Search Results for author: Joo-Kyung Kim

Found 13 papers, 1 papers with code

Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models

no code implementations18 Feb 2024 Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Long, Sudipta Kar, Abhinav Sethy, Joo-Kyung Kim, Dongyeop Kang

Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks.

II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering

no code implementations16 Feb 2024 Jihyung Kil, Farideh Tavazoee, Dongyeop Kang, Joo-Kyung Kim

II-MMR then analyzes this path to identify different reasoning cases in current VQA benchmarks by estimating how many hops and what types (i. e., visual or beyond-visual) of reasoning are required to answer the question.

Question Answering Visual Question Answering

Graph-Guided Reasoning for Multi-Hop Question Answering in Large Language Models

no code implementations16 Nov 2023 Jinyoung Park, Ameen Patel, Omar Zia Khan, Hyunwoo J. Kim, Joo-Kyung Kim

Specifically, we first leverage LLMs to construct a "question/rationale graph" by using knowledge extraction prompting given the initial question and the rationales generated in the previous steps.

Multi-hop Question Answering Question Answering

Cluster-Guided Label Generation in Extreme Multi-Label Classification

1 code implementation17 Feb 2023 Taehee Jung, Joo-Kyung Kim, Sungjin Lee, Dongyeop Kang

For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treating "Wikipedia" and "Wiki" as independent and separate labels.

Classification Extreme Multi-Label Classification

Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding

no code implementations25 Sep 2021 Joo-Kyung Kim, Guoyin Wang, Sungjin Lee, Young-Bum Kim

A large-scale conversational agent can suffer from understanding user utterances with various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity.

Spoken Language Understanding

Supervised Domain Enablement Attention for Personalized Domain Classification

no code implementations EMNLP 2018 Joo-Kyung Kim, Young-Bum Kim

The attention weights are explicitly encouraged to be similar to the corresponding elements of the ground-truth's one-hot vector by supervised attention, and the attention information of the other enabled domains is leveraged through self-distillation.

Classification domain classification +2

Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates

no code implementations29 Jun 2018 Joo-Kyung Kim, Young-Bum Kim

In domain classification for spoken dialog systems, correct detection of out-of-domain (OOD) utterances is crucial because it reduces confusion and unnecessary interaction costs between users and the systems.

Classification domain classification +2

A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding

no code implementations NAACL 2018 Young-Bum Kim, Dongchan Kim, Joo-Kyung Kim, Ruhi Sarikaya

Intelligent personal digital assistants (IPDAs), a popular real-life application with spoken language understanding capabilities, can cover potentially thousands of overlapping domains for natural language understanding, and the task of finding the best domain to handle an utterance becomes a challenging problem on a large scale.

domain classification General Classification +2

Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources

no code implementations EMNLP 2017 Joo-Kyung Kim, Young-Bum Kim, Ruhi Sarikaya, Eric Fosler-Lussier

Evaluating on POS datasets from 14 languages in the Universal Dependencies corpus, we show that the proposed transfer learning model improves the POS tagging performance of the target languages without exploiting any linguistic knowledge between the source language and the target language.

Cross-Lingual Transfer Language Modelling +7

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