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Found 34 papers, 18 papers with code
Attribute Injection for Pretrained Language Models: A New Benchmark and an Efficient Method
Metadata attributes (e. g., user and product IDs from reviews) can be incorporated as additional inputs to neural-based NLP models, by expanding the architecture of the models to improve performance.
SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs
While incorporating new information with the retrieval of relevant passages is a promising way to improve QA with LLMs, the existing methods often require additional fine-tuning which becomes infeasible with recent LLMs.
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
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.
Query-Efficient Black-Box Red Teaming via Bayesian Optimization
To this end, we propose Bayesian red teaming (BRT), novel query-efficient black-box red teaming methods based on Bayesian optimization, which iteratively identify diverse positive test cases leading to model failures by utilizing the pre-defined user input pool and the past evaluations.
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
We first present CAMBIGNQ, a dataset consisting of 5, 654 ambiguous questions, each with relevant passages, possible answers, and a clarification question.
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.
Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?
Task-oriented dialogue (TOD) systems are mainly based on the slot-filling-based TOD (SF-TOD) framework, in which dialogues are broken down into smaller, controllable units (i. e., slots) to fulfill a specific task.
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning
Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications.
Keep Me Updated! Memory Management in Long-term Conversations
Remembering important information from the past and continuing to talk about it in the present are crucial in long-term conversations.
Continuous Decomposition of Granularity for Neural Paraphrase Generation
While Transformers have had significant success in paragraph generation, they treat sentences as linear sequences of tokens and often neglect their hierarchical information.
Leveraging Pre-Trained Language Models to Streamline Natural Language Interaction for Self-Tracking
Current natural language interaction for self-tracking tools largely depends on bespoke implementation optimized for a specific tracking theme and data format, which is neither generalizable nor scalable to a tremendous design space of self-tracking.
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models
Based on a recent trend that multimodal generative evaluations exploit a vison-and-language pre-trained model, we propose the negative Gaussian cross-mutual information using the CLIP features as a unified metric, coined by Mutual Information Divergence (MID).
Ranked #1 on
Human Judgment Classification
on Pascal-50S
Hallucination Pair-wise Detection (1-ref)
Hallucination Pair-wise Detection (4-ref)
+5
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.
Two-Step Question Retrieval for Open-Domain QA
These models have shown a significant increase in inference speed, but at the cost of lower QA performance compared to the retriever-reader models.
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models
In this work, we study the challenge of imposing roles on open-domain dialogue systems, with the goal of making the systems maintain consistent roles while conversing naturally with humans.
On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model
Many recent studies on large-scale language models have reported successful in-context zero- and few-shot learning ability.
Plug-and-Play Adaptation for Continuously-updated QA
To this end, we first propose a novel task--Continuously-updated QA (CuQA)--in which multiple large-scale updates are made to LMs, and the performance is measured with respect to the success in adding and updating knowledge while retaining existing knowledge.
Response Generation with Context-Aware Prompt Learning
Pre-trained language models (PLM) have marked a huge leap in neural dialogue modeling.
Efficient Attribute Injection for Pretrained Language Models
Metadata attributes (e. g., user and product IDs from reviews) can be incorporated as additional inputs to neural-based NLP models, by modifying the architecture of the models, in order to improve their performance.
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data.
Weakly Supervised Pre-Training for Multi-Hop Retriever
In multi-hop QA, answering complex questions entails iterative document retrieval for finding the missing entity of the question.
NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation
We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation.
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation
Large-scale language models such as GPT-3 are excellent few-shot learners, allowing them to be controlled via natural text prompts.
ST-BERT: Cross-modal Language Model Pre-training For End-to-end Spoken Language Understanding
Language model pre-training has shown promising results in various downstream tasks.
CareCall: a Call-Based Active Monitoring Dialog Agent for Managing COVID-19 Pandemic
Tracking suspected cases of COVID-19 is crucial to suppressing the spread of COVID-19 pandemic.
ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers
Automatic speech recognition (ASR) via call is essential for various applications, including AI for contact center (AICC) services.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Efficient Dialogue State Tracking by Selectively Overwriting Memory
This mechanism consists of two steps: (1) predicting state operation on each of the memory slots, and (2) overwriting the memory with new values, of which only a few are generated according to the predicted state operations.
Ranked #10 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation
Answerer in Questioner's Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems.
Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog
Goal-oriented dialogue tasks occur when a questioner asks an action-oriented question and an answerer responds with the intent of letting the questioner know a correct action to take.
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Catastrophic forgetting is a problem of neural networks that loses the information of the first task after training the second task.
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals
To solve this issue, the subgoal and option framework have been proposed.
Multimodal Residual Learning for Visual QA
We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning.
Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy
The proposed architecture consists of deep representation learners and fast learnable shallow kernel networks, both of which synergize to track the information of new data.
