1 code implementation • LREC 2022 • Fitsum Gaim, Wonsuk Yang, Jong C. Park
Language identification is one of the fundamental tasks in natural language processing that is a prerequisite to data processing and numerous applications.
no code implementations • 22 Apr 2024 • Sukmin Cho, Soyeong Jeong, Jeongyeon Seo, Taeho Hwang, Jong C. Park
The robustness of recent Large Language Models (LLMs) has become increasingly crucial as their applicability expands across various domains and real-world applications.
1 code implementation • 21 Mar 2024 • Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park
Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA).
no code implementations • 26 Oct 2023 • Sukmin Cho, Jeongyeon Seo, Soyeong Jeong, Jong C. Park
Large language models (LLMs) enable zero-shot approaches in open-domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever.
1 code implementation • 20 Oct 2023 • Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park
Moreover, further finetuning LMs with labeled datasets is often infeasible due to their absence, but it is also questionable if we can transfer smaller LMs having limited knowledge only with unlabeled test data.
1 code implementation • 19 Oct 2023 • Jinheon Baek, Soyeong Jeong, Minki Kang, Jong C. Park, Sung Ju Hwang
Recent Language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters.
no code implementations • 21 Sep 2023 • Eui Jun Hwang, Huije Lee, Jong C. Park
Gloss-free Sign Language Production (SLP) offers a direct translation of spoken language sentences into sign language, bypassing the need for gloss intermediaries.
no code implementations • 12 Jun 2023 • Hancheol Park, Jong C. Park
Natural language understanding (NLU) tasks face a non-trivial amount of ambiguous samples where veracity of their labels is debatable among annotators.
1 code implementation • 7 Jun 2023 • Soyeong Jeong, Jinheon Baek, Sung Ju Hwang, Jong C. Park
To address this problem, we further introduce a novel contrastive learning strategy, making sure to reflect previous turns when retrieving the phrase for the current context, by maximizing representational similarities of consecutive turns in a conversation while minimizing irrelevant conversational contexts.
1 code implementation • 5 Jun 2023 • Hoyun Song, Jisu Shin, Huije Lee, Jong C. Park
Our detailed analysis shows that the proposed model is effective at leveraging domain knowledge, transferable to other mental disorders, and providing interpretable detection results.
1 code implementation • 23 May 2023 • Sukmin Cho, Soyeong Jeong, Jeongyeon Seo, Jong C. Park
Along with highlighting the impact of optimization on the zero-shot re-ranker, we propose a novel discrete prompt optimization method, Constrained Prompt generation (Co-Prompt), with the metric estimating the optimum for re-ranking.
1 code implementation • 10 Feb 2023 • Soyeong Jeong, Jinheon Baek, Sung Ju Hwang, Jong C. Park
Conversational Question Answering (ConvQA) models aim at answering a question with its relevant paragraph and previous question-answer pairs that occurred during conversation multiple times.
no code implementations • 12 Aug 2022 • Eui Jun Hwang, Jung Ho Kim, Suk min Cho, Jong C. Park
We propose a novel NAR-SLP model via Knowledge Distillation (KD) to address these problems.
1 code implementation • LREC 2022 • Huije Lee, Young Ju NA, Hoyun Song, Jisu Shin, Jong C. Park
In particular, we constructed a pair-wise dataset that includes troll comments and counter responses with labeled response strategies, which enables models fine-tuned on our dataset to generate responses by varying counter responses according to the specified strategy.
1 code implementation • ACL 2022 • Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park
Dense retrieval models, which aim at retrieving the most relevant document for an input query on a dense representation space, have gained considerable attention for their remarkable success.
Ranked #1000000000 on Passage Retrieval on Natural Questions
1 code implementation • NAACL (sdp) 2021 • Soyeong Jeong, Jinheon Baek, ChaeHun Park, Jong C. Park
In this paper, we propose an Unsupervised Document Expansion with Generation (UDEG) framework with a pre-trained language model, which generates diverse supplementary sentences for the original document without using labels on query-document pairs for training.
no code implementations • IJCNLP 2017 • Jinseon You, Jin-Woo Chung, Wonsuk Yang, Jong C. Park
Genetic information in the literature has been extensively looked into for the purpose of discovering the etiology of a disease.