Evaluating Open Language Models Across Task Types, Application Domains,
  and Reasoning Types: An In-Depth Experimental Analysis
The rapid rise of Language Models (LMs) has expanded their use in several
applications. Yet, due to constraints of model size, associated cost, or
proprietary restrictions, utilizing state-of-the-art (SOTA) LLMs is not always
feasible. With open, smaller LMs emerging, more applications can leverage their
capabilities, but selecting the right LM can be challenging. This work conducts
an in-depth experimental analysis of the semantic correctness of outputs of 10
smaller, open LMs across three aspects: task types, application domains and
reasoning types, using diverse prompt styles. We demonstrate that most
effective models and prompt styles vary depending on the specific requirements.
Our analysis provides a comparative assessment of LMs and prompt styles using a
proposed three-tier schema of aspects for their strategic selection based on
use-case and other constraints. We also show that if utilized appropriately,
these LMs can compete with, and sometimes outperform, SOTA LLMs like
DeepSeek-v2, GPT-3.5-Turbo, and GPT-4o.
667132b5bb4aa4b6a9c4471f
Neelabh Sinha
admin_assigned
2024-06-18T21:26:07.288Z
6639a17d7256327c68a0841e
/avatars/28fcd935149c2d89661c6f642842c310.svg
Neelabh Sinha
user
neelabhsinha
667132b5bb4aa4b6a9c44720
Vinija Jain
667132b5bb4aa4b6a9c44721
Aman Chadha
claimed_verified
2024-06-18T07:17:11.713Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
Evaluating Open Language Models Across Task Types, Application Domains,
  and Reasoning Types: An In-Depth Experimental Analysis
The rapid rise of Language Models (LMs) has expanded their use in several
applications. Yet, due to constraints of model size, associated cost, or
proprietary restrictions, utilizing state-of-the-art (SOTA) LLMs is not always
feasible. With open, smaller LMs emerging, more applications can leverage their
capabilities, but selecting the right LM can be challenging. This work conducts
an in-depth experimental analysis of the semantic correctness of outputs of 10
smaller, open LMs across three aspects: task types, application domains and
reasoning types, using diverse prompt styles. We demonstrate that most
effective models and prompt styles vary depending on the specific requirements.
Our analysis provides a comparative assessment of LMs and prompt styles using a
proposed three-tier schema of aspects for their strategic selection based on
use-case and other constraints. We also show that if utilized appropriately,
these LMs can compete with, and sometimes outperform, SOTA LLMs like
DeepSeek-v2, GPT-3.5-Turbo, and GPT-4o.
An experimental analysis of smaller open Language Models across various tasks, domains, and reasoning types reveals that effective models and prompt styles vary by use-case, enabling them to compete with state-of-the-art models.
Language Models semantic correctness task types application domains reasoning types prompt styles three-tier schema strategic selection