6 citations · 14 across the 13 of their papers we have counts for
8 papers · 1 filter
Unleashing Implicit Rewards: Prefix-Value Learning for Distribution-Level Optimization
Shiping Gao, Hongzhan Chen, Xiaojun Quan +2
Process reward models (PRMs) provide fine-grained supervision for reasoning, but reliable PRMs often require step annotations or heavy verification pipelines, making them costly to…
TokenSeek: Memory Efficient Fine Tuning via Instance-Aware Token Ditching
Runjia Zeng, Qifan Wang, Qiang Guan +6
Fine tuning has been regarded as a de facto approach for adapting large language models (LLMs) to downstream tasks, but the high training memory consumption inherited from LLMs mak…
How Do Large Language Models Learn Concepts During Continual Pre-Training?
Barry Menglong Yao, Sha Li, Yunzhi Yao +4
Human beings primarily understand the world through concepts (e.g., dog), abstract mental representations that structure perception, reasoning, and learning. However, how large lan…
All You Need is One: Capsule Prompt Tuning with a Single Vector
Yiyang Liu, James C. Liang, Heng Fan +7
Prompt-based learning has emerged as a parameter-efficient finetuning (PEFT) approach to facilitate Large Language Model (LLM) adaptation to downstream tasks by conditioning genera…
Probabilistic Token Alignment for Large Language Model Fusion
Runjia Zeng, James Chenhao Liang, Cheng Han +8
Training large language models (LLMs) from scratch can yield models with unique functionalities and strengths, but it is costly and often leads to redundant capabilities. A more co…
Error-driven Data-efficient Large Multimodal Model Tuning
Barry Menglong Yao, Qifan Wang, Lifu Huang
Large Multimodal Models (LMMs) have demonstrated impressive performance across numerous academic benchmarks. However, fine-tuning still remains essential to achieve satisfactory pe…
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…
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training
Jaehyung Kim, Yuning Mao, Rui Hou +7
Fine-tuning pre-trained language models (LMs) has become the de facto standard in many NLP tasks. Nevertheless, fine-tuned LMs are still prone to robustness issues, such as adversa…