EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 2·5 min read
Don't credit the LLM
TL;DR
A growing trend among professionals is to disclose their use of large language models (LLMs) like ChatGPT when completing tasks, reflecting a desire for transparency and credit. This practice raises questions about the perception of LLMs as tools versus sources of creativity.
✦ Why It Matters
Consider how you communicate your use of AI tools to foster transparency and authenticity in your work.
Key Takeaways
- Avoid attributing your work solely to LLMs.
- Embrace full accountability for both successes and failures.
- Recognize the limitations of LLM-generated content.
- Foster a culture of responsibility in your team.
Full Summary
Recently, many professionals have begun attributing their work to large language models (LLMs), such as stating that an LLM generated their unit tests or provided data insights. This trend may stem from a desire to share credit with the tool, a feeling of imposter syndrome, or even organizational pressure to use LLMs.
However, the author challenges this practice, emphasizing that credit and accountability are intertwined. By attributing work to an LLM, individuals may inadvertently dilute their own responsibility for the quality of the output.
Instead, they should embrace both the successes and failures of their work, as true accountability fosters growth and improvement. Ultimately, the author advocates for taking full credit for one's creations, regardless of the assistance received from AI tools.