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Found 6 papers, 3 papers with code
MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries
We hope MultiHop-RAG will be a valuable resource for the community in developing effective RAG systems, thereby facilitating greater adoption of LLMs in practice.
Exploring the Relationship between In-Context Learning and Instruction Tuning
In this work, we explore the relationship between ICL and IT by examining how the hidden states of LLMs change in these two paradigms.
An Empathetic User-Centric Chatbot for Emotional Support
This paper explores the intersection of Otome Culture and artificial intelligence, particularly focusing on how Otome-oriented games fulfill the emotional needs of young women.
FinEntity: Entity-level Sentiment Classification for Financial Texts
In this work, we introduce an entity-level sentiment classification dataset, called \textbf{FinEntity}, that annotates financial entity spans and their sentiment (positive, neutral, and negative) in financial news.
InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning
We present a new financial domain large language model, InvestLM, tuned on LLaMA-65B (Touvron et al., 2023), using a carefully curated instruction dataset related to financial investment.
Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions?
Multi-hop question answering (QA) requires a model to retrieve and integrate information from different parts of a long text to answer a question.
