243 citations · 390 across the 16 of their papers we have counts for
17 papers
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
Jianing Wang, Qiushi Sun, Xiang Li +1
Recently, Chain-of-Thought (CoT) prompting has delivered success on complex reasoning tasks, which aims at designing a simple prompt like ``Let's think step by step'' or multiple i…
Faith and Fate: Limits of Transformers on Compositionality
Nouha Dziri, Ximing Lu, Melanie Sclar +13
Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneo…
Editing Common Sense in Transformers
Anshita Gupta, Debanjan Mondal, Akshay Krishna Sheshadri +4
Editing model parameters directly in Transformers makes updating open-source transformer-based models possible without re-training (Meng et al., 2023). However, these editing metho…
SHQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering
Fangyu Lei, Xiang Li, Yifan Wei +4
Answering multi-hop questions over hybrid factual knowledge from the given text and table (TextTableQA) is a challenging task. Existing models mainly adopt a retriever-reader frame…
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder
Fangyu Lei, Shizhu He, Xiang Li +2
In the real-world question answering scenarios, hybrid form combining both tabular and textual contents has attracted more and more attention, among which numerical reasoning probl…
Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding
Jianing Wang, Wenkang Huang, Qiuhui Shi +4
Knowledge-enhanced Pre-trained Language Model (PLM) has recently received significant attention, which aims to incorporate factual knowledge into PLMs. However, most existing metho…
Bear the Query in Mind: Visual Grounding with Query-conditioned Convolution
Chonghan Chen, Qi Jiang, Chih-Hao Wang +4
Visual grounding is a task that aims to locate a target object according to a natural language expression. As a multi-modal task, feature interaction between textual and visual inp…
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.…
A Systematic Investigation of Commonsense Knowledge in Large Language Models
Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann +3
Language models (LMs) trained on large amounts of data have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. Here we aim to better understand…
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings
Shib Sankar Dasgupta, Michael Boratko, Siddhartha Mishra +4
Learning representations of words in a continuous space is perhaps the most fundamental task in NLP, however words interact in ways much richer than vector dot product similarity c…
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning
Xuelu Chen, Michael Boratko, Muhao Chen +3
Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for ea…
Improving Local Identifiability in Probabilistic Box Embeddings
Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang +3
Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings, where objects are repr…
Reading Comprehension as Natural Language Inference: A Semantic Analysis
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar +3
In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited…
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Wenhan Xiong, Xiang Lorraine Li, Srini Iyer +8
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datas…
ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
Michael Boratko, Xiang Lorraine Li, Rajarshi Das +3
Given questions regarding some prototypical situation such as Name something that people usually do before they leave the house for work? a human can easily answer them via acquire…
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures
Luke Vilnis, Xiang Li, Shikhar Murty +1
Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE) (Vendrov et al., 2016), are a natural way to model transi…
Improved Representation Learning for Predicting Commonsense Ontologies
Xiang Li, Luke Vilnis, Andrew McCallum
Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions tha…