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Found 10 papers, 3 papers with code
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine
Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks.
MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generation
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).
Fostering Natural Conversation in Large Language Models with NICO: a Natural Interactive COnversation dataset
Benefiting from diverse instruction datasets, contemporary Large Language Models (LLMs) perform effectively as AI assistants in collaborating with humans.
FSM: A Finite State Machine Based Zero-Shot Prompting Paradigm for Multi-Hop Question Answering
Large Language Models (LLMs) with chain-of-thought (COT) prompting have demonstrated impressive abilities on simple nature language inference tasks.
Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training
While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts.
Ziya2: Data-centric Learning is All LLMs Need
Although many such issues are addressed along the line of research on LLMs, an important yet practical limitation is that many studies overly pursue enlarging model sizes without comprehensively analyzing and optimizing the use of pre-training data in their learning process, as well as appropriate organization and leveraging of such data in training LLMs under cost-effective settings.
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Thus, model's comprehension ability towards real scenarios are hard to evaluate reasonably.
Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence
We hope that this project will be the foundation of Chinese cognitive intelligence.
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity
In this paper, we introduce an approach to combining word embeddings and machine translation for multilingual semantic word similarity, the task2 of SemEval-2017.
Cross-Lingual Word Embeddings
Multilingual Word Embeddings
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