Textual Time Travel: A Temporally Informed Approach to Theory of Mind
Bibliographic record
Abstract
Natural language processing systems such as dialogue agents should be able to reason about other people's beliefs, intentions and desires.This capability, called theory of mind (ToM), is crucial, as it allows a model to predict and interpret the needs of users based on their mental states.A recent line of research evaluates the ToM capability of existing memoryaugmented neural models through questionanswering.These models perform poorly on false belief tasks where beliefs differ from reality, especially when the dataset contains distracting sentences.In this paper, we propose a new temporally informed approach for improving the ToM capability of memory-augmented neural models.Our model incorporates priors about the entities' minds and tracks their mental states as they evolve over time through an extended passage.It then responds to queries through textual time travel-i.e., by accessing the stored memory of an earlier time step.We evaluate our model on ToM datasets and find that this approach improves performance, particularly by correcting the predicted mental states to match the false belief.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".