title: Training Socially Aligned Language Models in Simulated Human Society
summary: Social alignment in AI systems aims to ensure that these models behave
  according to established societal values. However, unlike humans, who derive
  consensus on value judgments through social interaction, current language
  models (LMs) are trained to rigidly replicate their training corpus in
  isolation, leading to subpar generalization in unfamiliar scenarios and
  vulnerability to adversarial attacks. This work presents a novel training
  paradigm that permits LMs to learn from simulated social interactions. In
  comparison to existing methodologies, our approach is considerably more
  scalable and efficient, demonstrating superior performance in alignment
  benchmarks and human evaluations. This paradigm shift in the training of LMs
  brings us a step closer to developing AI systems that can robustly and
  accurately reflect societal norms and values.
paper_authors._id: 6473f9752a74fb43cce7bc6a
paper_authors.name: Ruibo Liu
paper_authors.status: claimed_verified
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paper_authors.fullname: Ruibo Liu
paper_authors.type: user
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paper_authors._id: 6473f9752a74fb43cce7bc6b
paper_authors.name: Ruixin Yang
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paper_authors.fullname: Ray Yang
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paper_authors.user: RayY
paper_authors._id: 6473f9752a74fb43cce7bc6c
paper_authors.name: Chenyan Jia
paper_authors._id: 6473f9752a74fb43cce7bc6d
paper_authors.name: Ge Zhang
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paper_authors.fullname: Ge Zhang
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paper_authors.user: zhangysk
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paper_authors.name: Denny Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-20T18:28:38.521Z
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paper_authors.fullname: Denny Zhou
paper_authors.type: user
paper_authors.user: dennyzhou
paper_authors._id: 6473f9752a74fb43cce7bc6f
paper_authors.name: Andrew M. Dai
paper_authors._id: 6473f9752a74fb43cce7bc70
paper_authors.name: Diyi Yang
paper_authors._id: 6473f9752a74fb43cce7bc71
paper_authors.name: Soroush Vosoughi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-08T16:08:41.881Z
paper_authors._id: 648169d3cb64a2b85a4606e6
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paper_authors.fullname: Soroush Vosoughi
paper_authors.type: user
paper_authors.user: soroushv
paper_title: Training Socially Aligned Language Models in Simulated Human Society
paper_summary: Social alignment in AI systems aims to ensure that these models behave
  according to established societal values. However, unlike humans, who derive
  consensus on value judgments through social interaction, current language
  models (LMs) are trained to rigidly replicate their training corpus in
  isolation, leading to subpar generalization in unfamiliar scenarios and
  vulnerability to adversarial attacks. This work presents a novel training
  paradigm that permits LMs to learn from simulated social interactions. In
  comparison to existing methodologies, our approach is considerably more
  scalable and efficient, demonstrating superior performance in alignment
  benchmarks and human evaluations. This paradigm shift in the training of LMs
  brings us a step closer to developing AI systems that can robustly and
  accurately reflect societal norms and values.
paper_ai_summary: A novel training paradigm enables language models to learn from simulated social interactions, improving their alignment with societal norms and values compared to existing methods.
paper_ai_keywords: social alignment AI systems language models training corpus adversarial attacks simulated social interactions alignment benchmarks