Defining a New NLP Playground (2023)

Abstract: The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field’s 80 year history. This has resulted in concerns that the field will become homogenized and resource-intensive. This new status quo has put many academic researchers, especially PhD students, at a disadvantage. This paper aims to define a new NLP playground by proposing 20+ PhD-dissertation-worthy research directions, covering theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications.

## introduction it is the best of times. it is the worst of times. we are living in an incredibly exciting yet strange era of natural language processing (nlp) research due to the recent advancements of large language models (llms) on various data modalities, from natural language @xcite and programming language @xcite to vision @xcite @xcite and molecules @xcite @xcite . at the core, llms produce text sequences word-by-word by computing conditional probability based on context. at a sufficiently large scale, they can answer questions, generate arguments, write poetry, impersonate characters, negotiate contracts and achieve competitive results across a wide variety of standard nlp tasks including entity typing, sentiment analysis, and textual entailment, showcasing "emergent behavior" such as in-context learning @xcite . however, this "moment of breakthrough" received a polarized response in the nlp research community: while some welcomed the progress, others felt lost. why is nlp so vulnerable to a single advancement? in retrospect, when nlp adopted the machine learning paradigm in the early 1990s it started along a journey that led to increased homogeneity. the dominant methodology became: (1) identify a challenge problem or task; (2) create a dataset of desired input-output instances; (3) select or define one or more evaluation metrics; and (4) develop, apply, and refine machine learning models and algorithms to improve performance. if a challenge did not support the creation of a dataset (e.g., text styles of people in different professions) or metric (e.g., summaries of novels or movies), or worse yet if it was not amenable to a machine learning solution, then mainstream nlp simply did not address it. for a long time, nlg was in this position because its starting point -semantic representations-were neither standardized, nor easy to produce at scale, nor amenable to direct evaluation. no dataset, no metric -little attention. yet multi-sentence nlg starting with deep semantic input, and with output tailored to different audiences, is arguably the most complex task in nlp, since it involves so many aspects of linguistic communication together. as such, it surely deserved the concentrated effort that nlp has bestowed on mt, speech recognition, qa, and other major challenges in the past. suddenly, within the space of a few months, the landscape changed. nlp encountered an engine that seemingly could do everything the field had worked on for decades. many subtasks in nlp seemed to become irrelevant overnight: which grammar formalism to parse into? which rhetorical structure and focus control model for multisentence coherence? which neural architecture is optimal for information extraction or summarization? none of that matters if the magical engine can do the entire end-to-end language-to-language task seamlessly @xcite . dozens of ph.d. theses lost their point, because their point was a small step in the process that no longer seemed needed. the dominant paradigm is also challenged: instead of setting up benchmarks and then developing models accordingly, people started discovering new abilities of such models @xcite (who knew that llms could draw unicorns using tikz?). an important constraint is the practicality of the goal. this newer generation of llms is beyond the practical reach of all but a small number of nlp researchers. unless one of the organizations building llms provides free access for research -an unlikely occurrence given the estimated six-figure monthly expense to run one-or a procedure is developed to construct university-sized ones cheaply, the academic nlp community will have to be quite creative in identifying things that either generative llms cannot do in principle or applications that can be built without re-training them and at the same time are important and doable in practice. inspired by the efforts of a group of phd students @xcite , we believe it would be a valuable exercise to define new research roadmaps. we believe that while llms seemingly close research avenues, they also open up new ones. current llms remain somewhat monolithic, expensive, amnesic, delusional, uncreative, static, assertive, stubborn, and biased black boxes. they still have a surprising deficiency (near-random performance) in acquiring certain types of knowledge @xcite , knowledge reasoning and prediction. in this paper, we aim to define a new nlp playground by proposing a wide range of phd-dissertation-worthy research directions to democratize nlp research again. in particular, we cover observations and suggestions along the perspectives of llm theory (section 2), challenging new tasks (section 3), important but understudied learning paradigms (section 4), proper evaluation (section 5), and interdisciplinary applications (section 6). ## theoretical analysis of llms there is a growing necessity to open the black box of machine learning models through theoretical analysis. in this section, we advocate for both mathematical (by mathematical analysis) and experimental (inducing rules and laws such as ghorbani et al. (2021 ## evaluation as models become increasingly powerful and multi-purpose, their evaluation has become a growing bottleneck for advancing nlp. we first discuss the question of "what should be evaluated" followed by "how should we measure performance." ## nlp+x interdisciplinary applications post-hoc debiasing and improving the social awareness of pretrained llms are important to this end. though modern approaches have made great advances in democratizing llm training, most builders don't have a need to pretrain their own llms, opting to, at most, fine-tune them. rather than hope that an llm is unbiased after pretraining, many researchers have discussed the utility in having a separate general debiasing step to account for any unintended associations stemming from pretraining @xcite @xcite . relatively less explored is the complementary requirement of augmenting llms with the awareness and ability to abide by sociocultural norms. the crux of the problem is training the model to recognize what behaviors in its training data are the results of sociocultural norms, discover why and when those norms should be followed, and how those norms can be followed (i.e., is it only in a specific way or is this a behavior that can be generalized across situations?). another important direction is personalization based on the user, particularly for chatbots. llms have an amazing ability to multiplex behavior based on the language context provided in the prompt (section 2.1), but they do not have the ability to account for the audience apart from what's inferred from text. this poses a problem for personalization because the same context or conversation can have differing levels of appropriateness depending on the audience (e.g., something that one finds relatively harmless may be incredibly offensive to someone else). thus, we must improve llms' ability to infer the personal norms and appropriate behaviors in each individual context independently and act accordingly. this may, in part, involve bridging the gap between distant users who share similar beliefs to decode latent representations @xcite . in parallel, we can also provide users with multi-dimensional controls for generation @xcite , including their sentiment, political stance, and moral values, so that they can directly influence the model's language usage. ## what we need our overall aim is to combat both the stultification of nlp as a mere evaluation optimization endeavor and to dispel fears that llms and generative ai will shut down the field. as an old saying goes, frequent moves make a tree die but a person prosperous. just as nlp researchers in the 1980s had to learn about machine learning and then embrace it as a core technique in the field, so we now must explore and embrace llms and their capabilities. machine learning did not 'solve' the challenges of nlp: it did not produce an engine that could learn languages, translate, answer questions, create poetry, and do all the things a child can do. some people claim that llms can do all this, and more. but we are in the first flush of engagement, and have not yet have time to discover all their shortcomings. central is the challenge of scale. no child needs to read or hear more than half the internet's english text in order to use language. what reasoning and sensory capabilities do people have that llms lack? how can nlp research evolve to model and encompass those? we urgently need global infrastructures to dramatically scale up computing resources, because the open-source models still cannot achieve performance comparable to gpt variants @xcite . but we also urgently need deeper thinking about the foundational conceptual models driving our field. during this unique period when nlp researchers feel uncertain regarding which research problems to pursue, we as a community need a collective effort to systematically change and refine our paper review system and academic success measurements, in order to establish a more inclusive research environment and encourage researchers (particularly those in junior positions) to explore longterm, high-risk topics that are crucial for the entire field. the new challenges also require us to be more open-minded to close collaboration with researchers from other fields, including social science, natural science, computer vision, knowledge representation and reasoning, and human-computer interaction.