06 - Open Problems

&

Wrapping up, Recap, Career Guidance. etc

Recap

06 - Open Problems

&

Recap, Career Guidance. etc

Do our models understand our tasks?

Hupkes, Dieuwke, et al. "A taxonomy and review of generalization research in NLP." Nature Machine Intelligence 5.10 (2023): 1161-1174.

01- Generalization

How much do models really generalize? 

01- Generalization

1.  Clever Inductive Biases
2. Common sense reasoning
3. Supervision + Alignment, using small models to supervise larger models

01- Generalization

What makes NLP systems work?

02 - Analysis & Interpretation

What's going on inside NNs? 

input sentence

Output

Black Box Model

02 - Analysis & Interpretation

Can we build interpretable, but performant models?

e.g Establishing bounds with Model Class Reliance:

This article proposes MCR as the upper and lower limit on how important a set of variables can be to any well-performing model in a class.

In this way, MCR provides a more comprehensive and robust measure of importance than traditional importance measures for a single model.

More reads: https://rssdss.design.blog/2020/03/31/all-models-are-wrong-but-some-are-completely-wrong/

02 - Analysis & Interpretation

Can understanding help find the next transformer?

02 - Analysis & Interpretation

• What can't be learned via language model pertaining? 
• How are our models affecting people and transferring power?
• What does deep learning struggle to do
• What do Neural Nets tell us about language?
• What will replace the transformer?

03 - Multilinguality

There are significant gaps between high and low resource languages

03 - Multilinguality

Can we remove language resource gaps?

04 - Evaluation and Comparison

Benchmarks and how we evaluate drive the progress of the field

Recent models "have outpaced the benchmarks to test for them" quickly reaching super-human performance on standard benchmarks such as SuperGLUE and SQuAD. Does this mean that we have solved natural language processing?  - Far From it

- Seb Ruder

04 - Evaluation and Comparison

How do we evaluate things like interpretability?

05 - What is the next transformer? 

06 - Working in the real world

How do we make NLP systems work in the real world, on real problems?

05 - Working in the real world 

1 . Bio/Clinical NLP

05 - Working in the real world 

2 . Legal Domain

05 - Working in the real world 

3 . Scientific Communication

05 - Working in the real world 

3 . Education

07 - Fairness, Bias, Responsible NLP

.. and many others

Wrapping Up

• Key ideas - Distributed representations
• Major opportunities - NLP systems work in ways that support real-world applications
• Many open questions