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Found 16 papers, 12 papers with code
Red Dragon AI at TextGraphs 2020 Shared Task : LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking
Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences.
PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns
As recognizing patterns and abstracting concepts are key to general intelligence, we introduce PuzzleVQA, a collection of puzzles based on abstract patterns.
Contrastive Chain-of-Thought Prompting
Compared to the conventional chain of thought, our approach provides both valid and invalid reasoning demonstrations, to guide the model to reason step-by-step while reducing reasoning mistakes.
Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning
Interestingly, despite being introduced four years ago, T5-based LLMs, such as FLAN-T5, continue to outperform the latest decoder-based LLMs, such as LLAMA and VICUNA, on tasks that require general problem-solving skills.
M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models
M3Exam exhibits three unique characteristics: (1) multilingualism, encompassing questions from multiple countries that require strong multilingual proficiency and cultural knowledge; (2) multimodality, accounting for the multimodal nature of many exam questions to test the model's multimodal understanding capability; and (3) multilevel structure, featuring exams from three critical educational periods to comprehensively assess a model's proficiency at different levels.
INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models
Instruction-tuned large language models have revolutionized natural language processing and have shown great potential in applications such as conversational agents.
Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-Based Sentiment Analysis (ABSA) that considers each opinion term, their expressed sentiment, and the corresponding aspect targets.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources
Specifically, CoK consists of three stages: reasoning preparation, dynamic knowledge adapting, and answer consolidation.
Is GPT-3 a Good Data Annotator?
In this paper, we evaluate the performance of GPT-3 as a data annotator by comparing it with traditional data annotation methods and analyzing its output on a range of tasks.
A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach
Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers.
Ranked #2 on
Hyper-Relational Extraction
on HyperRED
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction
We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods.
Ranked #1 on
Zero-shot Relation Triplet Extraction
on Wiki-ZSL
Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term.
Ranked #5 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
Aspect Sentiment Triplet Extraction
Computational Efficiency
+1
Red Dragon AI at TextGraphs 2020 Shared Task: LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking
Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences.
Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation
The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions.
Scene Graph Parsing by Attention Graph
Scene graph representations, which form a graph of visual object nodes together with their attributes and relations, have proved useful across a variety of vision and language applications.
Transformer to CNN: Label-scarce distillation for efficient text classification
Significant advances have been made in Natural Language Processing (NLP) modelling since the beginning of 2018.
