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Interpretable NLI achieved via graph-based atomic propositions

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a novel approach to Natural Language Inference (NLI) that utilizes interpretable, graph-based representations derived from atomic propositions. This method decomposes sentences into ConceptNet triples, forming graphs that are then processed by a fine-tuned language model. While this graph-based pipeline achieves competitive accuracy on the SNLI dataset, it shows a performance gap on the ANLI dataset, which the authors attribute to representational limitations rather than data constraints. Combining both graph and text modalities proved to be complementary, yielding improved accuracy on SNLI. AI

IMPACT This research explores a novel method for making LLM decision-making more auditable, potentially improving trust and transparency in NLI systems.

RANK_REASON The cluster contains an academic paper detailing a new methodology for Natural Language Inference.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Interpretable NLI achieved via graph-based atomic propositions

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The cluster contains an academic paper detailing a new methodology for Natural Language Inference.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Younes Boufouss (LISN), Luc Pommeret (LISN, CNRS), Thomas Gerald (LISN), Patrick Paroubek (LISN, CNRS), Sophie Rosset (LISN, CNRS) ·

    Can We Do Interpretable NLI with Graphs Based on Atomic Propositions?

    arXiv:2609.16814v1 Announce Type: new Abstract: While Large Language Model (LLM)-based Natural Language Inference (NLI) systems achieve high accuracy, their decision-making processes lack auditable structures. This paper explores whether NLI can be performed using only interpreta…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Sophie Rosset ·

    Can We Do Interpretable NLI with Graphs Based on Atomic Propositions?

    While Large Language Model (LLM)-based Natural Language Inference (NLI) systems achieve high accuracy, their decision-making processes lack auditable structures. This paper explores whether NLI can be performed using only interpretable, graph-based representations of evidence. We…

  3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Sophie Rosset ·

    Can We Do Interpretable NLI with Graphs Based on Atomic Propositions?

    While Large Language Model (LLM)-based Natural Language Inference (NLI) systems achieve high accuracy, their decision-making processes lack auditable structures. This paper explores whether NLI can be performed using only interpretable, graph-based representations of evidence. We…

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