6 citations · 34 across the 20 of their papers we have counts for
17 papers · 1 filter
Competence-Based Evaluation of Logical Reasoning in Large Language Models
Jize Jiang, Chengxiang Zhai
Recent advances in large language models (LLMs) have brought impressive performance across diverse tasks; however, shortcomings remain in logical reasoning, including inconsistenci…
DAGFormer: Global Topology Prediction for Transformer Cross-Layer Connections
Xiaocong Yang, Yuren Hao, ChengXiang Zhai
We propose DAGFormer, a Transformer language model whose cross-layer connections are planned by a jointly trained topology predictor. Beyond the residual connections in a standard…
C-PMI: Conditional Pointwise Mutual Information for Turn-level Dialogue Evaluation
Liliang Ren, Mankeerat Sidhu, Qi Zeng +3
Existing reference-free turn-level evaluation metrics for chatbots inadequately capture the interaction between the user and the system. Consequently, they often correlate poorly w…
Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders
Daniel Campos, Alessandro Magnani, ChengXiang Zhai
In this paper, we consider the problem of improving the inference latency of language model-based dense retrieval systems by introducing structural compression and model size asymm…
To Asymmetry and Beyond: Structured Pruning of Sequence to Sequence Models for Improved Inference Efficiency
Daniel Campos, ChengXiang Zhai
Sequence-to-sequence language models can be used to produce abstractive summaries which are coherent, relevant, and concise. Still, model sizes can make deployment in latency-sensi…
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes
Daniel Campos, Alexandre Marques, Mark Kurtz +1
In this paper, we introduce the range of oBERTa language models, an easy-to-use set of language models which allows Natural Language Processing (NLP) practitioners to obtain betwee…
Competence-Based Analysis of Language Models
Adam Davies, Jize Jiang, ChengXiang Zhai
Despite the recent successes of large, pretrained neural language models (LLMs), comparatively little is known about the representations of linguistic structure they learn during p…
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications
Kevin Pei, Ishan Jindal, Kevin Chen-Chuan Chang +2
Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying…
Language Model Pre-Training with Sparse Latent Typing
Liliang Ren, Zixuan Zhang, Han Wang +3
Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks. However, most of the LM pre-training objectives only foc…
Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
Bhavya Bhavya, Jinjun Xiong, Chengxiang Zhai
We propose a novel application of prompting Pre-trained Language Models (PLMs) to generate analogies and study how to design effective prompts for two task settings: generating a s…
CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval
Kung-Hsiang Huang, ChengXiang Zhai, Heng Ji
Fact-checking has gained increasing attention due to the widespread of falsified information. Most fact-checking approaches focus on claims made in English only due to the data sca…
Incorporating Task-specific Concept Knowledge into Script Learning
Chenkai Sun, Tie Xu, ChengXiang Zhai +1
In this paper, we present Tetris, a new task of Goal-Oriented Script Completion. Unlike previous work, it considers a more realistic and general setting, where the input includes n…
Sparse*BERT: Sparse Models Generalize To New tasks and Domains
Daniel Campos, Alexandre Marques, Tuan Nguyen +2
Large Language Models have become the core architecture upon which most modern natural language processing (NLP) systems build. These models can consistently deliver impressive acc…
Multi-task Learning for Multilingual Neural Machine Translation
Yiren Wang, ChengXiang Zhai, Hany Hassan Awadalla
While monolingual data has been shown to be useful in improving bilingual neural machine translation (NMT), effectively and efficiently leveraging monolingual data for Multilingual…
Cooperative Reasoning on Knowledge Graph and Corpus: A Multi-agentReinforcement Learning Approach
Yunan Zhang, Xiang Cheng, Heting Gao +1
Knowledge-graph-based reasoning has drawn a lot of attention due to its interpretability. However, previous methods suffer from the incompleteness of the knowledge graph, namely th…
Improving N-gram Language Models with Pre-trained Deep Transformer
Yiren Wang, Hongzhao Huang, Zhe Liu +4
Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in infe…
Learning to Order Sub-questions for Complex Question Answering
Yunan Zhang, Xiang Cheng, Yufeng Zhang +5
Answering complex questions involving multiple entities and relations is a challenging task. Logically, the answer to a complex question should be derived by decomposing the comple…