A tricky method for NLP and LLM from my previous research work

Research
1

This post is from my research work in NLP.
-The background of this work.
I did a research on an AI system that can help professors to test reports of university students.
The main task of this system was to find out whether a report is correct and well organized in logic.
This system was supposed to give outputs such as: "There is a logical skip between paragraph C and D ", “There is a logical error from Para B to Para E.”…
My idea was this:
-Logical base
The mathematical(or logical) analysis or model of this problem is about Necessary condition and Sufficient condition. (I am pretty sure that every friend in this community is aware of this logic.) Here comes my main idea or trick.
I took the normal training process and inferencing in models as A->B(Necessary condition). Then what about B->A(Sufficient condition)? The answer: Reverse(In my team we called this reverse-from-result training or simply reverse-result-training) training and inferencing was taken as B->A(Sufficient condition).
This means that training or inferencing first follow the normal path then continues from the normal path result to the beginning of normal path. Then the model can compare reports(inputs) and inferences. For example the similarity is bigger than 0.8, the report is logically fine. If similarity of paragraph C of the report is 0.7-0.5, there is a logical skip after paragraph C of the report. If similarity is less than 0.5, there is a logical error from Para C to D of the report.
-Conclusion
This method can be developed into a new mechanism or replace a part of LLM model to reduce Hallucination in LLMs.
Feel free to use this idea and method for any purpose since there will be no copyrights conflicts for this idea and I am open to any discussions and feedbacks on this method.

2

Hmm… For now, how about something like this:

My reading is that there is a testable idea here, especially if reverse-result-training is framed as a diagnostic round-trip signal rather than, at least initially, as a proof that a report is logically correct.

A forward/reverse pipeline may reveal places where information, support, or structure is not preserved. That could be useful for locating passages that deserve review. However, a high reconstruction similarity would mainly show stability under that particular pipeline; by itself, it would not yet distinguish among:

  • coherent writing,
  • a sufficient argument,
  • a formally valid inference,
  • a factually correct statement, and
  • a model that has simply learned an easy way to reconstruct its own input.

For future search and comparison, I would keep your term reverse-result-training, while connecting it to nearby terms such as round-trip reconstruction, cycle consistency, dual learning, argument sufficiency, missing-premise reconstruction, and bidirectional verification. These are related families, not necessarily the same method.

A practical default route

The most manageable first version would probably be:

  1. Choose one target phenomenon, such as a missing premise or an unsupported conclusion, rather than trying to detect every kind of “logical error” at once.
  2. Define A and B operationally. A useful default could be A = premises/supporting units and B = claim or conclusion.
  3. Evaluate argumentative units and relations, not only raw paragraph similarity. A paragraph can contain background, a claim, evidence, a counterclaim, and a conclusion at the same time.
  4. Keep the round-trip score as one signal among several, alongside entailment, contradiction, discourse/argument relations, and human judgments.
  5. Calibrate the thresholds on labeled examples rather than assuming that 0.5 or 0.8 transfers across models, languages, document lengths, or similarity functions.
  6. Use an uncertain/review state. A practical system could leave high-confidence normal cases unflagged, flag high-confidence anomalies, and defer conflicting cases to a professor or reviewer.

That route would make the method useful even before it is reliable enough to act as an automatic grader. The output could be something like:

Location: between paragraphs C and D
Possible issue: missing premise
Observed evidence: paragraph D introduces X as a cause of Y,
but the preceding text does not establish a relation between X and Y
Confidence: medium
Suggested review: add supporting evidence or weaken the conclusion

The most useful first artifact

If the original implementation, report, or notes are still available, one complete worked example would make the proposal much easier to understand and reproduce:

Original A
Forward prompt / model / transformation
Generated B
Reverse prompt / model / transformation
Reconstructed A'
Similarity function
Paragraph- or unit-level scores
Human interpretation of the suspected gap

This would clarify whether “training” is a learned cycle objective, two jointly trained models, data augmentation, or an inference-time check using already trained models. It would also clarify whether A and B are paragraphs, premises and conclusions, natural language and a structured representation, or something else.

Nearby research and terminology

1. Round-trip reconstruction and cycle consistency

The broad mechanism resembles work on round-trip or cycle consistency: transform A into B, reconstruct A from B, and use the reconstruction result as a learning or evaluation signal. Dual Learning for Machine Translation is a well-known example of connecting two opposite-direction tasks in a closed loop.

The important difference is that translation has a comparatively clear pair of opposite tasks. In argumentative writing, a conclusion may have many possible valid supporting premises, and a paragraph may have many valid successors. Therefore, the reverse operation is often not a unique inverse.

A useful interpretation may be:

A low round-trip score identifies a candidate for inspection; an independent evaluation determines what kind of problem, if any, is present.

2. Argument sufficiency through conclusion generation

Assessing the Sufficiency of Arguments through Conclusion Generation tests a closely related hypothesis: if premises provide sufficient support, a model should be able to generate the conclusion from those premises. This is especially relevant if your A and B mean premises and conclusion.

That work reported strong sufficiency classification results and found that the generated conclusions could be qualitatively good, while also noting that the generated conclusions had limited ultimate impact on the classifier. That seems like a useful caution: generation can provide evidence about the premise–conclusion relation without necessarily being sufficient as the whole evaluator.

A different route is CASA: Causality-driven Argument Sufficiency Assessment, which asks whether introducing the premise makes the conclusion more likely in generated counterfactual contexts. It is not a round-trip method, but it provides a useful alternative question:

Does the proposed premise actually change the support for the conclusion?

3. Missing-premise and enthymeme reconstruction

If “logical skip” means that a reader needs an unstated premise to move from one claim to the next, the closest task may be enthymeme detection and reconstruction.

Mind the Gap: Automated Corpus Creation for Enthymeme Detection and Reconstruction in Learner Arguments introduces tasks for detecting gaps in learner arguments and reconstructing the missing argumentative discourse unit. This is very close to the educational use case, although its data-generation method and its definition of a gap may not match your original system exactly.

This connection could make “logical skip” more testable by separating it from other problems such as:

  • a sudden topic shift,
  • an unclear reference,
  • a contradiction,
  • weak evidence,
  • a missing rebuttal,
  • a causal reversal, or
  • a factually incorrect statement.

4. Coherence detection, reasons, and rewriting

DECOR treats student-writing support as three related tasks: detecting incoherence, identifying a reason, and rewriting the problematic sentence. Its official repository includes the data format and evaluation pipelines for detection, reasoning, and rewriting.

That decomposition may be useful for the output contract of a reverse-result system:

  1. Where is the suspected problem?
  2. What kind of problem is it?
  3. What textual evidence supports the warning?
  4. What repair would preserve the writer’s intended argument?

A single similarity score can help with the first step, but the remaining steps may require separate models or rubrics.

5. Logical quality as a multidimensional property

ReportLogic is aimed at deep-research reports rather than student essays, but its taxonomy is relevant. It separates:

  • Macro-Logic: whether the report has a coherent analytical arc,
  • Expositional-Logic: whether the reader receives the context needed to follow the progression, and
  • Structural-Logic: whether conclusions can be traced to explicit claims and support.

The paper also tests robustness against superficial cues such as verbosity. The ReportLogic repository currently contains code and evaluation material, although its README says the dataset is still under internal review and not yet fully released.

This suggests that “logical skip” may need more than one subtype. A missing explanation and an unsupported conclusion can look similar to a reader but require different repairs.

The meaning changes depending on A and B
Interpretation What the round trip could test Main limitation Useful comparison
A = premises, B = conclusion Whether the conclusion preserves or can recover essential support Several premise sets may support the same conclusion Argument sufficiency, missing-premise detection
A = paragraph C, B = paragraph D Local transition stability There may be many valid next paragraphs and many valid predecessors Discourse relation and local-coherence evaluation
A = report, B = argument graph Whether claims, premises, and support/attack relations preserve the report’s argumentative content Argument mining errors become part of the pipeline AAE2, PERSUADE, argument mining
A = report, B = diagnostic explanation Whether the explanation contains enough information about the report A diagnostic explanation is not normally intended to reconstruct the source report Feedback faithfulness and evidence attribution
A = answer, B = verification questions/results Whether claims survive an independent verification process This is verification rather than a true inverse Hallucination and factuality evaluation

If A and B are adjacent paragraphs

A raw similarity score may be especially difficult to interpret, because low similarity can be correct. For example:

  • a claim may be followed by numerical evidence,
  • a problem statement may be followed by a proposed mechanism,
  • a counterclaim may be followed by a rebuttal,
  • a method section may be followed by results.

These transitions can introduce many new words while remaining coherent. Conversely, a highly similar paragraph may merely repeat the preceding claim without advancing the argument.

When Argumentation Meets Cohesion is useful background here: cohesion features interact with argumentative roles, so the expected relationship is not necessarily the same for claim-to-claim, claim-to-evidence, counterclaim-to-rebuttal, and method-to-result transitions.

A stronger paragraph-level formulation may therefore be:

1. Identify the role of paragraph C and paragraph D.
2. Predict the expected discourse or argument relation.
3. Test whether D satisfies that relation.
4. Use the round-trip score as an additional anomaly signal.

If A and B are premises and conclusions

This is probably the cleanest first formulation. It supports controls such as:

  • remove one premise,
  • add an irrelevant premise,
  • reverse cause and effect,
  • weaken or strengthen the conclusion,
  • replace a valid warrant with a contradictory warrant.

The score can then be compared with human judgments of relevance and sufficiency.

If B is a structured argument representation

A structured intermediate representation could make the round trip more interpretable:

report
→ claims / premises / evidence / counterclaims
→ support and attack relations
→ reconstructed explanation or report

The AAE2 dataset card on Hugging Face provides student persuasive essays annotated with MajorClaim, Claim, Premise, supports, and attacks relations. The PERSUADE corpus provides a much larger collection of argumentative essays annotated for discourse elements, with double-blind expert rating and adjudication.

These datasets do not provide a universal gold label for “logical truth,” but they can provide existing component and relation labels instead of requiring the entire structure to be invented from scratch.

What similarity can and cannot establish

A reconstruction score may provide evidence for:

  • preservation of information,
  • semantic relatedness,
  • stability under a particular pair of models or prompts,
  • unusual passages relative to a calibrated dataset.

It does not by itself establish:

  • that the premises are true,
  • that the inference is formally valid,
  • that the evidence is sufficient,
  • that the statement is factually correct,
  • that the reverse mapping is unique,
  • that a high-scoring model used the intended reasoning process.

High similarity can hide important errors

The treatment reduced mortality by 10%.
The treatment increased mortality by 10%.

A causes B.
B causes A.

The study was conducted in 2024.
The study was conducted in 2025.

All mammals are warm-blooded.
All warm-blooded animals are mammals.

These pairs share most of their words, but the differences are central to the claim.

Low similarity can still be correct

Claim:
Remote work can reduce urban traffic.

Evidence:
A transport survey found fewer commuter journeys on days
with high remote-work participation.

The evidence introduces new entities and measurements, so lexical or embedding similarity may decrease even though the relation is appropriate.

The metric itself should be tested

CheckList and its reference implementation provide a behavioral-testing approach: instead of looking only at average accuracy, construct examples that target a specific capability.

Perturbation CheckLists for Evaluating NLG Evaluation Metrics applies the same idea to evaluation metrics and reports that no single tested metric worked well across all desired criteria. This seems directly relevant: the reconstruction similarity function should itself be treated as a component under test, not as a fixed ground truth.

A useful rule would be:

A metric should change when the targeted property changes, and remain stable when an irrelevant surface property changes.

A small sanity-check suite

Before training a new model, a compact controlled set could reveal what the score is actually measuring.

Test Intended property Expected behavior
Meaning-preserving paraphrase Robustness to wording Score remains relatively stable
Negation insertion Polarity sensitivity Contradiction/error signal increases
Number/date/entity replacement Factual-detail sensitivity Support or equivalence score decreases
Cause/effect reversal Direction of relation Logical-relation score decreases
Remove one necessary premise Sufficiency Gap signal increases
Add an irrelevant premise Relevance Relevance score decreases
Add a valid alternative premise Non-unique reverse mapping Exact reconstruction may change without becoming invalid
Same topic, different claim Topic shortcut Semantic similarity and claim support should separate
Repeat A almost verbatim as B Copy shortcut Round-trip score may rise without improving the argument
Reorder coherent paragraphs Global organization Document-level coherence should decrease
Internally coherent false premise Coherence vs. factuality Internal score may stay high while factual verification fails

I would also compare several configurations:

Configuration What it tests
Same model, same prompt family Basic reproducibility
Same model, substantially different prompts Prompt dependence
Different checkpoints of one family Checkpoint sensitivity
Different model families Shared-model bias
Generative reverse model plus non-generative NLI checker Method diversity
Model signals plus human rubric Practical validity

For every run, preserving the individual examples is more useful than reporting only one average score. The informative cases are often disagreements:

  • high similarity, human-labeled error;
  • low similarity, human-labeled valid transition;
  • reconstruction and NLI disagree;
  • human annotators disagree;
  • the result changes across model families.
A possible evaluation protocol

Phase 1: Define a small error taxonomy

A first taxonomy might include:

  • no issue,
  • missing premise,
  • unsupported conclusion,
  • irrelevant support,
  • contradiction,
  • topic discontinuity,
  • causal reversal,
  • overgeneralization,
  • unclear reference,
  • factual error.

Not every project needs all of these. A narrow first benchmark with no issue, missing premise, contradiction, and unsupported conclusion may be easier to interpret.

Phase 2: Preserve human evidence, not only labels

For each annotated case, it would be useful to retain:

annotator label
highlighted source span
reason for the label
confidence
suggested missing information or repair

This matters because argument quality is partly perspectival. Two competent readers can agree that a passage is weak while disagreeing about whether the cause is missing evidence, an unstated warrant, or an overly strong conclusion.

Phase 3: Record independent signals

Rather than immediately compressing everything into one number, record:

  • round-trip reconstruction score,
  • semantic similarity,
  • A-entails-A-prime,
  • A-prime-entails-A,
  • contradiction score,
  • predicted discourse relation,
  • premise–conclusion support,
  • external factual support where applicable,
  • human label and confidence.

Bidirectional entailment can help distinguish approximate equivalence from omission or addition, but it should still be treated as a model prediction rather than a proof.

Phase 4: Calibrate thresholds

If 0.5 and 0.8 came from the original experiments, the associated model, similarity function, language, dataset, and document segmentation would be valuable context. If they were illustrative values, they could be replaced with thresholds selected on a development set and then checked on a held-out set.

Useful reporting would include:

  • precision and recall per error type,
  • false-negative rate for serious errors,
  • false-positive warning rate,
  • localization accuracy,
  • calibration by score range,
  • performance by document length and domain.

Phase 5: Add an abstention/review policy

Instead of forcing every case into “fine / skip / error,” the operational output could be:

no flag
flag with reason
uncertain — human review

This also allows the system to be evaluated as review triage:

  • What percentage of passages can be left unflagged?
  • How many real gaps are missed?
  • How many unnecessary warnings are produced?
  • How much professor review time is saved?

Phase 6: Evaluate the feedback, not only the detection

For educational use, the final question is not only whether the system found an error. It is whether the feedback helps a student revise the argument without changing the intended position.

Possible criteria include:

  • correct location,
  • correct issue category,
  • explanation grounded in the student’s text,
  • actionable repair,
  • preservation of author intent,
  • appropriate uncertainty,
  • measurable improvement after revision.

Existing data that may reduce setup work

Resource Useful labels/tasks Main limitation for this proposal
AAE2 on Hugging Face MajorClaim, Claim, Premise, supports, attacks Argument structure, not factual truth or a round-trip score
PERSUADE Large-scale student discourse-element annotations Discourse roles do not directly equal logical validity
Mind the Gap Missing argumentative-unit detection and reconstruction Artificial deletion process may not cover every natural gap
DECOR Incoherence detection, reason, and rewrite L2 coherence is broader than premise–conclusion validity
Hallucination is a related but separate branch

The hallucination idea seems plausible as a later branch, but it changes the target property.

A report may be internally coherent and still be factually false. A model can also reproduce the same mistaken claim consistently in both directions. Therefore:

round-trip consistency ≠ external factual verification

A round-trip signal may still help detect unstable generations, but factual hallucination generally needs an additional independent signal such as:

  • retrieval from trusted sources,
  • claim decomposition,
  • source-grounded entailment,
  • independently answered verification questions,
  • citation verification,
  • cross-model checking.

Chain-of-Verification is a useful comparison. It drafts an answer, plans verification questions, answers those questions independently, and then produces a revised answer. This is not the same as reverse-result-training, but it illustrates why independence between generation and verification can matter.

A reasonable staged roadmap would be:

  1. demonstrate that the round-trip score localizes a defined class of argument gaps;
  2. test whether the signal survives model and prompt changes;
  3. add an independent support or factuality checker;
  4. evaluate whether the combined system catches errors that either component misses alone.
Implementation and reproducibility options

A minimal implementation does not need to begin with a large jointly trained architecture. It could begin as an evaluation harness around existing models.

Suggested component boundaries

Segmenter
→ argument/discourse unit extractor
→ forward transformation
→ reverse transformation
→ independent checkers
→ calibration and abstention layer
→ evidence-grounded feedback renderer

Keeping these components separate makes it possible to replace one model without changing the definition of the task.

Logs worth preserving

input text and unit boundaries
model and exact revision
prompt templates
generation parameters and random seeds
forward output
reverse output
all component scores
retrieved evidence spans
final decision and explanation
human labels

Evaluation tooling

The evaluation can be implemented with a small custom script. For a larger experiment, Hugging Face Lighteval currently supports custom tasks, custom metrics, multiple inference backends, and sample-by-sample result analysis. It is only one possible harness; the important part is retaining per-example outputs and versioned configurations.

For behavioral tests, the CheckList repository contains templates, perturbation utilities, custom expectations, and examples for testing Hugging Face transformer pipelines.

Reproducibility controls

Because this method uses several model-dependent stages, it would help to report:

  • exact model IDs and revisions,
  • maximum context length and truncation behavior,
  • paragraph/unit segmentation,
  • deterministic versus sampled decoding,
  • number of reverse samples,
  • aggregation rule,
  • similarity model and pooling method,
  • language and subject domain.

A threshold is interpretable only together with this context.

A small terminology note

Under the usual logical convention, if A implies B, A is a sufficient condition for B and B is a necessary condition for A. I may be reading your notation differently, though, because the main idea here seems to be the forward/reverse computational path rather than a claim that the model transformation is a formal implication.

I would therefore treat this as a terminology clarification, not as the central question. The more important issue is defining what information B must preserve about A, and what independent evidence would show that the preservation corresponds to argument quality.

Overall, I think the idea becomes strongest when stated modestly but operationally:

Reverse-result-training may provide an additional signal for locating possible argument gaps or unsupported transitions, provided that A and B are explicitly defined, the score is calibrated against human-labeled cases, and logical/factual checks are kept separate from reconstruction similarity.

If the original system already produced paragraph-level scores, the most direct comparison would be against a small labeled set of missing-premise, contradiction, and unsupported-conclusion cases. If the original work was mainly conceptual, the lowest-cost starting point would be the controlled perturbation suite above. Either route would show what the score measures before committing to a larger training architecture.