Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query, which is important to many downstream applications like fact...

Contradiction detection is a task to recognize contradiction relations between a pair of sentences. Despite the effectiveness of traditional context-based word embedding learning algorithms in many natural language processing tasks, such algorithms are not powerful enough for contradiction detection. Contrasting words such as “overfull” and “empty” are mostly mapped into close vectors in such embedding space. To solve this problem, we develop a tailored neural network to learn contradiction-specific word embedding (CWE). The method can separate antonyms in the opposite ends of a spectrum. CWE is learned from a training corpus which is automatically generated from the paraphrase database, and is naturally applied as features to carry out contradiction detection in SemEval 2014 benchmark dataset. Experimental results show that CWE outperforms traditional context-based word embedding in contradiction detection. The proposed model for contradiction detection performs comparably with the top-performing system in accuracy of three-category classification and enhances the accuracy from 75.97% to 82.08% in the contradiction category.

Full Stack application for retrieving Stock Data and News using LLM, LangChain and LangGraph - vinay-gatech/stocks-insights-ai-agent

Compare performance, cost, and accuracy of leading Vision Language Models including GPT-4V, Claude 3.5, and open-source alternatives. Real-world testing on document processing tasks.

We investigate whether large language models (LLMs) can successfully perform financial statement analysis in a way similar to a professional human analyst

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Cosine similarity - the duct tape of AI. Convenient but often misused. Let's find out how to use it better.

This paper describes an efficiently scaleable approach to measuring technological similarity between patents by combining embedding techniques from na…

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We’re on a journey to advance and democratize artificial intelligence through open source and open science.

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— Jo Kristian Bergum (@jobergum)

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