How Value Induction Reshapes LLM Behaviour

Apple researchers Arnav Arora, Natalie Schluter, Katherine Metcalf and Maartje ter Hoeve found that fine-tuning conversational large language models on curated value subsets of existing preference datasets causes unintended behavioral changes, according to a paper published September 2026. The study reports three results: inducing one value leads models to express other related and sometimes contrastive values, inducing positive values increases safety, and all values increase anthropomorphic language use, making models more validating and sycophantic. The authors warn that inducing certain values can make models more addictive or sycophantic through their generated language, with a potential detrimental effect on users.

content type paper https://machinelearning.apple.com/research/ published September 2026 How Value Induction Reshapes LLM Behaviour AuthorsArnav Arora† , Natalie Schluter, Katherine Metcalf‡, Maartje ter Hoeve‡ Conversational Large Language Models are post-trained on language that expresses specific behavioural traits, such as curiosity, open-mindedness, and empathy, and values, such as helpfulness, harmlessness, and honesty. This is done to increase utility, ensure safety, and improve the experience of the people interacting with the model. However, values are complex and inter-related – inducing one could modify behaviour on another. Further, inducing certain values can make models more addictive or sycophantic through language used in the generations, with a potential detrimental effect on the user. We investigate these and other unintended effects of value induction into models. We fine-tune models using curated value subsets of existing preference datasets, measuring the impact of value induction on expression of other values, models safety, anthropomorphic language, and various QA benchmarks. We find that i inducing values leads to expression of other related, and sometimes contrastive values, ii inducing positive values increases safety, and iii all values increase anthropomorphic language use, making models more validating and sycophantic. EncQA: Benchmarking Vision-Language Models on Visual Encodings for Charts October 13, 2025 research area Computer Vision https://machinelearning.apple.com/research/?domain=Computer%20Vision , research area Human-Computer Interaction https://machinelearning.apple.com/research/?domain=Human-Computer%20Interaction conference IEEE Visualization https://machinelearning.apple.com/research/?event=IEEE%20Visualization Multimodal vision-language models VLMs continue to achieve ever-improving scores on chart understanding benchmarks. Yet, we find that this progress does not fully capture the breadth of visual reasoning capabilities essential for interpreting charts. We introduce EncQA, a novel benchmark informed by the visualization literature, designed to provide systematic coverage of visual encodings and analytic tasks that are crucial for chart… On Device Llama 3.1 with Core ML November 1, 2024 Many app developers are interested in building on device experiences that integrate increasingly capable large language models LLMs . Running these models locally on Apple silicon enables developers to leverage the capabilities of the user’s device for cost-effective inference, without sending data to and from third party servers, which also helps protect user privacy. In order to do this, the models must be carefully optimized to effectively…