Search Results for author:
Found 40 papers, 25 papers with code
Improving Question Answering by Commonsense-Based Pre-Training
Although neural network approaches achieve remarkable success on a variety of NLP tasks, many of them struggle to answer questions that require commonsense knowledge.
Reasoning Over Semantic-Level Graph for Fact Checking
We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy.
Ranked #2 on
Fact Verification
on FEVER
A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts
We study question answering over a dynamic textual environment.
LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network
The graph is used to obtain graph-enhanced contextual representations of words in Transformer-based architecture.
Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection
We study the detection of propagandistic text fragments in news articles.
Neural Deepfake Detection with Factual Structure of Text
To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text.
Syntax-Enhanced Pre-trained Model
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa.
AR-LSAT: Investigating Analytical Reasoning of Text
Analytical reasoning is an essential and challenging task that requires a system to analyze a scenario involving a set of particular circumstances and perform reasoning over it to make conclusions.
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text
Logical reasoning of text requires understanding critical logical information in the text and performing inference over them.
Ranked #7 on
Reading Comprehension
on ReClor
Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge
Specifically, we first construct a \textit{directed heterogeneous document graph} for each news incorporating topics and entities.
From LSAT: The Progress and Challenges of Complex Reasoning
Complex reasoning aims to draw a correct inference based on complex rules.
Reasoning over Hybrid Chain for Table-and-Text Open Domain QA
CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering.
Ranked #2 on
Question Answering
on OTT-QA
ProQA: Structural Prompt-based Pre-training for Unified Question Answering
Furthermore, ProQA exhibits strong ability in both continual learning and transfer learning by taking the advantages of the structural prompt.
Modeling Semantic Composition with Syntactic Hypergraph for Video Question Answering
A key challenge in video question answering is how to realize the cross-modal semantic alignment between textual concepts and corresponding visual objects.
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training
We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models.
Improving Task Generalization via Unified Schema Prompt
Task generalization has been a long standing challenge in Natural Language Processing (NLP).
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding
This paper tackles an emerging and challenging problem of long video temporal grounding~(VTG) that localizes video moments related to a natural language (NL) query.
Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA
However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem.
Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers
This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making.
An Efficient COarse-to-fiNE Alignment Framework @ Ego4D Natural Language Queries Challenge 2022
This technical report describes the CONE approach for Ego4D Natural Language Queries (NLQ) Challenge in ECCV 2022.
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models
Impressively, GPT-4 surpasses average human performance on SAT, LSAT, and math competitions, attaining a 95% accuracy rate on the SAT Math test and a 92. 5% accuracy on the English test of the Chinese national college entrance exam.
MemoryBank: Enhancing Large Language Models with Long-Term Memory
To mimic anthropomorphic behaviors and selectively preserve memory, MemoryBank incorporates a memory updating mechanism, inspired by the Ebbinghaus Forgetting Curve theory, which permits the AI to forget and reinforce memory based on time elapsed and the relative significance of the memory, thereby offering a human-like memory mechanism.
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner.
GroundNLQ @ Ego4D Natural Language Queries Challenge 2023
Motivated by this, we leverage a two-stage pre-training strategy to train egocentric feature extractors and the grounding model on video narrations, and further fine-tune the model on annotated data.
Aligning Large Language Models with Human: A Survey
(2) Training methodologies: a detailed review of the prevailing training methods employed for LLM alignment.
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
When learnersourcing multiple-choice questions, creating explanations for the solution of a question is a crucial step; it helps other students understand the solution and promotes a deeper understanding of related concepts.
Adaptive-Solver Framework for Dynamic Strategy Selection in Large Language Model Reasoning
Experimental results from complex reasoning tasks reveal that the prompting method adaptation and decomposition granularity adaptation enhance performance across all tasks.
SELF: Self-Evolution with Language Feedback
SELF initiates with a meta-skill learning process that equips the LLMs with capabilities for self-feedback and self-refinement.
Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning
Despite their high performance on the original publicly available datasets, we find that all models perform poorly on these newly constructed datasets.
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models
To fill this research gap, in this paper, we propose FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for LLMs.
Data Management For Large Language Models: A Survey
Data plays a fundamental role in the training of Large Language Models (LLMs).
G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model
We first analyze the limitations of current Multimodal Large Language Models (MLLMs) in this area: they struggle to accurately comprehending basic geometric elements and their relationships.
YODA: Teacher-Student Progressive Learning for Language Models
With the teacher's guidance, the student learns to iteratively refine its answer with feedback, and forms a robust and comprehensive understanding of the posed questions.
Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios
The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools.
Learning to Edit: Aligning LLMs with Knowledge Editing
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention.
PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question Answering
This dataset is collected to investigate the use of personalized memories, focusing on social interactions and events in the QA task.
CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models
Developing Large Language Models (LLMs) with robust long-context capabilities has been the recent research focus, resulting in the emergence of long-context LLMs proficient in Chinese.
Analytical Reasoning of Text
In this paper, we study the challenge of analytical reasoning of text and collect a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016.
