arxiv: 2603.18361 · v2 · submitted 2026-03-18 · 💻 cs.CL

Recognition: 1 theorem link

· Lean Theorem

Synthetic Data Generation for Training Diversified Commonsense Reasoning Models

Tianhui Zhang , Bei Peng , Danushka Bollegala

Authors on Pith no claims yet

Pith reviewed 2026-05-15 08:07 UTC · model grok-4.3

classification 💻 cs.CL

keywords synthetic datacommonsense reasoninggenerative commonsense reasoningresponse diversityLLM fine-tuningCommonSyn datasetconversational agents

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The pith

Fine-tuning LLMs on synthetic commonsense data increases both response diversity and quality over human-annotated sets.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing generative commonsense reasoning datasets are small and narrow because human annotation is expensive, limiting training of conversational agents that must handle multiple plausible scenarios. The authors introduce a two-stage synthetic generation process that produces the CommonSyn dataset at larger scale. Fine-tuning large language models on this data improves both the variety of generated responses and their commonsense quality relative to vanilla models and models trained on human-crafted data. The gains appear across LLMs of different sizes.

Core claim

The central claim is that a two-stage synthetic generation process produces the CommonSyn dataset for diversified generative commonsense reasoning, and that models fine-tuned on it jointly increase both generation diversity and quality compared with vanilla models and models fine-tuned on human-crafted datasets across different sized large language models.

What carries the argument

A two-stage synthetic data generation process that yields the CommonSyn dataset for training diversified generative commonsense reasoning.

If this is right

Conversational agents trained this way can cover more alternative scenarios in their responses.
The benefit holds for LLMs ranging from smaller to larger parameter counts.
Synthetic data offers a lower-cost route to scaling commonsense training resources.
The method directly targets the narrow coverage problem of existing human-annotated GCR datasets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same two-stage approach might generate training data for other tasks that require output diversity, such as story continuation or multi-turn dialogue.
If the synthetic data avoids annotator bias, it could better represent edge-case commonsense scenarios that small human teams rarely produce.
Wider adoption would reduce dependence on repeated human annotation campaigns for each new reasoning domain.

Load-bearing premise

The synthetic generation process produces data whose diversity matches real commonsense distributions without introducing artifacts or biases that reduce model performance.

What would settle it

A side-by-side evaluation in which models fine-tuned on CommonSyn show no gain in diversity metrics or quality scores over models fine-tuned on human data.

Figures

Figures reproduced from arXiv: 2603.18361 by Bei Peng, Danushka Bollegala, Tianhui Zhang.

**Figure 1.** Figure 1: Quality–Diversity trade-off for representative models. The x-axis represents the semantic diversity (Self-CosSim, ↑) and the y-axis represents the generation quality (Overall, ↑). While vanilla models (hollow markers) often suffer from either low quality or limited diversity, its fine-tuned version on our synthetic data, COMMONSYN (solid markers), consistently pushes the performance frontier towards the… view at source ↗

**Figure 2.** Figure 2: Trade-off between Quality and Diversity. The [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Figure 3: Evaluation prompts used by GPT-4o to judge model generations in pairwise comparison. Each prompt [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗

**Figure 4.** Figure 4: Prompt template used to expand 2-seed concept sets during synthetic data generation. This instruction [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗

**Figure 5.** Figure 5: Prompts used to generate synthetic sentences from expanded concept sets. The top prompt is shared by [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗

**Figure 6.** Figure 6: Prompt used by the quality scorer (Gemini-2.5-flash) to assign plausibility scores [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗

**Figure 7.** Figure 7: Evaluation prompt used to compare model-generated sentences against human references on the Common [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗

read the original abstract

Conversational agents are required to respond to their users not only with high quality (i.e. commonsense bearing) responses, but also considering multiple plausible alternative scenarios, reflecting the diversity in their responses. Despite the growing need to train diverse commonsense generators, the progress of this line of work has been significantly hindered by the lack of large-scale high-quality diverse commonsense training datasets. Due to the high annotation costs, existing Generative Commonsense Reasoning (GCR) datasets are created using a small number of human annotators, covering only a narrow set of commonsense scenarios. To address this training resource gap, we propose a two-stage method to create the first-ever synthetic dataset CommonSyn for diversified (GCR). The model fine-tuned on our synthetic data jointly increase both generation diversity and quality compared with vanilla models and the model fine-tuned on human-crafted dataset across different size Large Language Models (LLMs)

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper proposes a synthetic data method for diversified commonsense reasoning but lacks the metrics needed to back up its performance claims.

read the letter

The punchline is that this paper proposes a two-stage LLM pipeline to generate synthetic training data for commonsense reasoning models that need to produce diverse responses, and it creates a dataset called CommonSyn. The idea addresses a real gap since existing datasets are small and human-annotated. What the paper does well is lay out a clear method to first generate scenarios and then diversify the responses using the LLM itself. This could in principle expand beyond the narrow coverage of human data without the high cost. The claim that fine-tuning on this data improves both diversity and quality across different LLM sizes is the kind of result that would be useful if it holds. The soft spots are in the evidence. The abstract makes the joint gain claim but provides no metrics, no description of how diversity is measured, no baselines beyond the high-level mention, and no analysis of whether the synthetic data introduces artifacts. The stress-test concern is valid here: since the data comes from an LLM, any diversity might just be surface variation around the same facts the model already favors, rather than genuinely new commonsense alternatives. Without seeing the full experiments and controls, it's impossible to tell if the improvements are real or metric artifacts. This paper is for researchers in NLP working on generative commonsense or conversational AI who are looking for ways to augment training data. A reader interested in data synthesis techniques would get some value from the method description. It deserves a serious referee because the problem is important and the approach is straightforward enough that experiments could clarify the issues quickly. I would recommend sending it to peer review rather than desk rejecting, provided the full paper includes proper evaluation.

Referee Report

2 major / 1 minor

Summary. The paper proposes a two-stage synthetic data generation method to create the CommonSyn dataset for diversified generative commonsense reasoning (GCR). It claims that models fine-tuned on this synthetic data jointly increase both generation diversity and quality compared with vanilla models and models fine-tuned on human-crafted datasets, across different sizes of LLMs.

Significance. If the empirical results hold, the work would be significant for addressing data scarcity in diversified GCR by offering a scalable synthetic alternative to costly human annotations, potentially enabling conversational agents that better handle multiple plausible scenarios.

major comments (2)

[Abstract] Abstract: the central claim that fine-tuning on CommonSyn 'jointly increase both generation diversity and quality' is asserted without any metrics, baselines, experimental details, or error analysis, leaving the headline result without verifiable support.
[§3] §3 (two-stage pipeline): the scenario-generation then response-diversification steps are performed by the base LLM, so any measured diversity gains may simply reproduce the generator's own sampling distribution and biases rather than expanding to rarer valid commonsense alternatives; a direct comparison to human-annotated rare cases is required to substantiate the diversity-expansion claim.

minor comments (1)

[Abstract] Abstract, final sentence: subject-verb agreement error ('The model ... jointly increase' should read 'increases').

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our work. We address each major comment below and have revised the manuscript to strengthen the presentation of our results and methodology.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that fine-tuning on CommonSyn 'jointly increase both generation diversity and quality' is asserted without any metrics, baselines, experimental details, or error analysis, leaving the headline result without verifiable support.

Authors: We agree that the abstract should provide more concrete support for the central claim. In the revised manuscript, we have updated the abstract to include key quantitative results (diversity and quality metrics), the main baselines, and the LLM sizes evaluated. This addition supplies the verifiable details requested while preserving the abstract's brevity. revision: yes
Referee: [§3] §3 (two-stage pipeline): the scenario-generation then response-diversification steps are performed by the base LLM, so any measured diversity gains may simply reproduce the generator's own sampling distribution and biases rather than expanding to rarer valid commonsense alternatives; a direct comparison to human-annotated rare cases is required to substantiate the diversity-expansion claim.

Authors: We acknowledge the possibility that LLM-generated data could reflect the generator's distribution. Our two-stage prompting, however, explicitly instructs the model to produce multiple distinct scenarios followed by diversified responses per scenario, which is intended to surface less frequent but valid commonsense alternatives. The paper's results show that models fine-tuned on CommonSyn outperform both the base LLM and human-data fine-tuned models on diversity metrics while preserving quality, indicating expansion beyond the original narrow human coverage. We have added discussion in the revised §3 on the prompting design and its relation to bias mitigation. A direct side-by-side comparison against newly collected human-annotated rare cases is not present in the current experiments; our evaluation instead relies on downstream performance gains relative to human datasets. revision: partial

Circularity Check

0 steps flagged

No circularity detected in synthetic data pipeline or claims

full rationale

The paper describes a two-stage LLM-based synthetic data generation process for CommonSyn and supports its claims via direct empirical comparisons of fine-tuned models against vanilla baselines and human-crafted datasets. No equations, fitted parameters renamed as predictions, self-definitional constructs, or load-bearing self-citations appear in the provided text. The diversity and quality improvements are presented as measured outcomes on external test sets rather than reducing to the generation inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the domain assumption that automatically generated data can faithfully replicate human-annotated commonsense diversity; no free parameters or invented entities are specified in the abstract.

axioms (1)

domain assumption Synthetic data from a two-stage process can substitute for costly human-annotated diverse commonsense scenarios
Invoked to justify creating CommonSyn as training data that improves model performance.

pith-pipeline@v0.9.0 · 5453 in / 1106 out tokens · 31195 ms · 2026-05-15T08:07:52.182337+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

two-stage method to create CommonSyn... balances local diversity within each concept set and global diversity across the entire dataset

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

64 extracted references · 64 canonical work pages · 1 internal anchor

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Brando Miranda, Alycia Lee, Sudharsan Sundar, Allison Casasola, and Sanmi Koyejo

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work page internal anchor Pith review Pith/arXiv arXiv 2018
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The goal keeper took a daring shot at goal, hoping to catch the player off guard

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The player takes a shot with the inten- tion of scoring the goal in the ongoing soccer match

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soccer player takes a shot on goal during their match

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football player takes a shot on goal during a friendly match

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soccer player taking a shot for the goal during the match

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football player takes a shot on goal during a training session

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The player takes a shot to score the final goal in the championship

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During the intense match, the player took a bold shot toward the goal

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The soccer player took a powerful shot to score the goal

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pan, stove, cook, food 1

The soccer player takes a fierce shot towards the goal, hoping to score. pan, stove, cook, food 1. She can pan fry the chicken on the stove to cook the delicious food

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In the morning, I like to cook deli- cious food on a stove using a pan

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She uses a stove to cook food in a pan

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She uses a pan on the stove to cook food for dinner

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A young boy cooking food with a pan on the stove

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A man is cooking food in a pan on a stove

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a man cooks with a frying pan on the stove

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In the morning, he stands at the stove to cook breakfast in a pan

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I use a pan on my stove to cook food every day

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Every Sunday, I cook a hearty meal on my pan over the stove

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dog, throw, frisbee, catch 1

He carefully cooked the pasta on the stovetop pan for his family. dog, throw, frisbee, catch 1. The dog happily catches the frisbee that was thrown by its owner

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The dog threw the frisbee and then eagerly waited to catch it

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The dog threw the frisbee and then patiently waited to catch it again

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A woman is throwing a frisbee for her dog to catch

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Two dogs play tug with a frisbee

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Dog throws the Frisbee for an- other dog to catch

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My dog stands excitedly as I throw the frisbee; it always loves a good catch

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She throws a frisbee for her dog to catch in the park

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My dog always catches the frisbee I throw during our evening walks

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coffee, drink, newspaper, read 1

The owner throws the frisbee for his dog to catch, enjoying the sunny day. coffee, drink, newspaper, read 1. He likes to drink coffee while reading the newspaper in the morning

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He likes to read the newspaper while drinking coffee in the morning

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He likes to drink coffee while reading the newspaper in the morning

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young woman reading a news- paper while drinking coffee and smoking

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young women drinking a cof- fee and reading the newspaper at home

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A woman is reading a newspaper while drinking coffee

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The man is drinking coffee while reading a newspaper

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She enjoys a quiet morning with a cup of coffee and the local newspaper, sip- ping it as she reads

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He likes to drink his coffee while read- ing the morning newspaper

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Every morning, he drinks a cup of cof- fee and reads the newspaper

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ball, throw, pitcher, batter 1

After work, I sit down to read the news- paper and drink coffee. ball, throw, pitcher, batter 1. The pitcher threw the ball to the batter during the baseball game

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The pitcher threw the ball to the batter during the baseball game

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The pitcher throws the ball to the bat- ter during the baseball game

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The pitcher has just thrown a pitch to home plate and the batter is swinging

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A batter is waiting for the ball to be thrown by the pitcher

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a ball thrown by a pitcher and the batter

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pitcher throws a ball at the batter

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The pitcher throws the ball, but the bat- ter swings and misses, watching it sail past

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The batter waited for the ball to be thrown by the pitcher

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During practice, a young batter focuses on how to react to a thrown ball by the pitcher

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The image shows sugar is added to make coffee sour

The pitcher throws a curve ball past the batter. Table 11: Extended case study showing four sampled generations per model for each concept set. CommonGen often ignores or distorts input concepts, while Vanilla exhibits high redundancy. CommonSyn produces diverse, fluent, and concept-consistent outputs that respect real-world roles and relationships. Task ...

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[62]

Let’s think step by step:

First write a reasoning or description to explain the underlying commonsense connection of the keywords. Start with“Let’s think step by step:”

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Then, on the next line, output ONE realistic English sentence that contains ALL the keywords

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Formatting constraints: • Use exactlyone reasoning paragraph (≥4sentences)andone sentenceper generation

Keep the sentence concise (≤22words). Formatting constraints: • Use exactlyone reasoning paragraph (≥4sentences)andone sentenceper generation. • Separate each generation with a blank line. • Do not add numbering, commentary, or bullet points. Keywords: ##problem## Figure 5: Prompts used to generate synthetic sentences from expanded concept sets. The top p...

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Score each sentence independently from 1 to 10

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Higher score = higher quality and commonsense correctness

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All scores must be integers

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•4–6 (average):Mostly correct but may have minor issues in clarity, grammar, or concept integration

Use the full range (1–10) with these guidelines: •1–3 (poor):Incorrect, implausible, ungrammatical, or fails to use the concepts meaningfully. •4–6 (average):Mostly correct but may have minor issues in clarity, grammar, or concept integration. •7–8 (good):Clear, fluent, plausible sentences that use the concepts well. •9–10 (excellent):Exceptional clarity,...

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tie". Now, please output your choice (

If a sentence is marked as “[EMPTY]”, assign it a score of 1. Evaluate based on: •Commonsense correctness: Is the event plausible and realistic? •Concept coverage: Are the concepts used meaningfully together? •Clarity and grammar: Is the sentence well-formed? Concept set: {concept_set} Candidate sentences: {sentence_list} Figure 6: Prompt used by the qual...

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