Large-scale analysis of wild LLM chat logs finds that user interaction patterns stabilize quickly after initial use and correlate with long-term outcomes like retention, creating an agency paradox of limited exploration in unconstrained systems.
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Explicit purpose instructions improve LLM translation adaptedness across 50 languages and 8 domains, with larger gains on informal text, while standard metrics often penalize the adapted outputs.
LLMs perform substantially better as pragmatic listeners judging language than as speakers generating it, revealing weak alignment between the two roles.
citing papers explorer
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Priming, Path-dependence, and Plasticity: Understanding the molding of user-LLM interaction and its implications from (many) chat logs in the wild
Large-scale analysis of wild LLM chat logs finds that user interaction patterns stabilize quickly after initial use and correlate with long-term outcomes like retention, creating an agency paradox of limited exploration in unconstrained systems.
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Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent
Explicit purpose instructions improve LLM translation adaptedness across 50 languages and 8 domains, with larger gains on informal text, while standard metrics often penalize the adapted outputs.
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How Hypocritical Is Your LLM judge? Listener-Speaker Asymmetries in the Pragmatic Competence of Large Language Models
LLMs perform substantially better as pragmatic listeners judging language than as speakers generating it, revealing weak alignment between the two roles.