5 papers
Speech World Model: Causal State-Action Planning with Explicit Reasoning for Speech
Xuanru Zhou, Jiachen Lian, Henry Hong +2
Current speech-language models (SLMs) typically use a cascade of speech encoder and large language model, treating speech understanding as a single black box. They analyze the cont…
Dataset Construction for Training LLM to Learn Analog Circuit Knowledge
Zihao Chen, Ji Zhuang, Jinyi Shen +11
This paper constructs a textual dataset for training large language models (LLMs) to learn analog circuit knowledge and customizes LLM training techniques. For dataset construction…
LLM Active Alignment: A Nash Equilibrium Perspective
Tonghan Wang, Yuqi Pan, Xinyi Yang +3
We develop a game-theoretic framework for predicting and steering the behavior of populations of large language models (LLMs) through Nash equilibrium (NE) analysis. To avoid the i…
ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement
Xiangyu Peng, Congying Xia, Xinyi Yang +3
Post-training Large Language Models (LLMs) with explicit reasoning trajectories can enhance their reasoning abilities. However, acquiring such high-quality trajectory data typicall…
DENIAHL: In-Context Features Influence LLM Needle-In-A-Haystack Abilities
Hui Dai, Dan Pechi, Xinyi Yang +2
The Needle-in-a-haystack (NIAH) test is a general task used to assess language models' (LMs') abilities to recall particular information from long input context. This framework how…