Natural Language Inference

33개 벤치마크 · 논문 2,097편 · 이 태스크의 논문 보기 →

Benchmarks

Most implemented

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

2018-10-11 · 구현 534개

Language Models are Few-Shot Learners

2020-05-28 · 구현 67개

RoBERTa: A Robustly Optimized BERT Pretraining Approach

2019-07-26 · 구현 67개

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

2019-10-23 · 구현 57개

A Structured Self-attentive Sentence Embedding

2017-03-09 · 구현 52개

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

2019-09-26 · 구현 48개

Papers

Cascaded Batch Prompting

2026-08-27 · Sho Hoshino, Peinan Zhang arxiv

Although batch prompting makes large language model inference more efficient by processing multiple instances simultaneously, it suffers from unpredictable downstream task performance. We propose cascaded batch prompting…

Natural Language Inference Question Answering

Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems

2026-08-21 · Balkrishna Giri, Md Toufique Hasan, Jussi Rasku, Muhammad Waseem 외 arxiv

Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) outputs in external knowledge, but RAG systems usually trust whatever they retrieve, creating a Security-Reliability Gap: high semantic relevance do…

Natural Language Inference

Structure, Association, and Decision Value: Representation-Based Difficulty Estimation for Adaptive Inference in African-Language NLI

2026-08-19 · Toheeb Ogunade arxiv

We ask whether internal representation statistics can provide useful example-level difficulty signals for adaptive inference in multilingual African NLP, and find that they cannot in this setting. Studying natural langua…

Natural Language Inference

Consensus Measures for Unstructured Biomedical Text Annotations

2026-08-04 · Pascal Wullschleger, Christian Kreis, Martin A. Walter, Marc Pouly 외 arxiv

Biomedical literature is increasingly mined for knowledge beyond the questions it was written to answer. Because the target concepts are not known in advance, annotators prefer open-ended labels, whose agreement is hard …

Natural Language Inference

Reference-Free Evaluation of Reasoning in Open-Ended Question Answering

2026-07-22 · Guneet Singh Kohli, Yuxiang Zhou, Michael Sejr Schlichtkrull, Gregory E Dean 외 arxiv

AI-generated answers in high-stakes domains are often fluent but difficult to verify, especially when they contain multi-step reasoning rather than a single final answer. We propose a reasoning-based, reference-free fram…

Natural Language Inference Mathematical Reasoning Question Answering

How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI

2026-07-17 · Haram Choi arxiv

Human label variation in natural language inference is increasingly treated as signal rather than noise, but how much of it formal semantic structure explains has not been measured directly. We measure it on the 3,113 SN…

Natural Language Inference

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