Alignment is a procedure to fine-tune pre-trained large language models (LLMs) to follow natural language instructions and serve as helpful AI assistants. We have observed, however, that the conventional alignment process fails to enhance the factual accuracy of LLMs, and often leads to the generation of more false facts (i.e.,). In this paper, we study how to make the LLM alignment process more factual, by first identifying factors that lead to hallucination in both alignment steps: supervised fine-tuning (SFT) and reinforcement learning (RL).In particular, we find that training the LLM on new or unfamiliar knowledge can encourage hallucination.This makes SFT less factual as it trains on human-labeled data that may be novel to the LLM. Furthermore, reward functions used in standard RL often inadequately capture factuality and favor longer and more detailed responses, which inadvertently promote hallucination.Based on these observations, we propose, comprised of and through direct preference optimization. Experiments show that our proposed guides LLMs to output more factual responses while maintaining their instruction-following capability.

PCWorld explains how vague prompts produce poor results from AI tools like ChatGPT and Gemini, emphasizing the need for specific, detailed requests. The article introduces prompt decomposition, a technique that breaks complex tasks into key variables to create more effective AI prompts. This method helps users guide AI tools more precisely, resulting in higher-quality, less biased outputs for complex tasks. It's never a good idea to hand ChatGPT, Claude, or Gemini big, vague tasks like "draw up a business plan for my new venture" or "act as my personal assistant." Fuzzy prompts like those are sure to yield equally fuzzy results, allowing the AI to make decisions based on its training data and inherent biases, potentially leading you down a path you never intended.

This curation process is believed to be necessary to produce 5 performant models with broad zero-shot generalization abilities. However, as larger 6 models requiring pretraining on trillions of tokens are considered, it is unclear how 7 scalable is curation, and whether we will run out of unique high-quality data soon.

Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data. We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing. To maintain data integrity, a model trained on the original dataset filters out minimal image edits and those which corrupt class-relevant information. The resulting dataset is visually consistent with the original training data and offers significantly enhanced diversity. We show that ALIA is able to surpasses traditional data augmentation and text-to-image generated data on fine-grained classification tasks, including cases of domain generalization and contextual bias. Code is available at https://github.com/lisadunlap/ALIA.

Human commonsense understanding of the physical and social world is organized around intuitive theories. These theories support making causal and moral judgments. When something bad happens, we naturally ask: who did what, and why? A rich literature in cognitive science has studied people's causal and moral intuitions. This work has revealed a number of factors that systematically influence people's judgments, such as the violation of norms and whether the harm is avoidable or inevitable.

The task of PubMedQA is to answer research questions with yes/no/maybe provided with the corresponding abstracts.