title: Blending Is All You Need: Cheaper, Better Alternative to
    Trillion-Parameters LLM
summary: In conversational AI research, there's a noticeable trend towards developing
  models with a larger number of parameters, exemplified by models like ChatGPT.
  While these expansive models tend to generate increasingly better chat
  responses, they demand significant computational resources and memory. This
  study explores a pertinent question: Can a combination of smaller models
  collaboratively achieve comparable or enhanced performance relative to a
  singular large model? We introduce an approach termed "blending", a
  straightforward yet effective method of integrating multiple chat AIs. Our
  empirical evidence suggests that when specific smaller models are
  synergistically blended, they can potentially outperform or match the
  capabilities of much larger counterparts. For instance, integrating just three
  models of moderate size (6B/13B paramaeters) can rival or even surpass the
  performance metrics of a substantially larger model like ChatGPT (175B+
  paramaters). This hypothesis is rigorously tested using A/B testing
  methodologies with a large user base on the Chai research platform over a span
  of thirty days. The findings underscore the potential of the "blending"
  strategy as a viable approach for enhancing chat AI efficacy without a
  corresponding surge in computational demands.
paper_authors._id: 659cbc9e53f784cdcabe92c3
paper_authors.name: Xiaoding Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-09T08:07:10.374Z
paper_authors._id: 631898bf0d238735a9024542
paper_authors.avatarUrl: /avatars/433c5d02ceb2b7a18a29e9d5ec848350.svg
paper_authors.fullname: Xiaoding Lu
paper_authors.type: user
paper_authors.user: xiaoding
paper_authors._id: 659cbc9e53f784cdcabe92c4
paper_authors.name: Adian Liusie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-09T08:07:16.712Z
paper_authors._id: 6062a8032cefbb7b8bf8b54e
paper_authors.avatarUrl: /avatars/3d6a73ada67636fde04caeb51c9a2953.svg
paper_authors.fullname: Adian Liusie
paper_authors.type: user
paper_authors.user: adianl
paper_authors._id: 659cbc9e53f784cdcabe92c5
paper_authors.name: Vyas Raina
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-09T08:07:23.535Z
paper_authors._id: 6397bfa225c1183858afa56c
paper_authors.avatarUrl: /avatars/587d4e116584c9960d27e59b10cfd1a3.svg
paper_authors.fullname: V R
paper_authors.type: user
paper_authors.user: vyasraina
paper_authors._id: 659cbc9e53f784cdcabe92c6
paper_authors.name: Yuwen Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-09T08:07:45.399Z
paper_authors._id: 6332377c49a95639154d6edf
paper_authors.avatarUrl: /avatars/5a212220927b9ce6e3dbc895ff2e7481.svg
paper_authors.fullname: Yuwen Zhang
paper_authors.type: user
paper_authors.user: thatyuwen
paper_authors._id: 659cbc9e53f784cdcabe92c7
paper_authors.name: William Beauchamp
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-09T08:08:31.350Z
paper_authors._id: 6155957c9fd38df575eccbcb
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1632998777241-noauth.jpeg
paper_authors.fullname: William Beauchamp
paper_authors.type: user
paper_authors.user: Meliodia
paper_title: Blending Is All You Need: Cheaper, Better Alternative to
    Trillion-Parameters LLM
paper_summary: In conversational AI research, there's a noticeable trend towards developing
  models with a larger number of parameters, exemplified by models like ChatGPT.
  While these expansive models tend to generate increasingly better chat
  responses, they demand significant computational resources and memory. This
  study explores a pertinent question: Can a combination of smaller models
  collaboratively achieve comparable or enhanced performance relative to a
  singular large model? We introduce an approach termed "blending", a
  straightforward yet effective method of integrating multiple chat AIs. Our
  empirical evidence suggests that when specific smaller models are
  synergistically blended, they can potentially outperform or match the
  capabilities of much larger counterparts. For instance, integrating just three
  models of moderate size (6B/13B paramaeters) can rival or even surpass the
  performance metrics of a substantially larger model like ChatGPT (175B+
  paramaters). This hypothesis is rigorously tested using A/B testing
  methodologies with a large user base on the Chai research platform over a span
  of thirty days. The findings underscore the potential of the "blending"
  strategy as a viable approach for enhancing chat AI efficacy without a
  corresponding surge in computational demands.
paper_ai_summary: A combination of smaller chat AI models, through a method called blending, can achieve performance comparable to or better than large single models with lower computational resources.
paper_ai_keywords: chat AIs blending A/B testing Chai research platform