3 citations · 7 across the 7 of their papers we have counts for
7 papers
Kanana: Compute-efficient Bilingual Language Models
Kanana LLM Team, Yunju Bak, Hojin Lee +26
We introduce Kanana, a series of bilingual language models that demonstrate exceeding performance in Korean and competitive performance in English. The computational cost of Kanana…
TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback
Eunseop Yoon, Hee Suk Yoon, SooHwan Eom +7
Reinforcement Learning from Human Feedback (RLHF) leverages human preference data to train language models to align more closely with human essence. These human preference data, ho…
General Item Representation Learning for Cold-start Content Recommendations
Jooeun Kim, Jinri Kim, Kwangeun Yeo +4
Cold-start item recommendation is a long-standing challenge in recommendation systems. A common remedy is to use a content-based approach, but rich information from raw contents in…
Binary Classifier Optimization for Large Language Model Alignment
Seungjae Jung, Gunsoo Han, Daniel Wontae Nam +1
In real-world services such as ChatGPT, aligning models based on user feedback is crucial for improving model performance. However, due to the simplicity and convenience of providi…
Semiparametric Token-Sequence Co-Supervision
Hyunji Lee, Doyoung Kim, Jihoon Jun +4
In this work, we introduce a semiparametric token-sequence co-supervision training method. It trains a language model by simultaneously leveraging supervision from the traditional…
How Well Do Large Language Models Truly Ground?
Hyunji Lee, Sejune Joo, Chaeeun Kim +4
To reduce issues like hallucinations and lack of control in Large Language Models (LLMs), a common method is to generate responses by grounding on external contexts given as input,…
Hexa: Self-Improving for Knowledge-Grounded Dialogue System
Daejin Jo, Daniel Wontae Nam, Gunsoo Han +4
A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e.g., web-search, memory retrieval) with modular approaches. However, data…