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Found 5 papers, 1 papers with code
Aligning Language Models to Explicitly Handle Ambiguity
However, conversational agents built upon even the most recent large language models (LLMs) face challenges in processing ambiguous inputs, primarily due to the following two hurdles: (1) LLMs are not directly trained to handle inputs that are too ambiguous to be properly managed; (2) the degree of ambiguity in an input can vary according to the intrinsic knowledge of the LLMs, which is difficult to investigate.
Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP
When deploying machine learning systems to the wild, it is highly desirable for them to effectively leverage prior knowledge to the unfamiliar domain while also firing alarms to anomalous inputs.
Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-shot In-Context Learners
Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning.
Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator
Large-scale pre-trained language models (PLMs) are well-known for being capable of solving a task simply by conditioning a few input-label pairs dubbed demonstrations on a prompt without being explicitly tuned for the desired downstream task.
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations
Despite recent explosion of interests in in-context learning, the underlying mechanism and the precise impact of the quality of demonstrations remain elusive.
