Summary of Bottom-up and Top-down Analysis Of Values, Agendas, and Observations in Corpora and Llms, by Scott E. Friedman et al.
Bottom-Up and Top-Down Analysis of Values, Agendas, and Observations in Corpora and LLMs
by Scott E. Friedman, Noam Benkler, Drisana Mosaphir, Jeffrey Rye, Sonja M. Schmer-Galunder, Micah Goldwater, Matthew McLure, Ruta Wheelock, Jeremy Gottlieb, Robert P. Goldman, Christopher Miller
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a validated approach for automatically extracting and analyzing the socio-cultural values expressed by large language models (LLMs). The method consists of three steps: (1) identifying heterogeneous latent value propositions in texts, (2) assessing the resonance and conflict of these values with the texts, and (3) combining these operations to characterize the pluralistic value alignment of human-sourced and LLM-sourced textual data. This research aims to promote safety, accuracy, inclusion, and cultural fidelity in the adoption of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study focuses on characterizing the socio-cultural values expressed by large language models (LLMs) and how they compare with human-generated texts. The researchers developed a method to automatically extract these values from texts, assess their alignment with the texts, and analyze the results. This work aims to improve the use of LLMs by ensuring safety, accuracy, and cultural sensitivity. |
Keywords
» Artificial intelligence » Alignment
