Large language model (LLM) personalization aims to align model outputs with individuals' unique preferences and opinions.While recent efforts have implemented various personalization methods, a unified theoretical framework that can systematically understand the drivers of effective personalization is still lacking.In this work, we integrate the well-established cognitive dual-memory model into LLM personalization, by mirroring episodic memory to historical user engagements and semantic memory to long-term, evolving user beliefs.Specifically, we systematically investigate memory instantiations and introduce a unified framework, PRIME, using episodic and semantic memory mechanisms.We further augment PRIME with a novel personalized thinking capability inspired by the slow thinking strategy.Moreover, recognizing the absence of suitable benchmarks, we introduce a dataset using Change My View (CMV) from Reddit 1 , specifically designed to evaluate long-context personalization.Extensive experiments validate PRIME's effectiveness across both longand short-context scenarios.Further analysis confirms that PRIME effectively captures dynamic personalization beyond mere popularity biases.

Xinliang Frederick Zhang, Winston Wu, Nick Beauchamp, Lu Wang. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets.But while much attention has been devoted to media bias via overt ideological language or topic selection, a more unobtrusive way in which the media shape opinion is via the strategic inclusion or omission of partisan events that may support one side or the other.We develop a latent variable-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology.Our experiments first validate the existence of partisan event selection, and then show that article alignment and cross-document comparison detect partisan events and article ideology better than competitive baselines.Our results reveal the high-level form of media bias, which is present even among mainstream media with strong norms of objectivity and nonpartisanship.

As polarization continues to rise among both the public and the news media, increasing attention has been devoted to detecting media bias.Most recent work in the NLP community, however, identify bias at the level of individual articles.However, each article itself comprises multiple sentences, which vary in their ideological bias.In this paper, we aim to identify sentences within an article that can illuminate and explain the overall bias of the entire article.We show that understanding the discourse role of a sentence in telling a news story, as well as its relation with nearby sentences, can reveal the ideological leanings of an author even when the sentence itself appears merely neutral.In particular, we consider using a functional news discourse structure and PDTB discourse relations to inform bias sentence identification, and distill the auxiliary knowledge from the two types of discourse structure into our bias sentence identification system.Experimental results on benchmark datasets show that incorporating both the global functional discourse structure and local rhetorical discourse relations can effectively increase the recall of bias sentence identification by 8.27% -8.62%, as well as increase the precision by 2.82% -3.48% 1 .

Stance detection is typically framed as predicting the sentiment in a given text towards a target entity.However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion.In this paper, we emphasize the need for studying interactions among entities when inferring stances.We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly.To support this study, we curate a new dataset with 10,619 annotations labeled at the sentence-level from news articles of different ideological leanings.We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them.We further enhance the model with a graph encoder to summarize entity activities and external knowledge surrounding the entities.Experiments show that our model outperforms strong comparisons by large margins.Further analyses demonstrate the usefulness of E2E stance detection for understanding media quotation and stance landscape, as well as inferring entity ideology.

Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments. We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two. Using a dataset of 118 Oxford-style debates, our model’s combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%). Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments.