Coreference resolution – which is a crucial task for understanding discourse and language at large – has yet to witness widespread benefits from large language models (LLMs). Moreover, coreference resolution systems largely rely on supervised labels, which are highly ex- pensive and difficult to annotate, thus making it ripe for prompt engineering. In this paper, we introduce a QA-based prompt-engineering method and discern generative, pre-trained LLMs’ abilities and limitations toward the task of coreference resolution. Our experiments show that GPT-2 and GPT-Neo can return valid answers, but that their capabilities to identify coreferent mentions are limited and prompt-sensitive, leading to inconsistent results.

What GPT Knows About Who is Who

**Organizers:** Shabnam Tafresh, João Sedoc, Anna Rogers, Aleksandr Drozd, Anna Rumshisky
 **Description:** Publication of negative results is difficult in most fields, but in NLP the problem is exacerbated by the near-universal
focus on improvements in benchmarks. This situation implicitly discourages hypothesis-driven research, and it turns
creation and fine-tuning of NLP models into art rather than science. Furthermore, it increases the time, effort, and carbon
emissions spent on developing and tuning models, as the researchers have no opportunity to learn what has already been
tried and failed. This workshop invites both practical and theoretical unexpected or negative results that have important
implications for future research, highlight methodological issues with existing approaches, and/or point out pervasive
misunderstandings or bad practices. In particular, the most successful NLP models currently rely on different kinds of
pretrained meaning representations (from word embeddings to Transformer-based models like BERT). To complement all
the success stories, it would be insightful to see where and possibly why they fail. Any NLP tasks are welcome: sequence
labeling, question answering, inference, dialogue, machine translation - you name it.
 
**Please visit our [website](https://insights-workshop.github.io/index)**

(Negative Results) The Third Workshop on Insights from Negative Results in NLP

workshop paper

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

Xiaohan Yang

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What GPT Knows About Who is Who

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