4 / papers
Measuring what models do rather than what benchmarks say they do.
Multilingual hallucination in large language models has begun to receive systematic attention. Recent work has measured aggregate hallucination rates across 19 to 30 languages (FACTSCORE-multilingual, MFAVA, Poly-FEVER), introduced…
The detection of AI-generated text under adversarial transformation — particularly paraphrasing — is widely characterised as an unsolved problem. The reality is more structured. Two technically distinct approaches, post-hoc detection and…
Reinforcement learning from human feedback (RLHF) is widely understood to incur an alignment tax: aligning a language model with human preferences can degrade capabilities the base model possessed. This phenomenon is well documented in…
The emergence of Large Language Models (LLMs) has revolutionized our understanding of artificial intelligence's capabilities in emotional processing. These models demonstrate remarkable proficiency in generating emotionally appropriate…
