A Roadmap to Pluralistic Alignment

• URL: http://arxiv.org/abs/2402.05070v3
• Date: Tue, 20 Aug 2024 19:14:31 GMT
• Title: A Roadmap to Pluralistic Alignment
• Authors: Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, Yejin Choi,
• Abstract summary: We propose a roadmap to pluralistic alignment, specifically using language models as a test bed. We identify and formalize three possible ways to define and operationalize pluralism in AI systems. We argue that current alignment techniques may be fundamentally limited for pluralistic AI.
• Score: 49.29107308098236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With increased power and prevalence of AI systems, it is ever more critical that AI systems are designed to serve all, i.e., people with diverse values and perspectives. However, aligning models to serve pluralistic human values remains an open research question. In this piece, we propose a roadmap to pluralistic alignment, specifically using language models as a test bed. We identify and formalize three possible ways to define and operationalize pluralism in AI systems: 1) Overton pluralistic models that present a spectrum of reasonable responses; 2) Steerably pluralistic models that can steer to reflect certain perspectives; and 3) Distributionally pluralistic models that are well-calibrated to a given population in distribution. We also formalize and discuss three possible classes of pluralistic benchmarks: 1) Multi-objective benchmarks, 2) Trade-off steerable benchmarks, which incentivize models to steer to arbitrary trade-offs, and 3) Jury-pluralistic benchmarks which explicitly model diverse human ratings. We use this framework to argue that current alignment techniques may be fundamentally limited for pluralistic AI; indeed, we highlight empirical evidence, both from our own experiments and from other work, that standard alignment procedures might reduce distributional pluralism in models, motivating the need for further research on pluralistic alignment.

Related papers

• Plurals: A System for Guiding LLMs Via Simulated Social Ensembles [1.9034114150823245]
  We introduce Plurals, a system and Python library for pluralistic AI deliberation. plurals consists of Agents which deliberate within customizable Structures, with Moderators overseeing deliberation. Six case studies demonstrate fidelity to theoretical constructs and efficacy.
  arXiv  Detail & Related papers  (2024-09-25T17:38:39Z)
• Eureka: Evaluating and Understanding Large Foundation Models [23.020996995362104]
  We present Eureka, an open-source framework for standardizing evaluations of large foundation models beyond single-score reporting and rankings. We conduct an analysis of 12 state-of-the-art models, providing in-depth insights into failure understanding and model comparison.
  arXiv  Detail & Related papers  (2024-09-13T18:01:49Z)
• Ranking Large Language Models without Ground Truth [24.751931637152524]
  Evaluation and ranking of large language models (LLMs) has become an important problem with the proliferation of these models. We provide a novel perspective where, given a dataset of prompts, we rank them without access to any ground truth or reference responses. Applying this idea repeatedly, we propose two methods to rank LLMs.
  arXiv  Detail & Related papers  (2024-02-21T00:49:43Z)
• Steering Responsible AI: A Case for Algorithmic Pluralism [0.0]
  I suggest examining further the notion of algorithmic pluralism. I argue, algorithmic pluralism has the potential to sustain the diversity, multiplicity, and inclusiveness that are so vital to democracy.
  arXiv  Detail & Related papers  (2023-11-20T18:45:04Z)
• ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life Videos [53.92440577914417]
  ACQUIRED consists of 3.9K annotated videos, encompassing a wide range of event types and incorporating both first and third-person viewpoints. Each video is annotated with questions that span three distinct dimensions of reasoning, including physical, social, and temporal. We benchmark our dataset against several state-of-the-art language-only and multimodal models and experimental results demonstrate a significant performance gap.
  arXiv  Detail & Related papers  (2023-11-02T22:17:03Z)
• Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties [68.66719970507273]
  Value pluralism is the view that multiple correct values may be held in tension with one another. As statistical learners, AI systems fit to averages by default, washing out potentially irreducible value conflicts. We introduce ValuePrism, a large-scale dataset of 218k values, rights, and duties connected to 31k human-written situations.
  arXiv  Detail & Related papers  (2023-09-02T01:24:59Z)
• Don't Copy the Teacher: Data and Model Challenges in Embodied Dialogue [92.01165203498299]
  Embodied dialogue instruction following requires an agent to complete a complex sequence of tasks from a natural language exchange. This paper argues that imitation learning (IL) and related low-level metrics are actually misleading and do not align with the goals of embodied dialogue research.
  arXiv  Detail & Related papers  (2022-10-10T05:51:40Z)
• Making sense of spoken plurals [1.80476943513092]
  This study focuses on the semantics of noun singulars and their plural inflectional variants in English. One model (FRACSS) proposes that all singular-plural pairs should be taken into account when predicting plural semantics from singular semantics. The other model (CCA) argues that conceptualization for plurality depends primarily on the semantic class of the base word.
  arXiv  Detail & Related papers  (2022-07-05T10:44:26Z)
• Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand [117.62186420147563]
  We propose a generalization of leaderboards, bidimensional leaderboards (Billboards) Unlike conventional unidimensional leaderboards that sort submitted systems by predetermined metrics, a Billboard accepts both generators and evaluation metrics as competing entries. We demonstrate that a linear ensemble of a few diverse metrics sometimes substantially outperforms existing metrics in isolation.
  arXiv  Detail & Related papers  (2021-12-08T06:34:58Z)
• AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses [97.50616524350123]
  We build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering. The first model, MinAvgOut, directly maximizes the diversity score through the output distributions of each batch. The second model, Label Fine-Tuning (LFT), prepends to the source sequence a label continuously scaled by the diversity score to control the diversity level. The third model, RL, adopts Reinforcement Learning and treats the diversity score as a reward signal.
  arXiv  Detail & Related papers  (2020-01-15T18:32:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.