5 citations · 7 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
Dissecting Human and LLM Preferences
Junlong Li, Fan Zhou, Shichao Sun +3
As a relative quality comparison of model responses, human and Large Language Model (LLM) preferences serve as common alignment goals in model fine-tuning and criteria in evaluatio…
Reformatted Alignment
Run-Ze Fan, Xuefeng Li, Haoyang Zou +5
The quality of finetuning data is crucial for aligning large language models (LLMs) with human values. Current methods to improve data quality are either labor-intensive or prone t…
Extending LLMs' Context Window with 100 Samples
Yikai Zhang, Junlong Li, Pengfei Liu
Large Language Models (LLMs) are known to have limited extrapolation ability beyond their pre-trained context window, constraining their application in downstream tasks with length…
The Critique of Critique
Shichao Sun, Junlong Li, Weizhe Yuan +3
Critique, as a natural language description for assessing the quality of model-generated content, has played a vital role in the training, evaluation, and refinement of LLMs. Howev…
Generative Judge for Evaluating Alignment
Junlong Li, Shichao Sun, Weizhe Yuan +3
The rapid development of Large Language Models (LLMs) has substantially expanded the range of tasks they can address. In the field of Natural Language Processing (NLP), researchers…