{"id":243728,"date":"2026-09-07T09:04:00","date_gmt":"2026-09-07T14:04:00","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2026\/09\/causal-evidence-that-language-models-use-confidence-to-drive-behaviour"},"modified":"2026-09-07T09:04:00","modified_gmt":"2026-09-07T14:04:00","slug":"causal-evidence-that-language-models-use-confidence-to-drive-behaviour","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2026\/09\/causal-evidence-that-language-models-use-confidence-to-drive-behaviour","title":{"rendered":"Causal evidence that language models use confidence to drive behaviour"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/causal-evidence-that-language-models-use-confidence-to-drive-behaviour.jpg\"><\/a><\/p>\n<p>Researchers have long known that AI models generate hidden \u201cconfidence signals\u201d when they process information. But a fundamental question remained: Does the AI actually use this internal sense of confidence to decide whether to answer a question or simply say, \u201cI don\u2019t know\u201d? 3. The Proof: To prove this wasn\u2019t just a coincidence, researchers directly intervened in the AI\u2019s internal workings, essentially turning its \u201cconfidence volume\u201d up or down. When they artificially boosted the AI\u2019s confidence, it answered more questions. When they suppressed it, the AI abstained more often. This provided direct, causal proof that the model\u2019s confidence level is what drives its decision to speak up or stay quiet. Interestingly, the study found that the AI expresses confidence in two ways: a mathematical one (based on how it calculates the probability of its next word) and a verbal one (when it explicitly states, \u201cI am 80% sure\u201d). The researchers discovered that both of these are actually just simplified glimpses into a much richer, more complex internal understanding of the AI\u2019s own uncertainty. 1. \u201cSaying\u201d it is confident isn\u2019t as accurate as \u201cBeing\u201d confident The study makes a crucial distinction between the AI\u2019s internal mathematical confidence and its *verbal* confidence (when the AI literally types out \u201cI am highly confident in this answer\u201d). The researchers found that verbal confidence is a \u201clossy read-out\u201d and is actually less accurate at predicting whether the AI is right or wrong than its hidden internal probabilities. In short: just because an AI tells you it is certain doesn\u2019t mean its internal mechanics actually back that up. We shouldn\u2019t blindly trust an AI\u2019s explicit claims of certainty. 2. We still can\u2019t see the full \u201cBlack Box\u201d Large language models are trained heavily on human feedback (a process called Reinforcement Learning from Human Feedback, or RLHF). During training, human reviewers often reward the AI for saying \u201cI don\u2019t know\u201d when a question is too difficult, rather than making up a false answer (hallucinating). A major ongoing debate in AI research is whether these models have genuinely developed an organic internal sense of uncertainty, or if they are simply executing highly advanced pattern-matching to mimic the abstention behaviors that human trainers rewarded them for during development.<\/p>\n<hr>\n<p>Kumaran <i>et al.<\/i> show that large language models making decisions on when to answer a question or abstain from answering can be influenced by boosting or suppressing confidence signals in the model.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers have long known that AI models generate hidden \u201cconfidence signals\u201d when they process information. But a fundamental question remained: Does the AI actually use this internal sense of confidence to decide whether to answer a question or simply say, \u201cI don\u2019t know\u201d? 3. The Proof: To prove this wasn\u2019t just a coincidence, researchers directly [\u2026]<\/p>\n","protected":false},"author":709,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2229,6],"tags":[],"class_list":["post-243728","post","type-post","status-publish","format-standard","hentry","category-mathematics","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/243728","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/709"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=243728"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/243728\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=243728"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=243728"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=243728"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}