10 citations · 19 across the 17 of their papers we have counts for
5 papers · 1 filter
Prompt Compression for Large Language Models: A Survey
Zongqian Li, Yinhong Liu, Yixuan Su +1
Leveraging large language models (LLMs) for complex natural language tasks typically requires long-form prompts to convey detailed requirements and information, which results in in…
500xCompressor: Generalized Prompt Compression for Large Language Models
Zongqian Li, Yixuan Su, Nigel Collier
Prompt compression is crucial for enhancing inference speed, reducing costs, and improving user experience. However, current methods face challenges such as low compression ratios…
Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence
Yinhong Liu, Yixuan Su, Ehsan Shareghi +1
Recent large language models (LLMs) have shown remarkable performance in aligning generated text with user intentions across various tasks. When it comes to long-form text generati…
Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators
Yinhong Liu, Han Zhou, Zhijiang Guo +4
Large Language Models (LLMs) have demonstrated promising capabilities as automatic evaluators in assessing the quality of generated natural language. However, LLMs still exhibit bi…
TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles
Yinhong Liu, Yimai Fang, David Vandyke +1
In light of recent advances in large language models (LLMs), the expectations for the next generation of virtual assistants include enhanced naturalness and adaptability across div…