2 citations · 3 across the 11 of their papers we have counts for
11 papers
Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts
Yi-Chang Chen, Feng-Ting Liao, Da-shan Shiu +1
Modern reasoning language models generate dense, sequential chain-of-thought traces implicitly assuming that every token contributes and that steps must be consumed in order. We ch…
Revisiting the Shape Convention of Transformer Language Models
Feng-Ting Liao, Guan-Ting Yi, Tzu-Quan Lin +2
The architectural shape of dense Transformers has remained remarkably stable: narrow-wide-narrow feed-forward networks (FFNs) consume most non-embedding parameters. Motivated by th…
Rethinking the shape convention of an MLP
Meng-Hsi Chen, Yu-Ang Lee, Feng-Ting Liao +1
Multi-layer perceptrons (MLPs) conventionally follow a narrow-wide-narrow design where skip connections operate at the input/output dimensions while processing occurs in expanded h…
CCI4.0: A Bilingual Pretraining Dataset for Enhancing Reasoning in Large Language Models
Guang Liu, Liangdong Wang, Jijie Li +6
We introduce CCI4.0, a large-scale bilingual pre-training dataset engineered for superior data quality and diverse human-like reasoning trajectory. CCI4.0 occupies roughly TB…
Latent Flow Transformer
Yen-Chen Wu, Feng-Ting Liao, Meng-Hsi Chen +3
Transformers, the standard implementation for large language models (LLMs), typically consist of tens to hundreds of discrete layers. While more layers can lead to better performan…
Group Think: Multiple Concurrent Reasoning Agents Collaborating at Token Level Granularity
Chan-Jan Hsu, Davide Buffelli, Jamie McGowan +4
Recent advances in large language models (LLMs) have demonstrated the power of reasoning through self-generated chains of thought. Multiple reasoning agents can collaborate to rais…
RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented Dialogues
Tzu-Lin Kuo, Feng-Ting Liao, Mu-Wei Hsieh +3
In real-world applications with Large Language Models (LLMs), external retrieval mechanisms - such as Search-Augmented Generation (SAG), tool utilization, and Retrieval-Augmented G…
Let's Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Robust and Instruction-Aware ASR and OCR
Chan-Jan Hsu, Yi-Chang Chen, Feng-Ting Liao +4
We propose "Generative Fusion Decoding" (GFD), a novel shallow fusion framework designed to integrate large language models (LLMs) into cross-modal text recognition systems for aut…
Breeze-7B Technical Report
Chan-Jan Hsu, Chang-Le Liu, Feng-Ting Liao +3
Breeze-7B is an open-source language model based on Mistral-7B, designed to address the need for improved language comprehension and chatbot-oriented capabilities in Traditional Ch…
Advancing the Evaluation of Traditional Chinese Language Models: Towards a Comprehensive Benchmark Suite
Chan-Jan Hsu, Chang-Le Liu, Feng-Ting Liao +3
The evaluation of large language models is an essential task in the field of language understanding and generation. As language models continue to advance, the need for effective b…
Image generation with shortest path diffusion
Ayan Das, Stathi Fotiadis, Anil Batra +5
The field of image generation has made significant progress thanks to the introduction of Diffusion Models, which learn to progressively reverse a given image corruption. Recently,…