Human Alignment

Opening — Why this matters now In an age when AI can outscore most humans in the International Mathematical Olympiad, a subtler question has emerged: can machines care about what they solve? The new study A Matter of Interest (Mishra et al., 2025) explores this psychological fault line—between mechanical brilliance and genuine curiosity. If future AI partners are to co‑invent mathematics, not just compute it, they must first learn what humans deem worth inventing. ...

Words, Not Just Answers: Using Psycholinguistics to Test LLM Alignment For years, evaluating large language models (LLMs) has revolved around whether they get the answer right. Multiple-choice benchmarks, logical puzzles, and coding tasks dominate the leaderboard mindset. But a new study argues we may be asking the wrong questions — or at least, measuring the wrong aspects of language. Instead of judging models by their correctness, Psycholinguistic Word Features: a New Approach for the Evaluation of LLMs Alignment with Humans introduces a richer, more cognitively grounded evaluation: comparing how LLMs rate words on human-centric features like arousal, concreteness, and even gustatory experience. The study repurposes well-established datasets from psycholinguistics to assess whether LLMs process language in ways similar to people — not just syntactically, but experientially. ...