Papers with NLI label
Don’t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference (P19-1)
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| Challenge: | Natural Language Inference (NLI) datasets often contain hypothesis-only biases . authors propose probabilistic methods to build models that are more robust to such artifacts - a new study shows . |
| Approach: | They propose probabilistic methods to build models that are more robust to biases . authors train on datasets containing biase . |
| Outcome: | The proposed methods can make NLI models more robust to dataset-specific artifacts . the methods transfer better than a baseline architecture in 9 out of 12 NLI datasets compared with baseline architectures based on the proposed methods . |
ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning (2023.acl-short)
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| Challenge: | Negation is a ubiquitous but complex linguistic phenomenon that poses a significant challenge for NLP systems. |
| Approach: | They propose a benchmark that measures how well models handle natural language negation . they extend ScoNe-NLI to embed negation reasoning in short narratives . |
| Outcome: | The proposed model can reason about negation, but struggles to do so on NLI examples outside of its core pretraining regime. |
Identifying inherent disagreement in natural language inference (2021.naacl-main)
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| Challenge: | Natural language inference is the task of determining whether text is entailed, contradicted or unrelated to another piece of text. |
| Approach: | They propose to tease systematic inferences from disagreement items by capturing modes in annotations to simulate uncertainty in the annotation process. |
| Outcome: | The proposed approach performs statistically better than baselines on the CommitmentBank corpus in English. |
Rank-Awareness and Angular Constraints: A New Perspective on Learning Sentence Embeddings from NLI Data (2025.emnlp-main)
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| Challenge: | High-quality sentence embeddings are critical for advancing a wide range of Natural Language Processing tasks. |
| Approach: | They propose a framework that leverages the full NLI dataset augmented with pre-computed continuous similarity scores (S) they employ a Rank Margin objective that enforces rank consistency against S using an explicit margin and a Gated Angular objective that conditionally refines embedding geometry based on NLI label (L) and S score agreement. |
| Outcome: | The proposed framework outperforms baseline models on STS and the MTEB benchmarks. |