163 citations · 397 across the 15 of their papers we have counts for
8 papers · 2 filters
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Zhihan Zhang, Shuohang Wang, Wenhao Yu +6
Large language models (LLMs) can perform a wide range of tasks by following natural language instructions, without the necessity of task-specific fine-tuning. Unfortunately, the pe…
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Siru Ouyang, Shuohang Wang, Yang Liu +7
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks. However, it remains unclear whether the exist…
LMGQS: A Large-scale Dataset for Query-focused Summarization
Ruochen Xu, Song Wang, Yang Liu +5
Query-focused summarization (QFS) aims to extract or generate a summary of an input document that directly answers or is relevant to a given query. The lack of large-scale datasets…
InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT
Yichong Xu, Ruochen Xu, Dan Iter +4
While large models such as GPT-3 demonstrate exceptional performance in zeroshot and fewshot summarization tasks, their extensive serving and fine-tuning costs hinder their utiliza…
In-Context Demonstration Selection with Cross Entropy Difference
Dan Iter, Reid Pryzant, Ruochen Xu +4
Large language models (LLMs) can use in-context demonstrations to improve performance on zero-shot tasks. However, selecting the best in-context examples is challenging because mod…
Automatic Prompt Optimization with "Gradient Descent" and Beam Search
Reid Pryzant, Dan Iter, Jerry Li +3
Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous…