Language Models for Lexical Inference in Context
Lexical inference in context (LIiC) is the task of recognizing textual entailment between two very similar sentences, i.e., sentences that only differ in one expression. It can therefore be seen as a variant of the natural language inference task that is focused on lexical semantics. We formulate and evaluate the first approaches based on pretrained language models (LMs) for this task: (i) a few-shot NLI classifier, (ii) a relation induction approach based on handcrafted patterns expressing the semantics of lexical inference, and (iii) a variant of (ii) with patterns that were automatically extracted from a corpus. All our approaches outperform the previous state of the art, showing the potential of pretrained LMs for LIiC. In an extensive analysis, we investigate factors of success and failure of our three approaches.
Code (1)
Tasks
Few-Shot NLINatural Language InferenceSimilar Papers 제목 키워드 기반
Real Images, Worse Judgments: Evaluating Vision-Language Models on Concreteness and Imagery
Visual inputs are often assumed to improve language understanding in multimodal models. We examine this assumption by asking whether vision-language models (VLMs) can distinguish useful visual evidence from incidental im…
Enhancing Speaker Diarization with Large Language Models: A Contextual Beam Search Approach
Large language models (LLMs) have shown great promise for capturing contextual information in natural language processing tasks. We propose a novel approach to speaker diarization that incorporates the prowess of LLMs to…
speaker-diarizationSpeaker DiarizationLexical Manifold Reconfiguration in Large Language Models: A Novel Architectural Approach for Contextual Modulation
Contextual adaptation in token embeddings plays a central role in determining how well language models maintain coherence and retain semantic relationships over extended text sequences. Static embeddings often impose con…
Language ModelingLanguage ModellingRepresentation LearningSentence+1SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference
We present SherLIiC, a testbed for lexical inference in context (LIiC), consisting of 3985 manually annotated inference rule candidates (InfCands), accompanied by (i) ~960k unlabeled InfCands, and (ii) ~190k typed textua…
Lexical EntailmentNatural Language InferenceS$^3$-Attention:Attention-Aligned Endogenous Retrieval for Memory-Bounded Long-Context Inference
Large language models are increasingly applied to multi-document and long-form inputs, yet long-context inference remains memory- and noise-inefficient. Key-value (KV) caching scales linearly with context length, while e…