Summary of Benchmarking Distributional Alignment Of Large Language Models, by Nicole Meister et al.
Benchmarking Distributional Alignment of Large Language Models
by Nicole Meister, Carlos Guestrin, Tatsunori Hashimoto
First submitted to arxiv on: 8 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 This paper investigates the ability of language models (LMs) to mimic the views of a specific demographic group, known as distributional alignment. The authors argue that previous works have overlooked three key variables – question domain, steering method, and distribution expression method – which affect the outcome. To address this gap, they created a benchmark that explicitly considers these dimensions, constructed a dataset that goes beyond political values, and established human baselines for the task. The analysis reveals that LMs can accurately describe opinion distributions but struggle to simulate them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper explores how well language models can mimic people’s opinions. It looks at three important factors that affect the outcome: the type of questions asked, how the model is directed, and how it expresses its answers. The researchers created a new benchmark to test these factors, used data beyond just political views, and set up a baseline for how humans perform on this task. The results show that language models can describe opinion distributions well but struggle to actually simulate them. |
Keywords
» Artificial intelligence » Alignment
