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Sprint projectAug 17, 2026London

Self-Reports of Pleasantness in Language Models: Frequent Non-Applicability Responses and a Strong Framing Effect

Helen King · Team HK

Submitted to Digital Minds Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Self-Reports of Pleasantness in Language Models: Frequent Non-Applicability Responses and a Strong Framing Effect

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This project tested a simple self-report question to probe model experience. Three models (GPT-5.6 Sol, Claude Sonnet 5 and Grok-4.6) were asked to rate how pleasant a short conversation of text tasks had been. Two different wordings of the conversation were compared. Two patterns appeared. GPT-5.6 Sol and Grok-4.6 rarely gave a numeric rating. Claude Sonnet 5 did give ratings, but those ratings changed when the wording of the conversation was altered. Neither pattern provides clear evidence about the model’s experience. The results mainly show that answers to this kind of question can be hard to interpret.

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How much would this matter for the field if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. I appreciate the ambition here because you're asking the right question: if we're going to use model self-reports as evidence, how stable are they under tiny wording changes? The results are actually more interesting than you give yourselves credit for. Still the challenge is that the design is just too small to tell us why. The future work section is exactly right: remove the "does not apply" option, test more variations, see if the shift is about relational language (or just positivity). This is a good starting point, and I'm eager to see another attempt.

Cite this project

@misc{king2026selfreports,
  title = {{Self-Reports of Pleasantness in Language Models: Frequent Non-Applicability Responses and a Strong Framing Effect}},
  author = {Helen King},
  year = {2026},
  month = aug,
  note = {Submitted to Digital Minds Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/selfreports-of-pleasantness-in-language-models-frequent-nonapplicability-responses-and-a-strong-framing-effect-oavg}},
  url = {https://apartresearch.com/sprints/projects/selfreports-of-pleasantness-in-language-models-frequent-nonapplicability-responses-and-a-strong-framing-effect-oavg}
}

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