5 papers · 1 filter
MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generation
Junqing He, Liang Zhu, Rui Wang +3
Long-term memory is important for chatbots and dialogue systems (DS) to create consistent and human-like conversations, evidenced by numerous developed memory-augmented DS (MADS).…
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine
Xiaochen Wang, Junqing He, Liang Chen +5
Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks. However, Multi-hop Question Answer…
Fostering Natural Conversation in Large Language Models with NICO: a Natural Interactive COnversation dataset
Renliang Sun, Mengyuan Liu, Shiping Yang +3
Benefiting from diverse instruction datasets, contemporary Large Language Models (LLMs) perform effectively as AI assistants in collaborating with humans. However, LLMs still strug…
FSM: A Finite State Machine Based Zero-Shot Prompting Paradigm for Multi-Hop Question Answering
Xiaochen Wang, Junqing He, Zhe yang +4
Large Language Models (LLMs) with chain-of-thought (COT) prompting have demonstrated impressive abilities on simple nature language inference tasks. However, they tend to perform p…
Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training
Junqing He, Kunhao Pan, Xiaoqun Dong +7
While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts. The "lost in the…