Measuring Faithfulness in Chain-of-Thought Reasoning (2023 · 262 citations)
Step-by-step "Chain-of-Thought" (CoT) prompting makes LLMs more accurate. It is less clear whether the written reasoning actually explains how the model reaches its answer. The authors, mostly at Anthropic, built a set of interventions to test this. They cut the CoT short, inserted an error into it, reworded it, or…
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training (2024 · 123 citations)
The paper asks whether an LLM that has learned a hidden, trigger-dependent harmful policy could be cleaned up by today's standard safety training.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

Measuring Faithfulness in Chain-of-Thought ReasoningarXiv:2307.13702Tamera Lanham, Anna Chen, Ansh Radhakrishnan, et al.2023 · 262 citations
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety TrainingarXiv:2401.05566Evan Hubinger, Carson Denison, Jesse Mu, et al.2024 · 123 citations
Question Decomposition Improves the Faithfulness of Model-Generated Reasoning ↗arXiv (Cornell University) · no summary yetAnsh Radhakrishnan et al.2023 · 74 citations

Co-authors here

12 of 17 co-authors with a page, the most papers together first.

Papers of our library cite their works 778 times (of the 149,439 papers whose bibliographies we read); 237 citing papers with a page here →.

Updated 2 November 2026