24 citations · 30 across the 30 of their papers we have counts for
10 papers · 1 filter
Dependency-Aware Revocable Decoding for Efficient Diffusion Large Language Model Inference
Wooje Park, Insu Lee, Minyoung Noh +4
Diffusion large language models (dLLMs) offer a promising alternative to autoregressive generation by decoding multiple tokens in parallel through iterative denoising. However, inc…
Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device
Juntae Lee, Jihwan Bang, Seunghan Yang +2
Retrieval-augmented generation (RAG) with large language models (LLMs) is especially valuable in specialized domains, where precision is critical. To more specialize the LLMs into…
Preserving Pre-trained Representation Space: On Effectiveness of Prefix-tuning for Large Multi-modal Models
Donghoon Kim, Gusang Lee, Kyuhong Shim +1
Recently, we have observed that Large Multi-modal Models (LMMs) are revolutionizing the way machines interact with the world, unlocking new possibilities across various multi-modal…
InfiniPot: Infinite Context Processing on Memory-Constrained LLMs
Minsoo Kim, Kyuhong Shim, Jungwook Choi +1
Handling long input contexts remains a significant challenge for Large Language Models (LLMs), particularly in resource-constrained environments such as mobile devices. Our work ai…
Crayon: Customized On-Device LLM via Instant Adapter Blending and Edge-Server Hybrid Inference
Jihwan Bang, Juntae Lee, Kyuhong Shim +2
The customization of large language models (LLMs) for user-specified tasks gets important. However, maintaining all the customized LLMs on cloud servers incurs substantial memory a…
Knowledge Distillation from Non-streaming to Streaming ASR Encoder using Auxiliary Non-streaming Layer
Kyuhong Shim, Jinkyu Lee, Simyung Chang +1
Streaming automatic speech recognition (ASR) models are restricted from accessing future context, which results in worse performance compared to the non-streaming models. To improv…
Teacher Intervention: Improving Convergence of Quantization Aware Training for Ultra-Low Precision Transformers
Minsoo Kim, Kyuhong Shim, Seongmin Park +2
Pre-trained Transformer models such as BERT have shown great success in a wide range of applications, but at the cost of substantial increases in model complexity. Quantization-awa…
Korean Tokenization for Beam Search Rescoring in Speech Recognition
Kyuhong Shim, Hyewon Bae, Wonyong Sung
The performance of automatic speech recognition (ASR) models can be greatly improved by proper beam-search decoding with external language model (LM). There has been an increasing…
Similarity and Content-based Phonetic Self Attention for Speech Recognition
Kyuhong Shim, Wonyong Sung
Transformer-based speech recognition models have achieved great success due to the self-attention (SA) mechanism that utilizes every frame in the feature extraction process. Especi…
Layer-wise Pruning of Transformer Attention Heads for Efficient Language Modeling
Kyuhong Shim, Iksoo Choi, Wonyong Sung +1
While Transformer-based models have shown impressive language modeling performance, the large computation cost is often prohibitive for practical use. Attention head pruning, which…