Large Language Model Alignment: A Survey

Recent years have witnessed remarkable progress made in large language models (LLMs). Such advancements, while garnering significant attention, have concurrently elicited various concerns. The potential of these models is undeniably vast; however, they may yield texts that are imprecise, misleading, or even detrimental. Consequently, it becomes paramount to employ alignment techniques to ensure these models to exhibit behaviors consistent with human values. This survey endeavors to furnish an extensive exploration of alignment methodologies designed for LLMs, in conjunction with the extant capability research in this domain. Adopting the lens of AI alignment, we categorize the prevailing methods and emergent proposals for the alignment of LLMs into outer and inner alignment. We also probe into salient issues including the models' interpretability, and potential vulnerabilities to adversarial attacks. To assess LLM alignment, we present a wide variety of benchmarks and evaluation methodologies. After discussing the state of alignment research for LLMs, we finally cast a vision toward the future, contemplating the promising avenues of research that lie ahead. Our aspiration for this survey extends beyond merely spurring research interests in this realm. We also envision bridging the gap between the AI alignment research community and the researchers engrossed in the capability exploration of LLMs for both capable and safe LLMs.Comment: 76 page

NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles

In this paper, we present a dataset of 713k articles collected between 02/2018-11/2018. These articles are collected directly from 194 news and media outlets including mainstream, hyper-partisan, and conspiracy sources. We incorporate ground truth ratings of the sources from 8 different assessment sites covering multiple dimensions of veracity, including reliability, bias, transparency, adherence to journalistic standards, and consumer trust. The NELA-GT-2018 dataset can be found at https://doi.org/10.7910/DVN/ULHLCB.Comment: Published at ICWSM 201

Reading Certainty: Evidence from a Large Study on NLP and Witness Testimony

Witness testimony provides the first draft of history, and requires a kind of reading that connects descriptions of events from many perspectives and sources. "Reading Certainty" examines one critical step in that process, namely how a group of approximately 230 readers decided whether a statement about an event is credible and factual. That examination supports an exploration of how readers of primary evidence think about factual and counterfactual statements, and how they interpret the certainty with which a witness makes their statements. This presentation argues that readers of collections of witness testimony were more likely to agree about event descriptions when those providing the description are certain, and that the ability of readers to accept gradations of certainty were better when a witness described factual, rather than counter-factual events. These findings lead to a suggestion for how researchers in linguistics and the humanities could better model the question of speaker certainty, at least when dealing with the kind of narrative non-fiction one finds in witness testimony