This guide explains how to create a retrieval‑augmented generation (RAG) system that avoids embeddings by converting documents into a hierarchical tree, using an LLM to navigate, summarize, and retrieve answers, complete with a full Python implementation and a GitHub repository.
iQIYI’s AI research team secured three paper acceptances—two at ACL 2025 (including a main conference and a Findings paper) and one at INTERSPEECH 2025—covering long‑context large language model evaluation, Chinese novel summarization, and efficient Thai speech recognition, with links to each work.
This article reviews the integration of contrastive learning into text generation, explains why it helps mitigate exposure bias, introduces the CoNT framework with three key improvements, presents extensive experiments on translation, summarization, code comment and data‑to‑text tasks, and discusses practical deployment considerations.
This article reviews recent advances in abstractive summarization, covering pointer‑generator networks with coverage loss, integration of entity knowledge, strategies to mitigate repetition such as unlikelihood training and nucleus sampling, and emerging non‑autoregressive approaches like the Levenshtein Transformer.
The article recaps the inaugural Multimodal Natural Language Processing workshop at NLPCC 2020, highlighting breakthroughs in multimodal summarization, pre‑training models, AI‑driven art, visual‑language interaction, and multimodal dialogue systems, and showcases research from leading institutions and industry partners.
