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Found 6 papers, 6 papers with code
FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema
In the quest to facilitate the deep intelligence of Large Language Models (LLMs) accessible in final-end user-bot interactions, the art of prompt crafting emerges as a critical yet complex task for the average user.
MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation
We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range open-domain conversations.
Event Knowledge Incorporation with Posterior Regularization for Event-Centric Question Answering
In particular, we define event-related knowledge constraints based on the event trigger annotations in the QA datasets, and subsequently use them to regularize the posterior answer output probabilities from the backbone pre-trained language models used in the QA setting.
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved.
Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation
Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines.
CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering
We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation.
