26 citations · 59 across the 24 of their papers we have counts for
6 papers · 1 filter
Archer: Adaptive Reuse of Cached Hidden States for Efficient Rollback in Diffusion Language Models
Xuning He, Zinan Sheng, Qingyuan Shi +5
Diffusion language models (DLMs) can revise earlier predictions as context evolves, but rollback requires costly full-sequence recomputation. Conventional key-value (KV) caching as…
Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy
Yihong Dong, Jianha Xiao, Xue Jiang +7
The formal reasoning capabilities of LLMs are crucial for advancing automated software engineering. However, existing benchmarks for LLMs lack systematic evaluation based on comput…
Rethinking Repetition Problems of LLMs in Code Generation
Yihong Dong, Yuchen Liu, Xue Jiang +2
With the advent of neural language models, the performance of code generation has been significantly boosted. However, the problem of repetitions during the generation process cont…
Reasoning is Periodicity? Improving Large Language Models Through Effective Periodicity Modeling
Yihong Dong, Ge Li, Xue Jiang +8
Periodicity, as one of the most important basic characteristics, lays the foundation for facilitating structured knowledge acquisition and systematic cognitive processes within hum…
Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models
Yihong Dong, Xue Jiang, Huanyu Liu +4
Recent statements about the impressive capabilities of large language models (LLMs) are usually supported by evaluating on open-access benchmarks. Considering the vast size and wid…
PACE: Improving Prompt with Actor-Critic Editing for Large Language Model
Yihong Dong, Kangcheng Luo, Xue Jiang +2
Large language models (LLMs) have showcased remarkable potential across various tasks by conditioning on prompts. However, the quality of different human-written prompts leads to s…