5 papers
CUB: Benchmarking Context Utilisation Techniques for Language Models
Lovisa Hagström, Youna Kim, Haeun Yu +4
Incorporating external knowledge is crucial for knowledge-intensive tasks, such as question answering and fact checking. However, language models (LMs) may ignore relevant informat…
UniKnow: A Unified Framework for Reliable Language Model Behavior across Parametric and External Knowledge
Youna Kim, Hyuhng Joon Kim, Minjoon Choi +4
Language models often benefit from external knowledge beyond parametric knowledge. While this combination enhances performance, achieving reliable knowledge utilization remains cha…
When to Speak, When to Abstain: Contrastive Decoding with Abstention
Hyuhng Joon Kim, Youna Kim, Sang-goo Lee +1
Large Language Models (LLMs) demonstrate exceptional performance across diverse tasks by leveraging pre-trained (i.e., parametric) and external (i.e., contextual) knowledge. While…
Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts
Youna Kim, Hyuhng Joon Kim, Cheonbok Park +6
When using large language models (LLMs) in knowledge-intensive tasks, such as open-domain question answering, external context can bridge the gap between external knowledge and the…
Aligning Language Models to Explicitly Handle Ambiguity
Hyuhng Joon Kim, Youna Kim, Cheonbok Park +5
In interactions between users and language model agents, user utterances frequently exhibit ellipsis (omission of words or phrases) or imprecision (lack of exactness) to prioritize…