Enhanced Personalized Search using Social Data

Zhou, Dong; Lawless, Séamus; Wu, Xuan; Zhao, Wenyu; Liu, Jianxun

doi:10.18653/v1/d16-1067

Cited by 6 publications

(1 citation statement)

References 37 publications

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“…4 For example, a title keyword search for 'personali' or 'personaliz' returns 124 articles from the ACL Anthology and a further 10 from the arXiv Computation and Language (cs.CL) subclass. These systems cover a wide range of tasks including dialogue [127,157,36,39,41,109,133,146,149,206,238,244], recipe or diet generation [147,87,159], summarisation [215,240], machine translation [156,153,194,237], QA [137,193], search and information retrieval [4,40,59,70,245], sentiment analysis [80,155,226], domain classification [129,114,113], entity resolution [132], and aggression or abuse detection [107,108]; and are applied to a number of societal domains such as education [118,163,241], medicine [3,15,225,235] and news consumption…”

Section: From Implicit To Explicit Personalisationmentioning

confidence: 99%

Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Kirk¹,

Vidgen²,

Röttger³

et al. 2023

Preprint

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Large language models (LLMs) are used to generate content for an increasingly wide range of tasks, and are set to reach a growing audience in coming years due to integration in product interfaces like ChatGPT or search engines like Bing. This intensifies the need to ensure that models are aligned with human preferences and do not produce unsafe, inaccurate or toxic outputs. While alignment techniques like reinforcement learning with human feedback (RLHF) and red-teaming can mitigate some safety concerns and improve model capabilities, it is unlikely that an aggregate fine-tuning process can adequately represent the full range of users' preferences and values. Different people may legitimately disagree on their preferences for language and conversational norms, as well as on values or ideologies which guide their communication. Personalising LLMs through micro-level preference learning processes may result in models that are better aligned with each user. However, there are several normative challenges in defining the bounds of a societally-acceptable and safe degree of personalisation. In this paper, we ask how, and in what ways, LLMs should be personalised. First, we review literature on current paradigms for aligning LLMs with human feedback, and identify issues including (i) a lack of clarity regarding what alignment means; (ii) a tendency of technology providers to prescribe definitions of inherently subjective preferences and values; and (iii) a "tyranny of the crowdworker", exacerbated by a lack of documentation in who we are really aligning to. Second, we present a taxonomy of benefits and risks associated with personalised LLMs, for individuals and society at large. Finally, we propose a three-tiered policy framework that allows users to experience the benefits of personalised alignment, while restraining unsafe and undesirable LLM-behaviours within (supra-)national and organisational bounds.

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