Don't ask an LLM for a confidence score
The author argues that LLM-generated confidence scores are unreliable and lack scientific validity. While models exhibit some internal state and occasional self‑correction, they cannot accurately quantify their own correctness. The piece urges skepticism toward confidence scores and highlights the ambiguity of what “confidence” actually measures.
- ▪Asking an LLM to output a numeric confidence score is essentially useless because it is not reliably grounded in the model’s actual certainty.
- ▪Research shows that models have latent internal states, but these are highly context‑dependent and insufficient for accurate self‑assessment.
- ▪Studies such as DeepMind’s have found that self‑correction without external feedback often degrades performance rather than improves it.
- ▪The term “confidence” is ambiguous, potentially referring to correctness, coherence, or goal fulfillment, making a single score misleading.
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Record
| Original publisher | Justin Flick |
| Canonical URL | https://justinflick.com/2026/07/27/llm-confidence-scores.html |
| Publication time | Tue, 28 Jul 2026 00:06:53 +0000 |
| Retrieval time | 2026-07-28T00:13:08.086Z |
| Last seen | 2026-07-28T00:13:08.086Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | i_ls_AaHvk_n · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Why I Hate LLM Confidence Scores July 27, 2026 Hello, Reader If someone sent you this post, you have probably tried to extrude a confidence score out of an LLM. I’ve now had this conversation at multiple companies with multiple people, and I’ve had it enough times that it seems like there’s a broader misunderstanding at work here. So my hope is that I can just send people this post instead of relitigating it in a thread every six months. The short version: asking an LLM to generate a score for how confident it is in its own response is, from everything I can tell, completely useless. I want to be upfront that I’d love to be wrong about this. There are people much, much smarter than me working in this space (and I work with many of them!).
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Justin Flick.
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