LLMs show partial and variable perceptual alignment with human touch on textiles, succeeding on samples like silk satin but failing on cotton denim when matching descriptive language to embedding similarity.
Artificial intelligence, values, and alignment
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
SmoothLLM mitigates jailbreaking attacks on LLMs by randomly perturbing multiple copies of a prompt at the character level and aggregating the outputs to detect adversarial inputs.
Selecting preference pairs whose DPO implicit reward gap is small yields better LLM alignment than random or baseline selection while using only 10% of the data.
citing papers explorer
No citing papers match the current filters.