deep learning is to replace opaque neural computations with human-me...
Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts ...
Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, mu...
Post-training of reasoning language models is commonly driven by supervised distillation and reinforcement learning w...
Preference-based RL provides an approach to learning reward models from pairwise comparisons of behaviors, bypassing ...
Multi-agent LLM systems share state through memory stores, vector indices, and tool registries. We model such sharing...
Vision-language models (VLMs) are typically trained as passive answerers, while their ability to actively ask diverse...
Vision encoders for retrieval are typically trained with class-label supervision: each training pair reduces to a sca...
In this report, we present LOGOS (Language Of Generative Objects in Science), a scientific generative language model ...