Position Paper: Data-Centric AI in the Age of Large Language Models (2024)

Abstract: This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs). We start by making a key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs, and advocate that data-centric research should receive more attention from the community. We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization. In each scenario, we underscore the importance of data, highlight promising research directions, and articulate the potential impacts on the research community and, where applicable, the society as a whole. For instance, we advocate for a suite of data-centric benchmarks tailored to the scale and complexity of data for LLMs. These benchmarks can be used to develop new data curation methods and document research efforts and results, which can help promote openness and transparency in AI and LLM research.

## introduction the latest large language models (llms) @xcite @xcite @xcite @xcite are typically trained on extensive corpora of raw data scrapped from the internet and then fine-tuned on specialized domain data. these llms have demonstrated not only incredible performance on benchmarks @xcite , but also remarkable abilities to follow and execute human instructions @xcite , and to learn "in-context" @xcite from the contextual data given by the user along with the query. at the core of these impressive achievements, we identify that data, in different forms, scales, and usages, is a common denominator. however, the bulk of research to date has focused on modeling improvements, and little is known about how to best use data for the developmental stages (i.e., pretraining and fine-tuning) and the inferential stage (using llms for inference or generation). for pretraining, the exact composition of pretraining datasets used by many leading foundation models is proprietary @xcite @xcite , while data scrapped from the internet is often noisy and can pose legal and security risks @xcite @xcite . moreover, since pretraining large models is expensive (e.g., gpt-4 costs over $100 million to build @xcite ), it is prohibitively costly to evaluate different choices of pretraining data. these characteristics raise the difficulties of identifying the factors that underlie an effective pretraining dataset. then, for fine-tuning, compared to the array of modeling techniques @xcite , the methods for data curation are underexplored @xcite and most prior works adopt manual approaches @xcite @xcite which are difficult to generalize and costly to deploy at scale. it is yet unclear how to push the llms' limits beyond what is achievable solely by better modeling techniques. specifically, we propose to identify a generalizable and cost-effective approach to designing pretraining and fine-tuning datasets to complement the model-centric techniques. separately, for the inferential stage, there are model-centric efforts "optimizing the instructions" for llms to improve how they utilize the user-provided contextual data @xcite but only relatively limited data-centric research on improving the user-supplied contextual data itself, even though the llm's performance is shown to be sensitive to the contextual data's quality @xcite and ordering @xcite . we advocate for data-centric research that can turn the art of using data into science and unlock the next generation of more effective and compact llms. our position is framed within the following four scenarios of different interactions between llms and data; refer to fig. 1 for a diagrammatic overview. for each scenario, we highlight the unique characteristics and challenges, identify motivating use cases and promising research directions, and discuss potential impacts. we do not claim to be the first to propose these directions, but rather aim to underscore the importance of the data-centric perspective and its impacts. while our exposition is not exhaustive, we hope our "first cut" at a holistic viewpoint of data-centric research can generate more discussion and inspire innovation. benchmarks and curation for training data. the recent successes of llms such as chat-gpt (openai, 2023), palm 2 @xcite , and llama 2 @xcite , as well as vision-language models including clip @xcite , flamingo @xcite , stable diffusion @xcite and dall-e @xcite , are powered by large, heterogeneous datasets rather than solely by advanced modeling techniques. clip is trained on 400 million image-text pairs (roughly 300× greater than the size of imagenet @xcite ), in-structgpt is trained on thousands of user-supplied and diverse prompts @xcite , and llava's instruction dataset contains over 100 thousand image-text pairs @xcite . these examples underscore the critical role of better designed and curated training data in further advancing the capabilities of llms. however, the heterogeneity, scale, and proprietary nature @xcite of the training data for most of the currently best-performing llms significantly impede the progress in developing and training llms through curating better training data. to advance the research on data curation, we advocate for building towards rigorous data-centric benchmarks (sec. 2) on the foundation of existing efforts like datacomp @xcite . ## rigorous data-centric benchmarks there are increasing data-centric efforts on quantitatively understanding how the training data affects llms' performance via identifying and improving the scaling laws @xcite @xcite @xcite . however, the datasets that train the state-of-the-art llms are often proprietary and closed-source while public datasets do not seem to achieve comparable scaling behavior to their proprietary counterparts @xcite . moreover, even for public datasets like c4 @xcite or laion-2b @xcite , the critical factors underlying effective training datasets remain unclear. indeed, different training data compositions (i.e., proportions of different sources) can lead to vastly different properties of the trained clip models @xcite and language models @xcite , while data filtering and pruning can sometimes even outperform the standard power-law scaling @xcite @xcite . there are also promising results by sourcing for "clean" data @xcite or low-perplexity data @xcite . these observations inspire several questions: what factors (besides the scale) are important to a training dataset @xcite ? how do the data compositions affect the performance @xcite ? what is a principled methodology to reliably outperform the power-law scaling trends @xcite )? while the above works provide excellent starting points, comprehensively addressing these questions requires a series of well-documented results and a systematic approach to identifying and quantitatively analyzing the key underlying factors of llms' performance. by building on the foundations in @xcite which primarily target conventional ml, we advocate for rigorous data-centric benchmarks catering to llms' scale and complexity. we also identify directions (that leverage existing non-llm-specific techniques) for designing effective data curation methods. ## data attribution for copyright/intellectual property rights considerations, data attribution is primarily motivated by the need for credit attribution. for ensuring safe applications of llms, the goal of attribution is to trace (and then remove) the sources of potentially problematic outputs. notably, data attribution and unlearning are useful to both these use cases. since most of the training data for the popular llms is scraped from the internet, it is almost inevitable that the training data contains certain copyrighted data (e.g., writing, code, or even entire datasets). then, it is important to design techniques to mitigate potential copyright infringements, especially when the data owners or creators request takedowns. this process involves first correctly identifying the source through data attribution and then removing it via unlearning @xcite . for sources that lead to problematic outputs by the llms, we first identify sources through attribution and then remove (the effects of) the sources through unlearning @xcite . the challenge in the unlearning step is to ensure its effectiveness without compromising the performance of the llm @xcite , incurring prohibitive costs from iterative retraining @xcite or needing additional training data @xcite . ## knowledge transfer given the prohibitive costs of deploying fullfledged llms @xcite , and that most users may not need such powerful general-purpose llms, the cost-effective adaptations of llms to users' specialized tasks are more appealing. in many cases, the generalpurpose llm already has the necessary "knowledge" to perform the specialized task @xcite , which can be transferred to a more compact and specialized model. knowledge transfer can be performed by first distilling the knowledge from the llm as synthesized data, then instilled into the specialized model by training it on the synthesized data. since data synthesis is a niche setting arising from the generative capabilities of llms and its quality is key to effective knowledge transfers, we focus on data synthesis, specifically label and input syntheses. ## inference contextualization with data as in the two examples in sec. 1 on how data can be used to contextualize the inference process of 11901 llms @xcite , llms have the demonstrated remarkable ability to utilize information "in-context" @xcite where the context here is often in the form of a few example data points for demonstration or supplementary information @xcite . such unique and unseen abilities present exciting use cases of data as an "anchor" to establish the right context at inference and enable the users to make certain specifications with flexibility and ease. we illustrate such contextualization as follows: (i) if a user prompts the llm to generate a piece of writing while providing writings from shakespeare, then the llm's generated output can appear "shakespearean" even though the llm is not necessarily (extensively) trained on the writings from shakespeare. (ii) if a user asks the llm to solve a mathematical question while providing data containing similar questions and the reasoning steps, then the generated output can also contain reasoning steps, even though the llm might not have been explicitly trained to do so. ## conclusion and future outlook this position paper has outlined a data-centric approach towards ai research with a focus on large language models (llms). we highlight the multifaceted role of data in the different developmental (e.g., pretraining, fine-tuning) and inferential (e.g., data synthesis, inference contextualization) stages of llms. in particular, we have identified four scenarios centered around data: rigorous data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization with data. they each have unique challenges that require careful consideration, and present opportunities for innovation. the impacts are described within each scenario for concreteness and clarity, but they are certainly not restricted to each of the scenarios and can sometimes "cross over". for instance, while we have identified democratization of the llm technologies as an impact of sec. 4, it is also applicable to sec. 5, which has highlighted the practical viability of personalized usages of llms. similarly, these scenarios (and the research directions therein) should not be viewed in isolation because there are indeed relationships and connections between the components. for instance, to mitigate problematic outputs by llms, a holistic treatment comprising both ex-ante and ex-post data-centric methods can perhaps be most effective (e.g., a more targeted data curation method from sec. 2 paired with attribution and unlearning methods from sec. 3). this initial exploration into a data-centric ai research paradigm in the age of llms is necessarily non-exhaustive and intended to catalyze broader discussions, stimulate further inquiry, and spark innovation that will expand the current limits of llms and, more broadly, ai, and build toward deployment of such technologies that promote greater democratization.