arXiv:2605.26738v1 Announce Type: new Abstract: Human communication depends on implicit social signals where effectiveness is shaped by tone, context, and conversational norms rather than semantic content alone. We introduce KARMA (Karma-Aligned Reward Model Adaptation), a framework for LLM learning of context-sensitive conversational behavior from large-scale social interaction data. KARMA trains a reward model on Reddit conversations to predict response valuation conditioned on context, and uses this signal to fine-tune language models via reinforcement learning to improve performance on pra
The increasing sophistication of LLMs is hitting a constraint around effective human-like communication, prompting research into more nuanced reward models beyond semantic accuracy.
This research could lead to LLMs that are significantly more effective in complex social and professional interactions, enhancing their utility across various applications where 'how' something is said is as important as 'what' is said.
LLMs' ability to understand and generate context-sensitive, socially appropriate communication will improve, moving beyond purely factual or semantically correct responses.
- · AI developers
- · Customer service platforms
- · Social media companies
- · Digital assistants
- · LLM developers without advanced reward modeling
- · Monotonous chatbots
- · Companies relying on basic conversational AI
LLMs will generate more 'human-like' and socially effective responses in dynamic conversational settings.
This improved conversational capability could increase user adoption and reliance on AI systems for complex interactions.
The blurring lines between human and AI communication might necessitate new ethical guidelines and authentication methods for online interactions.
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