Claim LD01-SL-C18 (v1.0)
McCoy and colleagues predicted that, because language models are trained to predict the next word, their accuracy should depend on how probable the task, input and output are, even where probability should not matter; GPT-4 decoded a simple cipher with 51% accuracy when the output was a high-probability word sequence and 13% when it was low-probability.
Research opportunities this claim motivates
What this claim leaves open for research. Each is a hypothesis about a gap, not a finding.
- Do the failure patterns of 2023–2024 language models persist in reasoning models?Accuracy tracks output probability in GPT-4 (51% against 13% on a cipher).
Provenance and status
- Claim type
- ESTIMATE + INTERPRETATION (as printed: “ESTIMATE / INTERPRETATION”)
- Publication status
- ELIGIBLE (PREPRINT) (as printed: “ELIGIBLE WITH PREPRINT QUALIFIER”)
- Verification state
- CONTENT_VERIFIED_PRIMARY (PREPRINT) (as printed: “CONTENT VERIFIED PREPRINT”; verified by: UNKNOWN)
- Does not license
- A claim that models cannot do these tasks, or that they lack understanding; the authors say the teleological view can be incomplete
- Source (field 6, as printed)
- McCoy, Yao, Friedman, Hardy & Griffiths, “Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve,” arXiv:2309.13638, 2023
- Source type (field 7)
- PREPRINT (as printed: “Preprint (a revised version was published in PNAS)”)
- Institutional position
- Independent academic (Princeton University)
- Maturity (claim evidence / publication maturity)
- EMERGING (as printed: “EMERGING”)
- Recorded date
- 2026-10-16
- Currentness / correction
- This is the current version. No correction or concern relation is recorded on its Sources.
Version
Current version v1.0, resolved through the explicit current pointer CLAIM--LD01-SL-C18.
Pinned Sources (field 6, version-specific)
- “McCoy, Yao, Friedman, Hardy & Griffiths, “Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve,” arXiv:2309.13638, 2023” → SRC-SL-MCCOY-ET-AL-EMBERS-ARXIV-2309-13638--v1.0 — McCoy, Yao, Friedman, Hardy & Griffiths, “Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve,” arXiv:2309.13638, 2023; class PREPRINT (explicit public provenance pin)
All 25 canonical Claim fields (raw values and normalized values)
| # | Canonical field | Canonical value (verbatim) | Normalized (Claim Value Vocabulary v1.0.2) |
|---|---|---|---|
| 1 | CLAIM ID | LD01-SL-C18 | — |
| 2 | PUBLIC-FACING CLAIM | McCoy and colleagues predicted that, because language models are trained to predict the next word, their accuracy should depend on how probable the task, input and output are, even where probability should not matter; GPT-4 decoded a simple cipher with 51% accuracy when the output was a high-probability word sequence and 13% when it was low-probability. | — |
| 3 | CLAIM TYPE | ESTIMATE / INTERPRETATION | base_types: ESTIMATE, INTERPRETATION |
| 4 | RESEARCH STRAND | B — Evidence that could tell the accounts apart | — |
| 5 | MATURITY | EMERGING | states: EMERGING |
| 6 | SOURCE | McCoy, Yao, Friedman, Hardy & Griffiths, “Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve,” arXiv:2309.13638, 2023 | source_ids: SRC-SL-MCCOY-ET-AL-EMBERS-ARXIV-2309-13638 |
| 7 | SOURCE TYPE | Preprint (a revised version was published in PNAS) | classes: PREPRINT |
| 8 | INSTITUTIONAL POSITION | Independent academic (Princeton University) | — |
| 9 | SCOPE | Eleven deterministic tasks in English; models available in 2023 | — |
| 10 | DESIGN | Experiments varying the probability of tasks, inputs and outputs | — |
| 11 | SAMPLE / DATASET | Eleven tasks | — |
| 12 | MODEL / TOOL / VERSION | GPT-3.5 and GPT-4 (the PNAS version adds models including Claude 3) | — |
| 13 | COMPARATOR | High- and low-probability versions of the same task | — |
| 14 | MAIN RESULT | Accuracy tracks probability: 51% against 13% on the cipher task; the authors state comparative claims (“X is harder than Y”), not impossibility claims | — |
| 15 | INFERENTIAL TARGET | Whether the training objective leaves predictable traces in behaviour | — |
| 16 | DOES NOT LICENSE | A claim that models cannot do these tasks, or that they lack understanding; the authors say the teleological view can be incomplete | — |
| 17 | VERIFICATION STATE | CONTENT VERIFIED PREPRINT | state: CONTENT_VERIFIED_PRIMARY · qualifiers: PREPRINT · verified_by: UNKNOWN |
| 18 | LIMITATIONS | Read in arXiv version 1 (24 September 2023), which covers GPT-3.5 and GPT-4 with basic prompting; the PNAS version (2024) was not compared; frontier models have changed since | — |
| 19 | CONTRADICTORY / SUPPORTING SOURCES | Other limits of the same kind: LD01-SL-C19, LD01-SL-C20 | — |
| 20 | GEOGRAPHY / LANGUAGE | English task set | — |
| 21 | DISTRIBUTIONAL LEDGER | Not analysed in this claim | — |
| 22 | R01 / FIELD-MAP LINKS | LD-01, T4, D3 | — |
| 23 | WHAT WOULD CHANGE THIS CLAIM? | Newer models whose accuracy no longer depends on output probability | — |
| 24 | PUBLICATION STATUS | ELIGIBLE WITH PREPRINT QUALIFIER | state: ELIGIBLE · qualifiers: PREPRINT |
| 25 | RECORDED DATE | 2026-10-16 | — |
Field 22 references resolved against Field Map v0.2: LD-01 (LIVING_DEBATE); T4 (TENSION); D3 (DOMAIN)
Cited by Living Debate versions
Research-of-record provenance
Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 223bc3555dceffe8e0d57dc94addd557e1768135f9000d2e710586d1e6adf1ca.
Repository-authored inputs (not Drive exports)
- SocioLens claim — LD01-SL-C18 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-16; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 16 October 2026 to build the LD-01 debate; a new Claim, first version, written from the project's LD-01 source notes, checked against full texts, official web pages or official abstracts. Block “ENTRY 18 — LD01-SL-C18” SHA-256
47f71682e57f155bf74d1d7a157663abd44c0a428d2b1124827a38795fbe5a75; fixturefixtures/authored/claims/LD01-SL-C18--v1.0.
Builder decisions (not Drive data)
- VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD01-SL-C18 v1.0); the first version of a new Claim of the LD-01 debate
- CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD01-SL-C18 v1.0" (Project Lead (Claude, AI), 2026-10-16); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 16 October 2026 to build the LD-01 debate; a new Claim, first version, written from the project's LD-01 source notes, checked against full texts, official web pages or official abstracts
- FIELD_6_SOURCE_REFERENCE = SRC-SL-MCCOY-ET-AL-EMBERS-ARXIV-2309-13638--v1.0 — basis: field 6 of ENTRY 18 — LD01-SL-C18 cites "McCoy, Yao, Friedman, Hardy & Griffiths, “Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve,” arXiv:2309.13638, 2023"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
- SOURCE_PROVENANCE_PIN = SRC-SL-MCCOY-ET-AL-EMBERS-ARXIV-2309-13638--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "McCoy, Yao, Friedman, Hardy & Griffiths, “Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve,” arXiv:2309.13638, 2023".