Two new research papers explore the evolving capabilities of Large Language Models (LLMs) and their implications. One study, "Artificial Effort," demonstrates that most real-effort tasks, previously used to measure human performance, can now be accurately solved by LLMs at minimal cost, raising concerns about their validity in unsupervised settings. The second paper, "How Far Will They Go? Red-Teaming Online Influence with Large Language Models," introduces a framework to audit the political steerability of open-source LLMs, finding that they often express left-leaning content and that their political range can be expanded through jailbreaking techniques. AI
IMPACT LLMs are increasingly capable of automating tasks previously thought to require human effort, and their political expressivity requires careful auditing to prevent misuse in influence campaigns.
RANK_REASON The cluster contains two academic papers published on arXiv, detailing research into LLM capabilities and potential misuse.
Read on Hugging Face Daily Papers →
- arXiv
- Large Language Models
- LLMs
- Artificial Effort
- How Far Will They Go? Red-Teaming Online Influence with Large Language Models
- open-source LLMs
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