3 citations · 3 across the 13 of their papers we have counts for
14 papers
Belief-Trajectory Energy: Measuring the Path to a Prediction
Jiahao Ying, Wei Tang, Boxian Ai +7
Large language models (LLMs) progressively revise their predictions across Transformer layers, yet we typically observe only the final output, discarding the trajectory through whi…
OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents
Jiahao Ying, Boxian Ai, Wei Tang +2
Skills, i.e., structured workflow instructions distilled for large language models (LLMs), are becoming an increasingly important mechanism for improving agent performance on real-…
Beyond Benchmarks: Understanding Mixture-of-Experts Models through Internal Mechanisms
Jiahao Ying, Mingbao Lin, Qianru Sun +1
Mixture-of-Experts (MoE) architectures have emerged as a promising direction, offering efficiency and scalability by activating only a subset of parameters during inference. Howeve…
EffiEval: Efficient and Generalizable Model Evaluation via Capability Coverage Maximization
Yaoning Wang, Jiahao Ying, Yixin Cao +2
The rapid advancement of large language models (LLMs) and the development of increasingly large and diverse evaluation benchmarks have introduced substantial computational challeng…
Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric
Yixin Cao, Jiahao Ying, Yaoning Wang +3
Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications, yet current evaluation methods struggle to keep pace with their rapid deve…
FRABench and UFEval: Unified Fine-grained Evaluation with Task and Aspect Generalization
Shibo Hong, Jiahao Ying, Haiyuan Liang +4
Evaluating open-ended outputs of Multimodal Large Language Models has become a bottleneck as model capabilities, task diversity, and modality rapidly expand. Existing ``MLLM-as-a-J…
Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks
Yixin Cao, Shibo Hong, Xinze Li +24
Large Language Models (LLMs) are advancing at an amazing speed and have become indispensable across academia, industry, and daily applications. To keep pace with the status quo, th…
EvoWiki: Evaluating LLMs on Evolving Knowledge
Wei Tang, Yixin Cao, Yang Deng +8
Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment. However, existing benchmarks…
Diagnosing and Remedying Knowledge Deficiencies in LLMs via Label-free Curricular Meaningful Learning
Kai Xiong, Xiao Ding, Li Du +4
Large Language Models (LLMs) are versatile and demonstrate impressive generalization ability by mining and learning information from extensive unlabeled text. However, they still e…
LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement
Jiahao Ying, Mingbao Lin, Yixin Cao +5
This paper introduces the innovative "LLMs-as-Instructors" framework, which leverages the advanced Large Language Models (LLMs) to autonomously enhance the training of smaller targ…
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential
Wei Tang, Yixin Cao, Jiahao Ying +4
Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models (LLMs). Targeting its bottleneck of retriever performance,…
QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism
Bo Wang, Heyan Huang, Yixin Cao +3
While large language models (LLMs) have made notable advancements in natural language processing, they continue to struggle with processing extensive text. Memory mechanism offers…
Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models
Jiahao Ying, Yixin Cao, Yushi Bai +7
Large language models (LLMs) have achieved impressive performance across various natural language benchmarks, prompting a continual need to curate more difficult datasets for large…
Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts
Jiahao Ying, Yixin Cao, Kai Xiong +3
This study investigates the behaviors of Large Language Models (LLMs) when faced with conflicting prompts versus their internal memory. This will not only help to understand LLMs'…