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In the rapidly evolving domain of software engineering (SE), Large Language Models (LLMs) are increasingly leveraged to automate developer support. Open source LLMs have grown competitive with pro- prietary models such as GPT-4 and Claude-3, without the associated financial and accessibility constraints. This study investigates whether state of the art open source LLMs including Solar-10.7B, CodeLlama-7B, Mistral-7B, Qwen2-7B, StarCoder2-7B, and LLaMA3-8B can generate responses to technical queries that align with those crafted by human experts. Leveraging retrieval augmented generation (RAG) and targeted fine tuning, we evaluate these models across critical performance dimen- sions, such as semantic alignment and contextual fluency. Our results show that Solar-10.7B, particularly when paired with RAG and fine tun- ing, most closely replicates expert level responses, o!ering a scalable and cost e!ective alternative to commercial models. This vision paper high- lights the potential of open-source LLMs to enable robust and accessible AI-powered developer assistance in software engineering.
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Personality of AI
This research paper delves into the evolving landscape of fine-tuning large language models (LLMs) to align with human users, extending beyond basic alignment to propose "personality alignment" for language models in organizational settings. Acknowledging the impact of training methods on the formation of undefined personality traits in AI models, the study draws parallels with human fitting processes using personality tests. Through an original case study, we demonstrate the necessity of personality fine-tuning for AIs and raise intriguing questions about applying human-designed tests to AIs, engineering specialized AI personality tests, and shaping AI personalities to suit organizational roles. The paper serves as a starting point for discussions and developments in the burgeoning field of AI personality alignment, offering a foundational anchor for future exploration in human-machine teaming and co-existence.
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- Award ID(s):
- 2131269
- PAR ID:
- 10546090
- Publisher / Repository:
- Springer LNCS
- Date Published:
- Subject(s) / Keyword(s):
- AI LLM Prompt Engineering
- Format(s):
- Medium: X
- Location:
- Zakopane, Poland
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
