arXiv:2601.02813v3 Announce Type: replace-cross Abstract: Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize. As a result, improvements in human-like behavior are largely driven by scale or broad supervised training, rather than targeted alignment. We introduce Human Aligning LLMs (HAL), a framework for aligning language models to conversational human-likeness using an interpretable, data-driven reward. HAL derives explicit conversational traits from contrastive dialogue data, combines them
The increasing sophistication of LLMs is forcing a focus on qualitative alignment beyond pure performance metrics, making human-likeness a critical frontier.
Achieving more human-like LLMs is crucial for their broader adoption in sensitive applications and for enhancing user trust and engagement, directly impacting their commercial viability and societal integration.
This framework offers a targeted, data-driven approach to an historically elusive aspect of LLM alignment, suggesting more deliberate control over their behavioral traits.
- · AI developers
- · Customer service & interaction sectors
- · Education & entertainment
- · Personalized AI assistant providers
- · LLM developers lacking advanced alignment techniques
- · Tasks requiring highly nuanced human-human interaction
- · Companies relying on easily distinguishable AI vs. human agents
General-purpose LLMs will exhibit significantly more natural and contextually appropriate conversational abilities.
Public perception of AI will increasingly shift towards treating LLMs as sophisticated conversational partners, rather than mere tools.
The pursuit of human-likeness will accelerate ethical debates around AI sentience and the blurring lines between human and machine interaction.
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Read at arXiv cs.CL