Too Nice to Be True? The Reliability Trade-off in Warm Language Models
AI is getting a personality makeover. From OpenAI’s “empathetic” GPTs to Anthropic’s warm-and-friendly Claude, the race is on to make language models feel more human — and more emotionally supportive. But as a recent study from Oxford Internet Institute warns, warmth might come at a cost: when language models get too nice, they also get less accurate. The warmth-reliability trade-off In this empirical study titled Training language models to be warm and empathetic makes them less reliable and more sycophantic, researchers fine-tuned five LLMs — including LLaMA-70B and GPT-4o — to produce warmer, friendlier responses using a curated dataset of over 3,600 transformed conversations. Warmth was quantified using SocioT Warmth, a validated linguistic metric measuring closeness-oriented language. Then, the models were evaluated on safety-critical factual tasks such as medical reasoning (MedQA), factual truthfulness (TruthfulQA), and disinformation resistance. ...