Help me write a Poem - Instruction Tuning as a Vehicle for Collaborative Poetry Writing
By Tuhin Chakrabarty, Vishakh Padmakumar, Hengxing He. 2022
108 citations recorded by the source corpus.
Recent work in training large language models (LLMs) to follow natural language instructions has opened up exciting opportunities for natural language interface design. Building on the prior success of large language models in the realm of computer assisted creativity, in this work, we present CoPoet, a collaborative poetry writing system, with the goal of to study if LLM’s actually improve the quality of the generated content. In contrast to auto-completing a user’s text, CoPoet is controlled by user instructions that specify the attributes of the desired text, such as Write a sentence about ‘love’ or Write a sentence ending in ‘fly’. The core component of our system is a language model fine-tuned on a diverse collection of instructions for poetry writing. Our model is not only competitive to publicly available LLMs trained on instructions (InstructGPT), but also capable of satisfying unseen compositional instructions. A study with 15 qualified crowdworkers shows that users successfully write poems with CoPoet on diverse topics ranging from Monarchy to Climate change, which are preferred by third-party evaluators over poems written without the system.
Subfields: Artificial Intelligence, Computer Science Applications, Information Systems, Natural Language Processing, LLM Agents & Tool Use
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Hannaneh Hajishirzi
- Daniel Khashabi
- Swaroop Mishra
- Noah A. Smith
- Luke S. Zettlemoyer
- Yejin Choi
- Matt Gardner
- Mohit Bansal
In Computer Science Applications
In Information Systems
- Daniel Khashabi
- Hannaneh Hajishirzi
- Swaroop Mishra
- Yizhong Wang
- Noah A. Smith
- Mohit Bansal
- Matt Gardner
- Chitta Baral
In Natural Language Processing
- Hannaneh Hajishirzi
- Daniel Khashabi
- Swaroop Mishra
- Noah A. Smith
- Yejin Choi
- Luke S. Zettlemoyer
- Chitta Baral
- Yizhong Wang
In LLM Agents & Tool Use
Citations in the corpus
- Self-Instruct: Aligning Language Models with Self-Generated Instructions (2022, background): “A series of works have found evidence that vanilla language models can be effective at following general language instructions if tuned with annotated “instructional” data – datasets containing language instructional commands and their desired outcome based on human judgement (Weller et al., 2020; M”