Large Language Models like ChatGPT say The Darnedest Things
We recorded 6 claims from this post. 5 were checked against evidence. 1 can’t be checked. 3 more are held. Read the post at its source.
Claims from this post (6)
In the order they appear in the post. Each quotation is the speaker’s words in this post. A label such as “Supported” or “Can’t be checked” is the claim’s current assessment; its page has the evidence and the reasons.
Assessment: UnresolvedSaid
Observation
“ChatGPT made all the same kinds of mistakes that its predecessors did.”
Our reading: ChatGPT made all the same kinds of mistakes that earlier GPT-3 versions did.
No horizon Evidence as of
Limitation: Selected failures establish occurrence, not prevalence.
Assessment: SupportedSaid
Observation
“It could sometimes produce outputs that were correct and acceptable in these regards but not reliably.”
Our reading: ChatGPT could sometimes produce outputs that were correct and acceptable in the regards listed (counting, arithmetic, event order, physical reasoning, human psychology, fabrication, bias), but not reliably.
No horizon Evidence as of
Limitation: The evidence covers the seven failure types unevenly.
Assessment: SupportedSaid
Observation
“It made things up (figure 6). Its output, when released, exhibited sexist and racist biases (figure 7).”
Our reading: ChatGPT made things up and, at release, exhibited sexist and racist biases.
No horizon Evidence as of
Limitation: The bias examples came from adversarial prompts reported in the press, not from a systematic audit.
Assessment: UnresolvedSaid
Observation
“ChatGPT couldn’t, for example, reliably count to four (figure 1) or do one-digit arithmetic in the context of simple word problem (figure 2).”
Our reading: ChatGPT could not reliably count to four or do one-digit arithmetic in simple word problems.
No horizon Evidence as of
Limitation: The packet provides no independent repeated or sufficiently varied launch-period tests for either specified task family.
Assessment: UnresolvedSaid
Causal claim
“In our opinion, this was inevitable, in a system that has been trained to optimize the goal of producing one plausible-sounding word after another rather than actually engage with the meaning of language.”
Our reading: ChatGPT's errors were inevitable because it is trained to produce plausible next words rather than engage with meaning.
No horizon Evidence as of
Limitation: The claim combines a causal assertion with an undefined account of meaning.
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“In our view, it is past time to have something like this, given the inherent unpredictability and wide range of the systems.”
Our reading: In the authors' view, it is past time to have a community-facing corpus of ChatGPT and other LLM errors, given the systems' inherent unpredictability and wide range.
No horizon
Qualifier: in our view
Held from this post (3)
These records did not pass every check, so nothing about them is published. Held is not a finding.
- The two reviews read the claim differently
- The criteria are ambiguous. Our reading, not assessed: ChatGPT's bias guardrails are imperfect, both too strict and too loose, and can be circumvented.
- The two reviews read the claim differently