Recognizing Textual Entailment with Logical Inference.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the goal of producing explainable entailment decisions, and ultimately having the computer understand the sentences it is processing, we have been pursuing a (somewhat) approach to recognizing entailment. First our system performs semantic interpretation of the sentence pairs. Then, it tries to determine if the (logic for) the H sentence subsumes (i.e., is implied by) some inference-elaborated version of the T sentence, using WordNet (including logical representations of its sense definitions) and the DIRT paraphrase database as its sources of knowledge. For pairs where it can conclude or refute entailment, the system often produces explanations which appear insightful, but also sometimes produces explanations which are clearly erroneous. In this paper we present our system and illustrate its good and bad behaviors. While the good behaviors are encouraging, the primary challenges continue to be: lack of lexical and world knowledge; poor quality of existing knowledge; and limitations of using a deductive style of reasoning with imprecise knowledge. Our best scores were: 56.5% (2-way task) and 48.1% (3-way task)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it