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Found 38 papers, 19 papers with code
Within-Between Lexical Relation Classification
We propose the novel \textit{Within-Between} Relation model for recognizing lexical-semantic relations between words.
Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance
Despite it being the cornerstone of BPE, the most common tokenization algorithm, the importance of compression in the tokenization process is still unclear.
Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models
Inspecting the information encoded in hidden representations of large language models (LLMs) can explain models' behavior and verify their alignment with human values.
Optimizing Retrieval-augmented Reader Models via Token Elimination
Fusion-in-Decoder (FiD) is an effective retrieval-augmented language model applied across a variety of open-domain tasks, such as question answering, fact checking, etc.
The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models
In this paper, we explore the behavior of LLMs when presented with (un)answerable queries.
A Comprehensive Evaluation of Tool-Assisted Generation Strategies
A growing area of research investigates augmenting language models with tools (e. g., search engines, calculators) to overcome their shortcomings (e. g., missing or incorrect knowledge, incorrect logical inferences).
Dont Add, dont Miss: Effective Content Preserving Generation from Pre-Selected Text Spans
Further, we substantially improve the silver training data quality via GPT-4 distillation.
Representation Learning via Variational Bayesian Networks
We present Variational Bayesian Network (VBN) - a novel Bayesian entity representation learning model that utilizes hierarchical and relational side information and is particularly useful for modeling entities in the ``long-tail'', where the data is scarce.
Revisiting Sentence Union Generation as a Testbed for Text Consolidation
In this paper, we suggest revisiting the sentence union generation task as an effective well-defined testbed for assessing text consolidation capabilities, decoupling the consolidation challenge from subjective content selection.
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering
The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks.
Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks
Data contamination has become prevalent and challenging with the rise of models pretrained on large automatically-crawled corpora.
Cross-document Event Coreference Search: Task, Dataset and Modeling
The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents.
Cross Document Coreference Resolution
Open-Domain Question Answering
+2
Interpreting BERT-based Text Similarity via Activation and Saliency Maps
Recently, there has been growing interest in the ability of Transformer-based models to produce meaningful embeddings of text with several applications, such as text similarity.
MetricBERT: Text Representation Learning via Self-Supervised Triplet Training
We present MetricBERT, a BERT-based model that learns to embed text under a well-defined similarity metric while simultaneously adhering to the ``traditional'' masked-language task.
QASem Parsing: Text-to-text Modeling of QA-based Semantics
Several recent works have suggested to represent semantic relations with questions and answers, decomposing textual information into separate interrogative natural language statements.
LM-Debugger: An Interactive Tool for Inspection and Intervention in Transformer-Based Language Models
The opaque nature and unexplained behavior of transformer-based language models (LMs) have spurred a wide interest in interpreting their predictions.
Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps
Transformer-based language models significantly advanced the state-of-the-art in many linguistic tasks.
Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space
Transformer-based language models (LMs) are at the core of modern NLP, but their internal prediction construction process is opaque and largely not understood.
Long Context Question Answering via Supervised Contrastive Learning
Long-context question answering (QA) tasks require reasoning over a long document or multiple documents.
Proposition-Level Clustering for Multi-Document Summarization
Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition.
Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates
Next, we propose a novel hybrid recommendation algorithm that bridges these two conflicting objectives and enables a harmonized balance between preserving high accuracy for warm items while effectively promoting completely cold items.
iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration
We introduce iFacetSum, a web application for exploring topical document sets.
GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps
The algorithmic advantages of GAM are explained in detail, and validated empirically, where it is shown that GAM outperforms its alternatives across various tasks and datasets.
Denoising Word Embeddings by Averaging in a Shared Space
We introduce a new approach for smoothing and improving the quality of word embeddings.
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference
Hence, we introduce SDR, a self-supervised method for document similarity that can be applied to documents of arbitrary length.
On the Evolution of Word Order
We also show that adding information to the sentence, such as case markers and noun-verb distinction, reduces the need for fixed word order, in accordance with the typological findings.
CDLM: Cross-Document Language Modeling
We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective.
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A Mixture of Variational Autoencoders for Deep Clustering
In this study we propose a deep clustering algorithm that utilizes variational auto encoder (VAE) framework with a multi encoder-decoder neural architecture.
Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
Error correction codes are an integral part of communication applications and boost the reliability of transmission.
RecoBERT: A Catalog Language Model for Text-Based Recommendations
In addition, we introduce a new language understanding task for wine recommendations using similarities based on professional wine reviews.
Paraphrasing vs Coreferring: Two Sides of the Same Coin
We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution.
Bayesian Hierarchical Words Representation Learning
BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors.
Attentive Item2Vec: Neural Attentive User Representations
However, it is possible that a certain early movie may become suddenly more relevant in the presence of a popular sequel movie.
perm2vec: Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
Error correction codes are an integral part of communication applications, boosting the reliability of transmission.
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding
In this paper, we introduce Distilled Sentence Embedding (DSE) - a model that is based on knowledge distillation from cross-attentive models, focusing on sentence-pair tasks.
Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders
We first consider the reconstruction of uncoded data symbols transmitted over a noisy linear intersymbol interference (ISI) channel, with an unknown impulse response, without using pilot symbols.
Blind Channel Equalization using Variational Autoencoders
A new maximum likelihood estimation approach for blind channel equalization, using variational autoencoders (VAEs), is introduced.
Inducing Regular Grammars Using Recurrent Neural Networks
Grammar induction is the task of learning a grammar from a set of examples.
