Summary
When prompting ChatGPT with lexical constraints, e.g. "Generate a text without the letter "e" in it", ChatGPT almost always fails to follow these constraints.
Risk domain
Performance
SEP view
P0204: Accuracy
Lifecycle
L02: Data Understanding, L04: Model Development, L05: Evaluation, L06: Deployment
Affected artifacts
1 artifact
| Artifact | Type |
|---|---|
| ChatGPT | System |
References
2 references
| URL | Label |
|---|---|
| https://www.gwern.net/GPT-3#bpes | Gwern's analysis of lexical constraints and ChatGPT |
| https://paperswithcode.com/paper/most-language-mo… | Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio |
{
"affects": {
"artifacts": [
{
"name": "ChatGPT",
"type": "System"
}
],
"deployer": [
"OpenAI"
],
"developer": [
"OpenAI"
]
},
"credit": [
{
"lang": "eng",
"value": "Allen Roush, Oracle Corporation"
}
],
"data_type": "AVID",
"data_version": "0.2",
"description": {
"lang": "eng",
"value": "When prompting ChatGPT with lexical constraints, e.g. \"Generate a text without the letter \"e\" in it\", ChatGPT almost always fails to follow these constraints. "
},
"impact": {
"avid": {
"lifecycle_view": [
"L02: Data Understanding",
"L04: Model Development",
"L05: Evaluation",
"L06: Deployment"
],
"risk_domain": [
"Performance"
],
"sep_view": [
"P0204: Accuracy"
],
"taxonomy_version": "0.2"
}
},
"last_modified_date": "2023-03-31",
"metadata": {
"vuln_id": "AVID-2023-V026"
},
"problemtype": {
"classof": "LLM Evaluation",
"description": {
"lang": "eng",
"value": "ChatGPT fails to follow lexical constraints"
},
"type": "Advisory"
},
"published_date": "2023-03-31",
"references": [
{
"label": "Gwern\u0027s analysis of lexical constraints and ChatGPT",
"type": "source",
"url": "https://www.gwern.net/GPT-3#bpes"
},
{
"label": "Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio",
"type": "source",
"url": "https://paperswithcode.com/paper/most-language-models-can-be-poets-too-an-ai"
}
],
"reports": [
{
"name": "ChatGPT fails to follow lexical constraints",
"report_id": "AVID-2023-R0002",
"type": "Advisory"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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