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.

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

Pinned version v1.0 (entry LD01-SL-C18--v1.0) — this page never floats to newer evidence.

This pinned version is also the current version.

Versions:v1.0 · current

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

LD-01 v0.1

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; fixture fixtures/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".