Search Results for author:
Found 27 papers, 15 papers with code
QADiver: Interactive Framework for Diagnosing QA Models
Question answering (QA) extracting answers from text to the given question in natural language, has been actively studied and existing models have shown a promise of outperforming human performance when trained and evaluated with SQuAD dataset.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
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
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
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.
Can Language Models be Biomedical Knowledge Bases?
To this end, we create the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs.
Saving Dense Retriever from Shortcut Dependency in Conversational Search
In this paper, we demonstrate the existence of a retrieval shortcut in CS, which causes models to retrieve passages solely relying on partial history while disregarding the latest question.
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.
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.
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
As a result, GST scales the amount of training data up to an order of magnitude that of VisDial (1. 2M to 12. 9M QA data).
Conditional Text Generation
Out-of-Distribution Detection
+1
Generating Information-Seeking Conversations from Unlabeled Documents
In this paper, we introduce a novel framework, SIMSEEK, (Simulating information-Seeking conversation from unlabeled documents), and compare its two variants.
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.
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.
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.
Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data
Through an online study (N = 48) where participants conversed with chatbots driven by different designs of prompts, we assessed how prompt designs and conversation topics affected the conversation flows and users' perceptions of chatbots.
Semi-Parametric Video-Grounded Text Generation
Efficient video-language modeling should consider the computational cost because of a large, sometimes intractable, number of video frames.
Ranked #13 on
Video Question Answering
on NExT-QA
Aligning Large Language Models through Synthetic Feedback
In human evaluation, our model is preferred to Alpaca and Dolly-v2, 55. 0% and 58. 5% of the time, respectively.
Revealing User Familiarity Bias in Task-Oriented Dialogue via Interactive Evaluation
Most task-oriented dialogue (TOD) benchmarks assume users that know exactly how to use the system by constraining the user behaviors within the system's capabilities via strict user goals, namely "user familiarity" bias.
Gradient Ascent Post-training Enhances Language Model Generalization
In this work, we empirically show that updating pretrained LMs (350M, 1. 3B, 2. 7B) with just a few steps of Gradient Ascent Post-training (GAP) on random, unlabeled text corpora enhances its zero-shot generalization capabilities across diverse NLP tasks.
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on the instruction.
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
We first construct the Feedback Collection, a new dataset that consists of 1K fine-grained score rubrics, 20K instructions, and 100K responses and language feedback generated by GPT-4.
Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models
To cope with the challenge, we propose a novel framework, Tree of Clarifications (ToC): It recursively constructs a tree of disambiguations for the AQ -- via few-shot prompting leveraging external knowledge -- and uses it to generate a long-form answer.
LangBridge: Multilingual Reasoning Without Multilingual Supervision
We introduce LangBridge, a zero-shot approach to adapt language models for multilingual reasoning tasks without multilingual supervision.
Aligning Large Language Models by On-Policy Self-Judgment
Existing approaches for aligning large language models with human preferences face a trade-off that requires a separate reward model (RM) for on-policy learning.
KMMLU: Measuring Massive Multitask Language Understanding in Korean
We propose KMMLU, a new Korean benchmark with 35, 030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM.
Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversational Search
Furthermore, we fine-tune a smaller LM using this dataset to align it with the retrievers' preferences as feedback.
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.
