arXiv subjects

Jacob Morrison

Publications and source records attributed to Jacob Morrison.

22 records · Page 2Linked to original sources

arXiv2024

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open models are often released without accompanying training data or recipes to reproduce them. As a result, it is challenging to conduct and advance scientific research on language modeling, such as understanding how training data impacts model capabilities and limitations. To facilitate scientific research on language model pretraining, we curate and release Dolma, a three-trillion-token English corpus, built from a diverse mixture of web content, scientific papers, code, public-domain books, social media, and encyclopedic materials. We extensively document Dolma, including its design principles, details about its construction, and a summary of its contents. We present analyses and experimental results on intermediate states of Dolma to share what we have learned about important data curation practices. Finally, we open-source our data curation toolkit to enable reproduction of our work as well as support further research in large-scale data curation.

cs.CL↗

arXiv2024

A Legal Risk Taxonomy for Generative Artificial Intelligence

For the first time, this paper presents a taxonomy of legal risks associated with generative AI (GenAI) by breaking down complex legal concepts to provide a common understanding of potential legal challenges for developing and deploying GenAI models. The methodology is based on (1) examining the legal claims that have been filed in existing lawsuits and (2) evaluating the reasonably foreseeable legal claims that may be filed in future lawsuits. First, we identified 29 lawsuits against prominent GenAI entities and tallied the claims of each lawsuit. From there, we identified seven claims that are cited at least four times across these lawsuits as the most likely claims for future GenAI lawsuits. For each of these seven claims, we describe the elements of the claim (what the plaintiff must prove to prevail) and provide an example of how it may apply to GenAI. Next, we identified 30 other potential claims that we consider to be more speculative, because they have been included in fewer than four lawsuits or have yet to be filed. We further separated those 30 claims into 19 that are most likely to be made in relation to pre-deployment of GenAI models and 11 that are more likely to be made in connection with post-deployment of GenAI models since the legal risks will vary between entities that create versus deploy them. For each of these claims, we describe the elements of the claim and the potential remedies that plaintiffs may seek to help entities determine their legal risks in developing or deploying GenAI. Lastly, we close the paper by noting the novelty of GenAI technology and propose some applications for the paper's taxonomy in driving further research.

cs.CY↗

arXiv2022

Transparent Human Evaluation for Image Captioning

We establish THumB, a rubric-based human evaluation protocol for image captioning models. Our scoring rubrics and their definitions are carefully developed based on machine- and human-generated captions on the MSCOCO dataset. Each caption is evaluated along two main dimensions in a tradeoff (precision and recall) as well as other aspects that measure the text quality (fluency, conciseness, and inclusive language). Our evaluations demonstrate several critical problems of the current evaluation practice. Human-generated captions show substantially higher quality than machine-generated ones, especially in coverage of salient information (i.e., recall), while most automatic metrics say the opposite. Our rubric-based results reveal that CLIPScore, a recent metric that uses image features, better correlates with human judgments than conventional text-only metrics because it is more sensitive to recall. We hope that this work will promote a more transparent evaluation protocol for image captioning and its automatic metrics.

cs.CL↗

arXiv2022

Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

Natural language processing researchers have identified limitations of evaluation methodology for generation tasks, with new questions raised about the validity of automatic metrics and of crowdworker judgments. Meanwhile, efforts to improve generation models tend to depend on simple n-gram overlap metrics (e.g., BLEU, ROUGE). We argue that new advances on models and metrics should each more directly benefit and inform the other. We therefore propose a generalization of leaderboards, bidimensional leaderboards (Billboards), that simultaneously tracks progress in language generation models and metrics for their evaluation. Unlike conventional unidimensional leaderboards that sort submitted systems by predetermined metrics, a Billboard accepts both generators and evaluation metrics as competing entries. A Billboard automatically creates an ensemble metric that selects and linearly combines a few metrics based on a global analysis across generators. Further, metrics are ranked based on their correlation with human judgments. We release four Billboards for machine translation, summarization, and image captioning. We demonstrate that a linear ensemble of a few diverse metrics sometimes substantially outperforms existing metrics in isolation. Our mixed-effects model analysis shows that most automatic metrics, especially the reference-based ones, overrate machine over human generation, demonstrating the importance of updating metrics as generation models become stronger (and perhaps more similar to humans) in the future. Our project website is available at https://nlp.cs.washington.edu/billboard/.

cs.CL↗