Loss masking has been proposed as a method for preventing language models from generating specific content by selectively zeroes the training loss on sensitive tokens,which allows a language model to learn protected content as contextwithout learning to reproduce it (CITATION).% Although promising, many critical questions about the impacts to a model remain unanswered. In this work, we investigate the impact of loss masking on internal model representation and context understanding using a small causal language model (GPT-2) at three scales (124M, 355M, 774M parameters) and apply mechanistic interpretability tools including causal tracing, attention analysis, and linear probing. We explore two use cases of loss-masking: backdoor concealment and prevention of memorization of named entities. In both settings, we find that loss masking successfully blocks generation of the protected tokens. Through mechanistic analysis, we show that protected token identity remains fully encoded in hidden states regardless of loss masking, confirming that loss masking suppresses the output pathway but not the internal encoding. Code is available at https://github.com/Tagore-7/loss-masking-analysis

This study seeks to test whether low-cost inference and efficient Small Language Models (SLMs) fine-tuned on existing open-source question answering datasets are capable of creating financial literacy chat bots that can answer financial questions for those with limited financial knowledge. The use of SLMs is growing in popularity across many domains, but SLMs are not thoroughly explored in the finance sector. This study offers an exploration of challenges and opportunities that exist in the finance sector to utilize SLMs for open-source financial question answering applications. In particular, this study examines the outputs of several open-source SLMs fine-tuned on the open-source FinGPT FiQA_QA financial question answering dataset. We fine-tuned two versions of each model, one with an instruction prompt and one without an instruction prompt and compared the model outputs with ground truth human responses from the dataset. Further qualitative rating and analysis are provided for model outputs and the dataset. The exploration highlighted challenges with available open data and the fine-tuned SLMs. Existing open data sets in the financial AI research community are not sufficient to produce high-quality outputs with SLMs. Successful fine-tuning of SLMs has occurred in other domains with high quality data sets. We thus issue a call for new and better open financial question answering datasets that could result in higher-quality small language models.

Large language models are known to memorize training data under certain training conditions. It can be desirable to selectively prevent personal information from being memorized; and one such method of selectively preventing memorization that has been proposed is loss masking. To the best of the authors knowledge, at the time of writing, although this method has been alluded to, there has not been a thorough empirical evaluation of the utility of this method. We describe the method of loss masking and demonstrate its performance through a set of experiments on a small autoregressive language model. We base one experiment on previous work finding memorized personal information in language models and another experiment on searching for backdoor watermarking trigger words and phrases. Overall, we find that loss masking is highly effective at selectively preventing memorization of sensitive information.

The use of small language models (SLMs), herein defined as models with less than three billion parameters, is increasing across various domains and applications. Due to their ability to run on more accessible hardware and preserve user privacy, SLMs possess the potential to democratize access to language models for individuals of different socioeconomic status and with different privacy preferences. This study assesses several state-of-the-art SLMs (e.g., Apple’s OpenELM, Microsoft’s Phi, Google’s Gemma, and the Tinyllama project) for use in the financial domain to support the development of financial literacy LMs. Democratizing access to quality financial information for those who are financially under educated is greatly needed in society, particularly as new financial markets and products emerge and participation in financial markets increases due to ease of access. We are the first to examine the use of open-source SLMs to democratize access to financial question answering capabilities for individuals and students. To this end, we provide an analysis of the memory usage, inference time, similarity comparisons to ground-truth answers, and output readability of prominent SLMs to determine which models are most accessible and capable of supporting access to financial information. We analyze zero-shot and few-shot learning variants of the models. The results suggest that some off-the-shelf SLMs merit further exploration and fine-tuning to prepare them for individual use, while others may have limits to their democratization. Code to replicate our experiments is shared.

In this paper we detail our submissions to the Strict and Strict-Small tracks of the 2024 BabyLM Challenge. We approach this challenge with two methodologies: i) use of a novel dataset, and ii) development of a pre-training technique based on the fusion of child language acquisition with traditional masked language modeling, which we call curriculum masking. The novel dataset used for this task is based on user submissions to the Reddit forum (i.e., subreddit) “Explain Like I’m Five”, which explains diverse concepts using simple language. Curriculum masking works by creating learning phases based on a standard child language development timeline, where the masked words learned by the model start with simple nouns and gradually expand to include more complex parts of speech. We show that using internet-based training data shows a small improvement in evaluation scores as compared to baseline training data. Our proposed pre-training method of curriculum masking is conceptually novel and also shows improved rates of learning over typical masked language modeling pre-training, potentially allowing for good performance with fewer total epochs on smaller training datasets. Code for the curriculum masking implementation is shared at https://github.com/evan-person/curriculumMaskingBabyLM2024.