Ireland

Large NLP models have recently shown impressive performance in language understanding tasks, typically evaluated by fine-tuning tasks. Alternatively, probing has received increasing attention as being a lightweight method for interpreting the intrinsic mechanisms of large NLP models. In probing, post-hoc classifiers are trained on &quot;out-of-domain&quot; datasets that diagnose specific abilities. While probing the language models has led to insightful findings, they appear disjointed from the development of models. This paper explores the utility of probing deep NLP models to extract a proxy signal widely used in model developments, the fine-tuning performance. We find that it is possible to use the accuracies of only three probing results to predict the fine-tuning performance with errors 40%- 80% smaller than baselines. We further show the possibility of incorporating specialized probing datasets into developing deep NLP models.

ACL 2022

Predicting Fine-tuning Performance with Probing

performance prediction

probing

fine-tuning

# 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)

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). 

Large NLP models have recently shown impressive performance in language understanding tasks, typically evaluated by fine-tuning tasks. Alternatively, probing has received increasing attention as being a lightweight method for interpreting the intrinsic mechanisms of large NLP models. In probing, post-hoc classifiers are trained on "out-of-domain" datasets that diagnose specific abilities. While probing the language models has led to insightful findings, they appear disjointed from the development of models. This paper explores the utility of probing deep NLP models to extract a proxy signal widely used in model developments, the fine-tuning performance. We find that it is possible to use the accuracies of only three probing results to predict the fine-tuning performance with errors 40%- 80% smaller than baselines. We further show the possibility of incorporating specialized probing datasets into developing deep NLP models.

workshop paper

Panel on challenges for scaling large language models

Scaling Large Language Models

The panelists discuss why we need large-scale, open collaboration in machine learning research and share their thoughts, experience, challenges and opportunities around collaborative approaches, projects and organziations including GEM, BigBench, Masakhane, EleutherAI and ML Collective.

Large-scale collaborations

Mind the Gaps and Normal Accidents

Human Values in Recommender Systems: a Multidisciplinary Discussion

Problematic Information on Social Media Platforms: Understanding and Countering

Datasets, Learner Annotation, and Bias in Multiple Languages

Supporting professional fact-checking: how can NLP/AI help?

Model robustness and spurious correlations have received increasing attention in the NLP community, both in methods and evaluation. The term “spurious correlation” has been abused to refer to any undesirable shortcuts learned by the model, judged by domain knowledge. However, in NLP, many features (e.g. word overlap and negation) are not spurious in the sense that the background is a spurious feature to classifying the object in the image. They carry important information that’s needed to make predictions by humans. In this talk, we argue that it is more productive to consider features from the aspects of necessity and sufficiency, and discuss the implications of this categorization in representation, learning, and evaluation.

What We Talk About When We Talk About Spurious Correlations

This presentation will frame the increasing monopolistic power of Big Tech in the field of NLP as an instance of data colonialism. The concept of data colonialism explains the emergence of a new social order based not on the extraction of natural resources or labor, but on the appropriation of human life —including language— through data. This new type of colonialism is already resulting in a highly unequal social arrangement that is deeply incompatible with human freedom and autonomy. And as research in NLP becomes the exclusive domain of powerful corporations, language itself is becoming a zone of colonial extractivism. Resisting this order will require strategies that decolonial thinking has foregrounded for centuries.

Data Colonialism and Language Technology

Multiple studies have shown that Transformers are remarkably robust, and retain performance advantages under massive pruning and other disruptions. Contrary to this received wisdom, in this talk, we will show that pre-trained Transformer encoders are surprisingly fragile to the removal of a very small number of features in the layer outputs. We will also consider the performance of several pre-trained Transformer families and pre-training objectives on a number of downstream probing tasks, and discuss some important challenges to these architectures.

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