39 citations · 107 across the 16 of their papers we have counts for
13 papers · 1 filter
Reinforcement Learning for Long-Horizon Multi-Turn Search Agents
Vivek Kalyan, Martin Andrews
Large Language Model (LLM) agents can leverage multiple turns and tools to solve complex tasks, with prompt-based approaches achieving strong performance. This work demonstrates th…
A Reasoning-Based Approach to Cryptic Crossword Clue Solving
Martin Andrews, Sam Witteveen
Cryptic crossword clues are challenging language tasks for which new test sets are released daily by major newspapers on a global basis. Each cryptic clue contains both the definit…
Capturing Sparks of Abstraction for the ARC Challenge
Martin Andrews
Excellent progress has been made recently in solving ARC Challenge problems. However, it seems that new techniques may be required to push beyond 60% accuracy. Even commercial Larg…
Proving that Cryptic Crossword Clue Answers are Correct
Martin Andrews, Sam Witteveen
Cryptic crossword clues are challenging cognitive tasks, for which new test sets are released on a daily basis by multiple international newspapers. Each cryptic clue contains both…
Handshakes AI Research at CASE 2021 Task 1: Exploring different approaches for multilingual tasks
Vivek Kalyan, Paul Tan, Shaun Tan +1
The aim of the CASE 2021 Shared Task 1 (Hürriyetoğlu et al., 2021) was to detect and classify socio-political and crisis event information at document, sentence, cross-sentence, an…
Red Dragon AI at TextGraphs 2021 Shared Task: Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings
Vivek Kalyan, Sam Witteveen, Martin Andrews
Creating explanations for answers to science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. This year, to refocus the Textgra…
Red Dragon AI at TextGraphs 2020 Shared Task: LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking
Yew Ken Chia, Sam Witteveen, Martin Andrews
Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. To counter the limitations of metho…
Paraphrasing with Large Language Models
Sam Witteveen, Martin Andrews
Recently, large language models such as GPT-2 have shown themselves to be extremely adept at text generation and have also been able to achieve high-quality results in many downstr…
Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation
Yew Ken Chia, Sam Witteveen, Martin Andrews
The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI's e…
Unsupervised Natural Question Answering with a Small Model
Martin Andrews, Sam Witteveen
The recent (2019-02) demonstration of the power of huge language models such as GPT-2 to memorise the answers to factoid questions raises questions about the extent to which knowle…
Scene Graph Parsing by Attention Graph
Martin Andrews, Yew Ken Chia, Sam Witteveen
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 appl…
Relationships from Entity Stream
Martin Andrews, Sam Witteveen
Relational reasoning is a central component of intelligent behavior, but has proven difficult for neural networks to learn. The Relation Network (RN) module was recently proposed b…
Compressing Word Embeddings
Martin Andrews
Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic. However, the…