Search Results for author:
Found 37 papers, 16 papers with code
RAISE: Enhancing Scientific Reasoning in LLMs via Step-by-Step Retrieval
We analyze that unlike other baselines, RAISE retrieves documents that are not only similar in terms of the domain knowledge, but also documents logically more relevant.
Memorization or Reasoning? Exploring the Idiom Understanding of LLMs
Idioms have long posed a challenge due to their unique linguistic properties, which set them apart from other common expressions.
Does Localization Inform Unlearning? A Rigorous Examination of Local Parameter Attribution for Knowledge Unlearning in Language Models
Large language models often retain unintended content, prompting growing interest in knowledge unlearning.
KGMEL: Knowledge Graph-Enhanced Multimodal Entity Linking
However, most existing MEL methods overlook the rich structural information available in the form of knowledge-graph (KG) triples.
UniKnow: A Unified Framework for Reliable Language Model Behavior across Parametric and External Knowledge
Language models often benefit from external knowledge beyond parametric knowledge.
When to Speak, When to Abstain: Contrastive Decoding with Abstention
Large Language Models (LLMs) demonstrate exceptional performance across diverse tasks by leveraging pre-trained (i. e., parametric) and external (i. e., contextual) knowledge.
FCMR: Robust Evaluation of Financial Cross-Modal Multi-Hop Reasoning
Real-world decision-making often requires integrating and reasoning over information from multiple modalities.
Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts
When using large language models (LLMs) in knowledge-intensive tasks, such as open-domain question answering, external context can bridge the gap between external knowledge and the LLMs' parametric knowledge.
Subgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning
To this end, we propose a Subgraph-Aware Training framework for KGC (SATKGC) with two ideas: (i) subgraph-aware mini-batching to encourage hard negative sampling and to mitigate an imbalance in the frequency of entity occurrences during training, and (ii) new contrastive learning to focus more on harder in-batch negative triples and harder positive triples in terms of the structural properties of the knowledge graph.
Revisiting the Impact of Pursuing Modularity for Code Generation
Modular programming, which aims to construct the final program by integrating smaller, independent building blocks, has been regarded as a desirable practice in software development.
Investigating the Influence of Prompt-Specific Shortcuts in AI Generated Text Detection
Based on the findings, we further train the classifier with the dataset augmented by FAILOpt prompt.
Aligning Language Models to Explicitly Handle Ambiguity
To address these issues, we propose Alignment with Perceived Ambiguity (APA), a novel pipeline that aligns LLMs to manage ambiguous queries by leveraging their own assessment of ambiguity (i. e., perceived ambiguity).
BlendX: Complex Multi-Intent Detection with Blended Patterns
Task-oriented dialogue (TOD) systems are commonly designed with the presumption that each utterance represents a single intent.
Hyper-CL: Conditioning Sentence Representations with Hypernetworks
While the introduction of contrastive learning frameworks in sentence representation learning has significantly contributed to advancements in the field, it still remains unclear whether state-of-the-art sentence embeddings can capture the fine-grained semantics of sentences, particularly when conditioned on specific perspectives.
Analysis of Multi-Source Language Training in Cross-Lingual Transfer
The successful adaptation of multilingual language models (LMs) to a specific language-task pair critically depends on the availability of data tailored for that condition.
X-SNS: Cross-Lingual Transfer Prediction through Sub-Network Similarity
Cross-lingual transfer (XLT) is an emergent ability of multilingual language models that preserves their performance on a task to a significant extent when evaluated in languages that were not included in the fine-tuning process.
Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP
When deploying machine learning systems to the wild, it is highly desirable for them to effectively leverage prior knowledge to the unfamiliar domain while also firing alarms to anomalous inputs.
Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-shot In-Context Learners
Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning.
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble
Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience.
Revisiting the Practical Effectiveness of Constituency Parse Extraction from Pre-trained Language Models
Constituency Parse Extraction from Pre-trained Language Models (CPE-PLM) is a recent paradigm that attempts to induce constituency parse trees relying only on the internal knowledge of pre-trained language models.
Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator
Large-scale pre-trained language models (PLMs) are well-known for being capable of solving a task simply by conditioning a few input-label pairs dubbed demonstrations on a prompt without being explicitly tuned for the desired downstream task.
HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization
We propose a unified framework that enables us to consider various aspects of contextualization at different levels to better identify the idiomaticity of multi-word expressions.
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations
Despite recent explosion of interests in in-context learning, the underlying mechanism and the precise impact of the quality of demonstrations remain elusive.
Self-Guided Contrastive Learning for BERT Sentence Representations
In this work, we propose a contrastive learning method that utilizes self-guidance for improving the quality of BERT sentence representations.
Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads
Here, we propose a novel fully unsupervised parsing approach that extracts constituency trees from PLM attention heads.
IDS at SemEval-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?
We propose a novel method that enables us to determine words that deserve to be emphasized from written text in visual media, relying only on the information from the self-attention distributions of pre-trained language models (PLMs).
Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models
As it has been unveiled that pre-trained language models (PLMs) are to some extent capable of recognizing syntactic concepts in natural language, much effort has been made to develop a method for extracting complete (binary) parses from PLMs without training separate parsers.
Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction
With the recent success and popularity of pre-trained language models (LMs) in natural language processing, there has been a rise in efforts to understand their inner workings.
Summary Level Training of Sentence Rewriting for Abstractive Summarization
As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then paraphrasing the selected ones to generate a summary.
Ranked #6 on
Extractive Text Summarization
on CNN / Daily Mail
Abstractive Text Summarization
Extractive Text Summarization
+4
Don't Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja
We propose a simple yet effective approach for improving Korean word representations using additional linguistic annotation (i. e. Hanja).
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching
We present a latent variable model for predicting the relationship between a pair of text sequences.
Cell-aware Stacked LSTMs for Modeling Sentences
We propose a method of stacking multiple long short-term memory (LSTM) layers for modeling sentences.
Ranked #11 on
Sentiment Analysis
on SST-5 Fine-grained classification
Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations
Most existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing.
SNU\_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension
We present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018.
Element-wise Bilinear Interaction for Sentence Matching
When we build a neural network model predicting the relationship between two sentences, the most general and intuitive approach is to use a Siamese architecture, where the sentence vectors obtained from a shared encoder is given as input to a classifier.
SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension
We present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018.
A Syllable-based Technique for Word Embeddings of Korean Words
Word embedding has become a fundamental component to many NLP tasks such as named entity recognition and machine translation.
