Powerful LLMs like ChatGPT are adopted rapidly for a wide array of tasks, but their limitations in domain-specific areas become apparent, particularly when prompted to recite facts. This is critical especially for knowledge workers, who are adopting LLM-based tools rapidly.While there are various techniques that can help ingest knowledge into LLMs such as instruction tuning and alignment, most have disadvantages. We examine the impact of prominent training techniques on LLMs’ knowledge accuracy using a knowledge-dense dataset that we curate from r/AskHistorians, a rich source of historical knowledge. We evaluate the impact of different models sizes from 1.3B to 7B parameters and other factors such as LoRA adapters, quantization, overfitting, and the inclusion of Reddit data in pretraining.In addition, we measure linguistic metrics and human and LLM-based preference. Our results suggest that pretraining and model size have a much stronger effect on knowledge accuracy than continued pretraining – unless the model is overfit to the tested knowledge.Fine-tuning on our Reddit dataset introduces less complex, but slightly more toxic language. Our study explores the challenges of injecting domain-specific datasets into LLMs and has implications for practitioners, e.g., when LLMs are to be fine-tuned with a company’s datasets.

Retail investing is on the rise, and a growing number of users is relying on online finance communities to educate themselves.However, recent years have positioned Large Language Models (LLMs) as powerful question answering (QA) tools, shifting users away from interacting in communities towards discourse with AI-driven conversational interfaces.These AI tools are currently limited by the availability of labelled data containing domain-specific financial knowledge.Therefore, in this work, we curate a QA preference dataset SocialFinanceQA for fine-tuning and aligning LLMs, extracted from more than 7.4 million submissions and 82 million comments from 2008 to 2022 in Reddit’s 15 largest finance communities. Additionally, we propose a novel framework called SocialQA-Eval as a generally-applicable method to evaluate generated QA responses.We evaluate various LLMs fine-tuned on this dataset, using traditional metrics, LLM-based evaluation, and human annotation. Our results demonstrate the value of high-quality Reddit data, with even state-of-the-art LLMs improving on producing simpler and more specific responses.

Recent Large Language Models (LLMs) have shown the ability to generate content that is difficult or impossible to distinguish from human writing. We investigate the ability of differently-sized LLMs to replicate human writing style in short, creative texts in the domain of Showerthoughts, thoughts that may occur during mundane activities. We compare GPT-2 and GPT-Neo fine-tuned on Reddit data as well as GPT-3.5 invoked in a zero-shot manner, against human-authored texts. We measure human preference on the texts across the specific dimensions that account for the quality of creative, witty texts. Additionally, we compare the ability of humans versus fine-tuned RoBERTa-based classifiers to detect AI-generated texts. We conclude that human evaluators rate the generated texts slightly worse on average regarding their creative quality, but they are unable to reliably distinguish between human-written and AI-generated texts. We further provide the dataset for creative, witty text generation based on Reddit Showerthoughts posts.