2 citations · 4 across the 19 of their papers we have counts for
19 papers
Mechanistic Interpretability of Structure-Aware Numerical Reasoning in LLaMA 3.1 8B
Rahul Chowdhury, Timothy A Rupprecht, Senhao Cao +5
Recent work has shown that large language models (LLMs) exhibit strong numerical sequence modeling capabilities and show promise in time-series prediction. While LLMs display in-co…
Dynamic Rollout Editing for Reducing Overthinking in RL-Trained Reasoning Models
Zihao Wei, Wenjie Shi, Liang Pang +8
Long-form chain-of-thought reasoning can improve LLM performance on complex tasks, but models often continue generating unnecessary reasoning after a correct answer has emerged. We…
Teacher-Guided Policy Optimization for On-Policy Reasoning Distillation under Large Policy Divergence
Xinyu Liu, Kechen Jiao, Chunyang Xiao +10
On-policy distillation (OPD) has become a promising paradigm for reasoning-oriented post-training of large language models (LLMs), especially when combined with reinforcement learn…
IIET: Efficient Numerical Transformer via Implicit Iterative Euler Method
Xinyu Liu, Bei Li, Jiahao Liu +6
High-order numerical methods enhance Transformer performance in tasks like NLP and CV, but introduce a performance-efficiency trade-off due to increased computational overhead. Our…
TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making
Kechen Jiao, Zhirui Fang, Jiahao Liu +9
Using effective generalization capabilities of vision language models (VLMs) in context-specific dynamic tasks for embodied artificial intelligence remains a significant challenge.…
The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis
Zihao Wei, Liang Pang, Jiahao Liu +7
Test-time scaling via explicit reasoning trajectories significantly boosts large language model (LLM) performance but often triggers overthinking. To explore this, we analyze reaso…
Libra: Assessing and Improving Reward Model by Learning to Think
Meng Zhou, Bei Li, Jiahao Liu +5
Reinforcement learning (RL) has significantly improved the reasoning ability of large language models. However, current reward models underperform in challenging reasoning scenario…
Dynamic Fisher-weighted Model Merging via Bayesian Optimization
Sanwoo Lee, Jiahao Liu, Qifan Wang +3
The fine-tuning of pre-trained language models has resulted in the widespread availability of task-specific models. Model merging offers an efficient way to create multi-task model…
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
Bei Li, Tong Zheng, Rui Wang +8
Residual networks, as discrete approximations of Ordinary Differential Equations (ODEs), have inspired significant advancements in neural network design, including multistep method…
FIRP: Faster LLM inference via future intermediate representation prediction
Pengfei Wu, Jiahao Liu, Zhuocheng Gong +5
Recent advancements in Large Language Models (LLMs) have shown remarkable performance across a wide range of tasks. Despite this, the auto-regressive nature of LLM decoding, which…
ReMamba: Equip Mamba with Effective Long-Sequence Modeling
Danlong Yuan, Jiahao Liu, Bei Li +4
While the Mamba architecture demonstrates superior inference efficiency and competitive performance on short-context natural language processing (NLP) tasks, empirical evidence sug…
Graph-Structured Speculative Decoding
Zhuocheng Gong, Jiahao Liu, Ziyue Wang +5
Speculative decoding has emerged as a promising technique to accelerate the inference of Large Language Models (LLMs) by employing a small language model to draft a hypothesis sequ…
EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction
Li Yang, Qifan Wang, Jianfeng Chi +7
Product attribute value extraction involves identifying the specific values associated with various attributes from a product profile. While existing methods often prioritize the d…
Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism
Jiahao Liu, Qifan Wang, Jingang Wang +1
The recent advancements in large language models (LLMs) have been extraordinary, yet the escalating inference costs associated with them present challenges in real-world applicatio…
Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration
Pengfei Wu, Jiahao Liu, Zhuocheng Gong +5
Large language models (LLMs) have recently shown remarkable performance across a wide range of tasks. However, the substantial number of parameters in LLMs contributes to significa…
What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation
Zhuocheng Gong, Jiahao Liu, Jingang Wang +3
Quantization has emerged as a promising technique for improving the memory and computational efficiency of large language models (LLMs). Though the trade-off between performance an…
C-ICL: Contrastive In-context Learning for Information Extraction
Ying Mo, Jiahao Liu, Jian Yang +4
There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on task…
Improving Input-label Mapping with Demonstration Replay for In-context Learning
Zhuocheng Gong, Jiahao Liu, Qifan Wang +4
In-context learning (ICL) is an emerging capability of large autoregressive language models where a few input-label demonstrations are appended to the input to enhance the model's…
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression
Jiduan Liu, Jiahao Liu, Qifan Wang +5
Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these mode…