Skip to content
OpenTrain AIFor AI Companies
← Back to explorer

Commonsense on Demand: Generating and Selectively Integrating Commonsense Knowledge for Natural Language Inference

Chathuri Jayaweera, Brianna Yanqui, Bonnie J. Dorr · Jul 20, 2025 · Citations: 0

Automatic Metrics General Llm As Judge
Open arXiv Find Implementation RSS feed Shortlist (0)

How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Natural Language Inference (NLI) determines whether a premise entails, contradicts, or is neutral with respect to a hypothesis. The task is often framed as emulating human inference, in which commonsense knowledge plays a major role. This study examines whether Large Language Models (LLMs) can reliably generate factual commonsense axioms for NLI, and evaluates their utility on the SNLI and ANLI benchmarks using Llama-3.1-70B and gpt-oss-120b. Because commonsense axioms lack explicit textual references, standard factuality metrics are ill-suited to their evaluation. We therefore introduce a reference-free method using an LLM-as-Judge framework. The evaluation reveals a substantial gap between models: gpt-oss-120b generates predominantly accurate axioms, whereas Llama produces more incorrect than correct ones. We further evaluate three prompting pipelines: direct inference, inference augmented with generated commonsense axioms, and a hybrid approach that selectively incorporates highly factual axioms based on judged factuality. The hybrid approach yields consistent accuracy gains of 3.87%-8.5% across tested configurations. Targeted commonsense knowledge also helps models overcome a bias toward the Neutral class by providing essential real-world context.

Low-signal caution for protocol decisions

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • The available metadata is too thin to trust this as a primary source.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

The available metadata is too thin to trust this as a primary source.

Trust level

Low

Usefulness score

37/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 45%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Natural Language Inference (NLI) determines whether a premise entails, contradicts, or is neutral with respect to a hypothesis."

Evaluation Modes

partial

Llm As Judge, Automatic Metrics

Includes extracted eval setup.

"Natural Language Inference (NLI) determines whether a premise entails, contradicts, or is neutral with respect to a hypothesis."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Natural Language Inference (NLI) determines whether a premise entails, contradicts, or is neutral with respect to a hypothesis."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Natural Language Inference (NLI) determines whether a premise entails, contradicts, or is neutral with respect to a hypothesis."

Reported Metrics

partial

Accuracy

Useful for evaluation criteria comparison.

"The hybrid approach yields consistent accuracy gains of 3.87%-8.5% across tested configurations."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: General

Evaluation Details

  • Evaluation modes: Llm As Judge, Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

accuracy

Research Brief

Metadata summary

Natural Language Inference (NLI) determines whether a premise entails, contradicts, or is neutral with respect to a hypothesis.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Natural Language Inference (NLI) determines whether a premise entails, contradicts, or is neutral with respect to a hypothesis.
  • The task is often framed as emulating human inference, in which commonsense knowledge plays a major role.
  • This study examines whether Large Language Models (LLMs) can reliably generate factual commonsense axioms for NLI, and evaluates their utility on the SNLI and ANLI benchmarks using Llama-3.1-70B and gpt-oss-120b.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Accuracy

Research Summary

Contribution Summary

  • The task is often framed as emulating human inference, in which commonsense knowledge plays a major role.
  • This study examines whether Large Language Models (LLMs) can reliably generate factual commonsense axioms for NLI, and evaluates their utility on the SNLI and ANLI benchmarks using Llama-3.1-70B and gpt-oss-120b.
  • Because commonsense axioms lack explicit textual references, standard factuality metrics are ill-suited to their evaluation.

Why It Matters For Eval

  • The task is often framed as emulating human inference, in which commonsense knowledge plays a major role.
  • This study examines whether Large Language Models (LLMs) can reliably generate factual commonsense axioms for NLI, and evaluates their utility on the SNLI and ANLI benchmarks using Llama-3.1-70B and gpt-oss-120b.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Llm As Judge, Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: accuracy

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.