4 citations · 7 across the 11 of their papers we have counts for
14 papers
Nameless Tokenization: A Lossless Tokenizer-Level Defense Against Control-Token Forgery in Open-Weight LLMs
Kisu Yang, Yoonna Jang, Heuiseok Lim
Open-weight language models publish the strings their chat templates use to mark turns, roles and tool results, which the tokenizer maps back to the reserved identifiers the model…
Llamion Technical Report
Kisu Yang, Yoonna Jang, Hyeonseok Moon +6
We release Llamion, a family of 14B-parameter open-weight language models obtained by transforming Orion-14B into the standardized Llama-family architecture. The transformation is…
Expanding Computation Spaces of LLMs at Inference Time
Yoonna Jang, Kisu Yang, Isabelle Augenstein
Chain-of-thought (CoT) rationale enables language models to use additional task-related text for problem-solving, benefiting not only from detailed reasoning steps but also from th…
Reliable Evaluation Protocol for Low-Precision Retrieval
Kisu Yang, Yoonna Jang, Hwanseok Jang +3
Lowering the numerical precision of model parameters and computations is widely adopted to improve the efficiency of retrieval systems. However, when computing relevance scores bet…
Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations
Yoonna Jang, Suhyune Son, Jeongwoo Lee +6
Despite the striking advances in recent language generation performance, model-generated responses have suffered from the chronic problem of hallucinations that are either untrue o…
KoBigBird-large: Transformation of Transformer for Korean Language Understanding
Kisu Yang, Yoonna Jang, Taewoo Lee +4
This work presents KoBigBird-large, a large size of Korean BigBird that achieves state-of-the-art performance and allows long sequence processing for Korean language understanding.…
You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona
Jungwoo Lim, Myunghoon Kang, Yuna Hur +8
To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model th…
Analysis of Utterance Embeddings and Clustering Methods Related to Intent Induction for Task-Oriented Dialogue
Jeiyoon Park, Yoonna Jang, Chanhee Lee +1
The focus of this work is to investigate unsupervised approaches to overcome quintessential challenges in designing task-oriented dialog schema: assigning intent labels to each dia…
Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models
Suhyune Son, Chanjun Park, Jungseob Lee +5
As pre-trained language models become more resource-demanding, the inequality between resource-rich languages such as English and resource-scarce languages is worsening. This can b…
FreeTalky: Don't Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue
Chanjun Park, Yoonna Jang, Seolhwa Lee +2
We propose a deep learning-based foreign language learning platform, named FreeTalky, for people who experience anxiety dealing with foreign languages, by employing a humanoid robo…
Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge
Yoonna Jang, Jungwoo Lim, Yuna Hur +6
Humans usually have conversations by making use of prior knowledge about a topic and background information of the people whom they are talking to. However, existing conversational…
PicTalky: Augmentative and Alternative Communication Software for Language Developmental Disabilities
Chanjun Park, Yoonna Jang, Seolhwa Lee +3
Augmentative and alternative communication (AAC) is a practical means of communication for people with language disabilities. In this study, we propose PicTalky, which is an AI-bas…
Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features
Bruce W. Lee, Yoo Sung Jang, Jason Hyung-Jong Lee
We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random For…
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning
Jungwoo Lim, Dongsuk Oh, Yoonna Jang +2
CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance w…