Prompting has shown impressive success in enabling large pre-trained language models (LMs) to perform diverse NLP tasks, especially with only few downstream data. Automatically finding the optimal prompt for each task, however, is challenging. Most existing work resorts to tuning *soft* prompts (e.g., embeddings) which fall short of interpretability, reusability across LMs, and applicability when gradients are not accessible. *Discrete* prompts, on the other hand, are difficult to optimize, and are often created by "enumeration (e.g., paraphrasing)-then-selection" heuristics that do not explore the prompt space systematically. This paper proposes RLPrompt, an efficient discrete prompt optimization approach with reinforcement learning (RL). RLPrompt formulates a parameter-efficient policy network that generates the optimized discrete prompt after training with reward. To harness the complex and stochastic reward signals from the large LM environment, we incorporate effective reward stabilization that substantially enhances training efficiency. RLPrompt is flexibly applicable to different types of LMs, such as masked (e.g., BERT) and left-to-right models (e.g., GPTs), for both classification and generation tasks. Experiments on few-shot classification and unsupervised text style transfer show superior performance over a wide range of existing fine-tuning or prompting methods. Interestingly, the resulting optimized prompts are often ungrammatical gibberish text; and surprisingly, those gibberish prompts are transferrable between different LMs to retain significant performance, indicating that LM prompting may not follow human language patterns.

RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Evaluating natural language generation is difficult. We propose a general framework that helps solve this difficulty by unifying the evaluation with a common central operation. Our uniformly designed metrics outperform state-of-the-art metrics specially designed for diverse tasks such as summarization, style transfer, and knowledge-grounded dialog.

Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation

**Topics:** Commonsense Reasoning
 Dialogue and Interactive Systems
 Discourse and Pragmatics
 Efficient Methods for NLP
 Ethics
 Information Extraction
 Information Retrieval and Text Mining
 Interpretability, Interactivity and Analysis of Models for NLP
 Language Modeling and Analysis of Language Models
 Linguistic Theories, Cognitive Modeling and Psycholinguistics
 Machine Learning for NLP
 Machine Learning for NLP (CL)
 Machine Translation
 Multilinguality

Poster Session 1

poster

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
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EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

[![](https://assets.underline.io/uploads/markdown_image/1/image/26c36e70b5f083b72ae632cbdcc2b5b1.png)](https://us06web.zoom.us/rec/play/jnnjarGDmiQJRrCaHkOGb1I42O5csAJ1LpkxhdEx0yyKShWdDbydaLcEXGgp1hDxfAEULNnaSc3WXGUX.pdpXB3VxNP0wugam?autoplay=true&startTime=1670428143000) 

[![](https://assets.underline.io/uploads/markdown_image/1/image/7e41b6370eefd82b0bf5eb67e35a574b.png)](https://us06web.zoom.us/rec/play/4hetiYPKWio8m-ge5MbRPazwzlU-yH34oWv66G4udQ26iuVuMfqHa-eVqxaWF5plloGJwODhFNHYtY5a.HQSebYGq6Va0PqaI?autoplay=true&startTime=1670360978000) 

**Chair:** Jekaterina Novikova **Organizers:** Antoine Bosselut, Khyathi Chandu, Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, Yacine Jernite, Jekaterina Novikova and Laura Perez-Beltrachini Please visit our [**website**](https://gem-benchmark.com/workshop) for more information and detailed schedule.

**RocketChat channel for this workshop:** https://emnlp2022.rocket.chat/channel/workshop-5-gem

Mingkai Deng

2

7

SHORT BIO

Presentations

RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation

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