R0053/2026-03-31-02/C003/SRC01

Research	R0053 — Prompt Claims
Run	2026-03-31-02
Claim	C003
Search	S02
Result	S02-R02
Source	SRC01

Sharma et al. — foundational paper on LLM sycophancy (ICLR 2024)

Source

Field	Value
Title	Towards Understanding Sycophancy in Language Models
Publisher	ICLR 2024
Author(s)	Mrinank Sharma et al.
Date	2023 (updated May 2025)
URL	https://arxiv.org/abs/2310.13548
Type	Research paper

Summary

Dimension	Rating
Reliability	High
Relevance	High
Bias: Missing data	Low risk
Bias: Measurement	Low risk
Bias: Selective reporting	Low risk
Bias: Randomization	N/A — not an RCT
Bias: Protocol deviation	N/A — not an RCT
Bias: COI/Funding	Some concerns

Rationale

Dimension	Rationale
Reliability	Published at ICLR 2024, a top ML venue. Multi-author with rigorous methodology.
Relevance	Directly studies the sycophancy phenomenon described in the claim.
Bias flags	Some COI concerns — authors affiliated with Anthropic, which has commercial interest in understanding sycophancy to improve its product.

Evidence Extracts

Evidence ID	Summary
SRC01-E01	Systematic sycophancy across five AI models and four tasks