Reinforcement Learning with Human Feedback(RLHF) is a common post-training procedureto align the outputs of Large Language Mod-els (LLMs) with human preferences. As a re-sult, one might expect RLHF to induce someelements of human-like audience design intoLLMs. However, RLHF and other post-trainingalignment methods have many complex effectson the outputs of LLMs that have yet to be stud-ied quantitatively. We apply an information-theoretic lens to investigate the changes in the"naturalness" of language and the presence ofaudience design in LLMs before and after post-training. The Uniform Information Density(UID) Hypothesis posits that humans optimizelanguage production and comprehension acrossa noisy channel by transferring information ata more uniform rate. Accordingly, we analyzeand compare how information is distributedwithin model- and human-generated text fromdifferent domains. We find that pretrained andpost-trained LLMs both show superhuman uni-formity across various text domains, and bothRLHF and other post-training methods reduceuniformity slightly from their pretrained coun-terparts. However, RLHF uniquely encourageslower variance in uniformity between docu-ments, potentially demonstrating that trainingon human preferences encourages consistencyin information flow.

Large language models (LLMs) encapsulate vast amounts of knowledge but still remain vulnerable to external misinformation. Existing research mainly studied this susceptibility behavior in a single-turn setting. However, belief can change during a multi-turn conversation, especially a persuasive one. Therefore, in this study, we delve into LLMs’ susceptibility to persuasive conversations, particularly on factual questions that they can answer correctly. We first curate the Farm (i.e., Fact to Misinform) dataset, which contains factual questions paired with systematically generated persuasive misinformation. Then, we develop a testing framework to track LLMs’ belief changes in a persuasive dialogue. Through extensive experiments, we find that LLMs’ correct beliefs on factual knowledge can be easily manipulated by various persuasive strategies.

We introduce VL-CheckList, a toolbox for evaluating Vision-Language Pretraining (VLP) models, including the preliminary datasets that deepen the image-texting ability of a VLP model. Most existing VLP works evaluated their systems by comparing the fine-tuned downstream task performance. However, only average downstream task accuracy provides little information about the pros and cons of each VLP method. In this paper, we demonstrate how minor input changes in language and vision will affect the prediction outputs. Then, we describe the detailed user guidelines to utilize and contribute to the community. We show new findings on one of the representative VLP models to provide an example analysis. The data/code is available at https://github.com/om-ai-lab/VL-CheckList