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Found 28 papers, 13 papers with code
GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages
Language identification is one of the fundamental tasks in natural language processing that is a prerequisite to data processing and numerous applications.
Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling
Our findings reveal a correlation between the types of trolling encountered and the preferred RS.
An Efficient Sign Language Translation Using Spatial Configuration and Motion Dynamics with LLMs
With this in mind, we introduce Spatial and Motion-based Sign Language Translation (SpaMo), a novel LLM-based SLT framework.
Gloss-free Sign Language Translation
Sign Language Translation
+2
DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation
Recent advancements in Large Language Models (LLMs) have significantly improved their performance across various Natural Language Processing (NLP) tasks.
Universal Gloss-level Representation for Gloss-free Sign Language Translation and Production
Gloss-free methods have emerged to address these limitations, but they often depend on external sign language data or dictionaries, failing to completely eliminate the need for gloss annotations.
Gloss-free Sign Language Translation
Self-Supervised Learning
+4
Database-Augmented Query Representation for Information Retrieval
Information retrieval models that aim to search for the documents relevant to the given query have shown many successes, which have been applied to diverse tasks.
Self-Knowledge Distillation for Learning Ambiguity
To address this issue, we propose a novel self-knowledge distillation method that enables models to learn label distributions more accurately by leveraging knowledge distilled from their lower layers.
Ask LLMs Directly, "What shapes your bias?": Measuring Social Bias in Large Language Models
To this end, we propose a novel strategy to intuitively quantify these social perceptions and suggest metrics that can evaluate the social biases within LLMs by aggregating diverse social perceptions.
Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations
The robustness of recent Large Language Models (LLMs) has become increasingly crucial as their applicability expands across various domains and real-world applications.
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA).
Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering
Large language models (LLMs) enable zero-shot approaches in open-domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever.
Test-Time Self-Adaptive Small Language Models for Question Answering
Moreover, further finetuning LMs with labeled datasets is often infeasible due to their absence, but it is also questionable if we can transfer smaller LMs having limited knowledge only with unlabeled test data.
Knowledge-Augmented Language Model Verification
Recent Language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters.
Autoregressive Sign Language Production: A Gloss-Free Approach with Discrete Representations
Gloss-free Sign Language Production (SLP) offers a direct translation of spoken language sentences into sign language, bypassing the need for gloss intermediaries.
Deep Model Compression Also Helps Models Capture Ambiguity
Natural language understanding (NLU) tasks face a non-trivial amount of ambiguous samples where veracity of their labels is debatable among annotators.
Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning
To address this problem, we further introduce a novel contrastive learning strategy, making sure to reflect previous turns when retrieving the phrase for the current context, by maximizing representational similarities of consecutive turns in a conversation while minimizing irrelevant conversational contexts.
A Simple and Flexible Modeling for Mental Disorder Detection by Learning from Clinical Questionnaires
Our detailed analysis shows that the proposed model is effective at leveraging domain knowledge, transferable to other mental disorders, and providing interpretable detection results.
Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker
Along with highlighting the impact of optimization on the zero-shot re-ranker, we propose a novel discrete prompt optimization method, Constrained Prompt generation (Co-Prompt), with the metric estimating the optimum for re-ranking.
Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement
Conversational Question Answering (ConvQA) models aim at answering a question with its relevant paragraph and previous question-answer pairs that occurred during conversation multiple times.
Non-Autoregressive Sign Language Production via Knowledge Distillation
We propose a novel NAR-SLP model via Knowledge Distillation (KD) to address these problems.
ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls
In particular, we constructed a pair-wise dataset that includes troll comments and counter responses with labeled response strategies, which enables models fine-tuned on our dataset to generate responses by varying counter responses according to the specified strategy.
Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation
Dense retrieval models, which aim at retrieving the most relevant document for an input query on a dense representation space, have gained considerable attention for their remarkable success.
Ranked #1000000000 on
Passage Retrieval
on Natural Questions
Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
In this paper, we propose an Unsupervised Document Expansion with Generation (UDEG) framework with a pre-trained language model, which generates diverse supplementary sentences for the original document without using labels on query-document pairs for training.
Extraction of Gene-Environment Interaction from the Biomedical Literature
Genetic information in the literature has been extensively looked into for the purpose of discovering the etiology of a disease.
