Search Results for author:
Found 19 papers, 12 papers with code
NovelQA: A Benchmark for Long-Range Novel Question Answering
The rapid advancement of Large Language Models (LLMs) has introduced a new frontier in natural language processing, particularly in understanding and processing long-context information.
Knowledge Conflicts for LLMs: A Survey
This survey provides an in-depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge.
LLMs with Chain-of-Thought Are Non-Causal Reasoners
We further examine the factors influencing the causal structure of the implied SCM, revealing that in-context learning, supervised fine-tuning, and reinforcement learning on human feedback significantly impact the causal relations.
Self-DC: When to retrieve and When to generate? Self Divide-and-Conquer for Compositional Unknown Questions
Retrieve-then-read and generate-then-read are two typical solutions to handle unknown and known questions in open-domain question-answering, while the former retrieves necessary external knowledge and the later prompt the large language models to generate internal known knowledge encoded in the parameters.
SQL-CRAFT: Text-to-SQL through Interactive Refinement and Enhanced Reasoning
Modern LLMs have become increasingly powerful, but they are still facing challenges in specialized tasks such as Text-to-SQL.
TRAMS: Training-free Memory Selection for Long-range Language Modeling
The Transformer architecture is crucial for numerous AI models, but it still faces challenges in long-range language modeling.
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
This survey addresses the crucial issue of factuality in Large Language Models (LLMs).
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
To ensure the reliability of PandaLM, we collect a diverse human-annotated test dataset, where all contexts are generated by humans and labels are aligned with human preferences.
RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering
Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of generating answers by simultaneously referring to multiple passages.
Exploiting Abstract Meaning Representation for Open-Domain Question Answering
The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database.
Evaluating Open-QA Evaluation
This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs).
Knowledgeable Salient Span Mask for Enhancing Language Models as Knowledge Base
Pre-trained language models (PLMs) like BERT have made significant progress in various downstream NLP tasks.
Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning
To quantitatively and intuitively explore the generalization ability of pre-trained language models (PLMs), we have designed several tasks of arithmetic and logical reasoning.
Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?
Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions.
Commonsense Knowledge Graph Reasoning by Selection or Generation? Why?
We theoretically and empirically compare the two methods, finding the selection method is more suitable than the generation method in CKGR.
SemEval-2020 Task 4: Commonsense Validation and Explanation
In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a natural language statement that makes sense to humans from one that does not, and provide the reasons.
Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation
Introducing common sense to natural language understanding systems has received increasing research attention.
Domain Representation for Knowledge Graph Embedding
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning.
