7 citations · 7 across the 9 of their papers we have counts for
9 papers
ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning
Juyong Jiang, Jiasi Shen, Sunghun Kim +3
While Large Language Models (LLMs) have revolutionized code generation, standard ``System 1'' approaches that generate solutions in a single forward pass often hit a performance ce…
What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say "I Don't Know"
Joosung Lee, Hwiyeol Jo, Donghyeon Ko +3
While large language models (LLMs) demonstrate strong capabilities across diverse user queries, they still suffer from hallucinations, often arising from knowledge misalignment bet…
OmniACBench: A Benchmark for Evaluating Context-Grounded Acoustic Control in Omni-Modal Models
Seunghee Kim, Bumkyu Park, Kyudan Jung +5
Most testbeds for omni-modal models assess multimodal understanding via textual outputs, leaving it unclear whether these models can properly speak their answers. To study this, we…
Enhancing Hallucination Detection via Future Context
Joosung Lee, Cheonbok Park, Hwiyeol Jo +3
Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. As users increasingly encounter such black-bo…
Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models
Cheonbok Park, Jeonghoon Kim, Joosung Lee +3
Reinforcement learning with verifiable reward (RLVR) has been instrumental in eliciting strong reasoning capabilities from large language models (LLMs) via long chains of thought (…
ReGUIDE: Data Efficient GUI Grounding via Spatial Reasoning and Search
Hyunseok Lee, Jeonghoon Kim, Beomjun Kim +7
Recent advances in Multimodal Large Language Models (MLLMs) have enabled autonomous agents to interact with computers via Graphical User Interfaces (GUIs), where accurately localiz…
Peri-LN: Revisiting Normalization Layer in the Transformer Architecture
Jeonghoon Kim, Byeongchan Lee, Cheonbok Park +7
Selecting a layer normalization (LN) strategy that stabilizes training and speeds convergence in Transformers remains difficult, even for today's large language models (LLM). We pr…
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices
Jung Hyun Lee, Jeonghoon Kim, June Yong Yang +4
With the commercialization of large language models (LLMs), weight-activation quantization has emerged to compress and accelerate LLMs, achieving high throughput while reducing inf…
HyperCLOVA X Technical Report
Kang Min Yoo, Jaegeun Han, Sookyo In +393
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…