667 citations · 996 across the 20 of their papers we have counts for
15 papers · 1 filter
The Bidirectional Process Reward Model
Lingyin Zhang, Jun Gao, Xiaoxue Ren +1
Process Reward Models (PRMs), which assign fine-grained scores to intermediate reasoning steps within a solution trajectory, have emerged as a promising approach to enhance the rea…
SelfCP: Compressing Over-Limit Prompt via the Frozen Large Language Model Itself
Jun Gao, Ziqiang Cao, Wenjie Li
Long prompt leads to huge hardware costs when using transformer-based Large Language Models (LLMs). Unfortunately, many tasks, such as summarization, inevitably introduce long docu…
Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization
Shichao Sun, Ruifeng Yuan, Ziqiang Cao +2
Large language models (LLMs) have demonstrated the capacity to improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the init…
UniICL: An Efficient Unified Framework Unifying Compression, Selection, and Generation
Jun Gao, Qi Lv, Zili Wang +3
In-context learning (ICL) enhances the reasoning abilities of Large Language Models (LLMs) by prepending a few demonstrations. It motivates researchers to introduce more examples t…
CoUDA: Coherence Evaluation via Unified Data Augmentation
Dawei Zhu, Wenhao Wu, Yifan Song +3
Coherence evaluation aims to assess the organization and structure of a discourse, which remains challenging even in the era of large language models. Due to the scarcity of annota…
Personalized Large Language Model Assistant with Evolving Conditional Memory
Ruifeng Yuan, Shichao Sun, Yongqi Li +3
With the rapid development of large language models, AI assistants like ChatGPT have become increasingly integrated into people's works and lives but are limited in personalized se…