Blending Is All You Need: Cheaper, Better Alternative to
  Trillion-Parameters LLM
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.
659cbc9e53f784cdcabe92c3
Xiaoding Lu
admin_assigned
2024-01-09T08:07:10.374Z
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Xiaoding Lu
user
xiaoding
659cbc9e53f784cdcabe92c4
Adian Liusie
admin_assigned
2024-01-09T08:07:16.712Z
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Adian Liusie
user
adianl
659cbc9e53f784cdcabe92c5
Vyas Raina
admin_assigned
2024-01-09T08:07:23.535Z
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V R
user
vyasraina
659cbc9e53f784cdcabe92c6
Yuwen Zhang
admin_assigned
2024-01-09T08:07:45.399Z
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Yuwen Zhang
user
thatyuwen
659cbc9e53f784cdcabe92c7
William Beauchamp
admin_assigned
2024-01-09T08:08:31.350Z
6155957c9fd38df575eccbcb
https://cdn-avatars.huggingface.co/v1/production/uploads/1632998777241-noauth.jpeg
William Beauchamp
user
Meliodia
Blending Is All You Need: Cheaper, Better Alternative to
  Trillion-Parameters LLM
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.
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.
chat AIs blending A/B testing Chai research platform