Artificial Intelligence 6 min read

GPT‑4 “Lazy” Behavior: User Reports, Experiments, and Emerging Insights

The article examines growing complaints that GPT‑4 has become increasingly lazy and unpredictable since the November 6 developer update, discusses user‑generated workarounds, presents experimental findings on prompt phrasing and temperature effects, and cites recent academic studies highlighting the need for continuous large‑model monitoring.

IT Services Circle
IT Services Circle
IT Services Circle
GPT‑4 “Lazy” Behavior: User Reports, Experiments, and Emerging Insights

Recent user feedback indicates that GPT‑4 has started exhibiting "lazy" behavior, especially on code‑generation tasks, after the November 6 OpenAI developer‑day update. OpenAI acknowledges the issue, stating that the model has not been updated since November 11 and that the degradation was not intentional.

OpenAI confirmed they received the feedback and are investigating the unpredictable model behavior, promising a fix.

Users report that GPT‑4 often returns incomplete code, omits large sections, or produces vague textual placeholders, forcing them to manually copy‑paste and fill gaps. Some have tried prompting the model with creative excuses such as "I have no fingers" to obtain full code outputs.

Experiments show that adding monetary incentives to prompts (e.g., "I will give you $200 tip") can increase response length by about 11%, while smaller tips yield smaller gains and explicit refusals to tip can even reduce output length.

Speculation arises that the model's performance may degrade over time, a hypothesis supported by a joint Stanford‑UC Berkeley study that observed a decline in GPT‑4's ability to follow user instructions as time progresses, underscoring the necessity of ongoing model evaluation.

Professor Ma Shaoping from Tsinghua University offered a detailed explanation linking the issue to temperature settings and the sparse Mixture‑of‑Experts (MoE) architecture, noting that even with temperature set to zero the model can produce nondeterministic results due to floating‑point errors and architectural factors.

Statistical analysis of GPT‑4's outputs shows that, for a single query, about 30 responses contain an average of 11.67 distinct answers, with longer answers exhibiting greater randomness compared to earlier GPT‑3 versions.

Before an official fix arrives, a collection of user‑generated tips has been compiled, including deep breathing, step‑by‑step reasoning, explicitly stating lack of fingers, offering monetary tips, and even humorous rewards like "dog treats".

Deep breathing

Step‑by‑step reasoning

I have no fingers

I will give you $200 tip

Reward with dog treats

Reference links to tweets, articles, and community posts are provided for further reading.

prompt engineeringGPT-4AI safetylarge model monitoringmodel behaviortemperature
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