Abstract Background Large language models (LLMs) are increasingly demonstrating the potential to reach human-level performance in generating clinical summaries from patient-clinician conversations. LLMs are usually evaluated against clinical summaries that focus mainly on patients’ biology and not on their biography (e...
Maria Lizarazo Jimenez, A. Claros, Kieran Green et al.· Journal of Medical Internet...· 0 citations
This work investigates strategic diversity, or substantive variation among approaches to a problem, as an alternative principle for constructing self-training data with two sampling methods: GROOT, a new method which constructs a hierarchical tree of approaches and samples distinct paths, and Verbalized Sampling, adapt...
Alexander Gurung, Esmeralda S. Whitammer, Mirella Lapata· 0 citations
This hybrid system uses large language models to propose and ground visual elements, estimate their salience to the narrative, and generate compressed realizations, which makes better decisions than prompted LLMs about what to describe and when to describe it.
Igor Sterner, Mirella Lapata, A. Lascarides et al.· 0 citations
This work uses Reinforced Self-Training (ReST) to train the model to achieve high reward and shows this leads to a steerable policy that changes its behavior predictably conditioned on the provided costs, leading to higher reward and accuracy.
Jonathan Berant, M. Chen, Adam Fisch et al.· arXiv.org· 6 citations· ⚡2
A training-free, two-step method that identifies the tables needed to answer a question from a pixel-compressed context, and then reasons over those at native resolution, with no accuracy loss.
Audio Description (AD) is a verbal narration of key visual content in videos, enabling access for visually impaired audiences. Unlike standard video captioning, AD is a structured editorial task: descriptions must be inserted into gaps in dialogue and must convey only what is needed to understand the narrative being to...
Igor Sterner, Mirella Lapata, A. Lascarides et al.· 2 citations· ⚡1
Dynamic Heterogeneous Character Networks are introduced, which organize long novels into temporally localized heterogeneous graphs that align characters with their textual contexts, and GraphLit is proposed, a self-supervised learning framework that learns rich literary representations through a masked graph autoencode...
Gaspard Michel, Elena V. Epure, Romain Hennequin et al.· arXiv.org· 1 citation
Experiments across three domains show that Pandora's Router matches the routing quality of exhaustive estimation, while querying the expensive estimator far less often.
Adam Fisch, Shubhendu Trivedi, Fantine Huot et al.· 1 citation
Routing policies for multi-model systems are evaluated almost exclusively on task accuracy and inference cost. We argue that two properties, orthogonal to performance, determine whether routing is meaningful. First, the society of actors must be behaviourally differentiated: if all actors respond identically, routing i...
Fantine Huot, Michael Kaisers, Mirella Lapata· arXiv.org· 0 citations
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