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Inho Kang

7 accepted papers

2024

RADCoT: Retrieval-Augmented Distillation to Specialization Models for Generating Chain-of-Thoughts in Query Expansion

COLING 2024main

Large language models (LLMs) have demonstrated superior performance to that of small language models (SLM) in information retrieval for various subtasks including dense retrieval, reranking, query expansion, and pseudo-document generation. However, the parameter sizes of LLMs are extremely large, ma…

2024

SLM as Guardian: Pioneering AI Safety with Small Language Model

EMNLP 2024industry

Most prior safety research of large language models (LLMs) has focused on enhancing the alignment of LLMs to better suit the safety requirements of their use cases. However, internalizing such safeguard features into larger models brought challenges of higher training cost and unintended degradation…

Cited by 7SourcePDFScholar
2023

RINK: Reader-Inherited Evidence Reranker for Table-and-Text Open Domain Question Answering

AAAI 2023technical

Most approaches used in open-domain question answering on hybrid data that comprises both tabular-and-textual contents are based on a Retrieval-Reader pipeline in which the retrieval module finds relevant 
“heterogenous” evidence for a given question and the reader module generates an answer from th…

Cited by 5SourcePDFScholar
2022

LM-BFF-MS: Improving Few-Shot Fine-tuning of Language Models based on Multiple Soft Demonstration Memory

ACL 2022short

LM-BFF (CITATION) achieves significant few-shot performance by using auto-generated prompts and adding demonstrations similar to an input example. To improve the approach of LM-BFF, this paper proposes LM-BFF-MS—better few-shot fine-tuning of language models with multiple soft demonstrations by maki…

2022

SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification

COLING 2022main

This study proposes Semantic-Infused SElective Graph Reasoning (SISER) for fact verification, which newly presents semantic-level graph reasoning and injects its reasoning-enhanced representation into other types of graph-based and sequence-based reasoning methods. SISER combines three reasoning typ…

Cited by 7SourcePDFScholar
2021

Self-supervised Pre-training and Contrastive Representation Learning for Multiple-choice Video QA

AAAI 2021technical

Video Question Answering (VideoQA) requires fine-grained understanding of both video and language modalities to answer the given questions. In this paper, we propose novel training schemes for multiple-choice video question answering with a self-supervised pre-training stage and a supervised contras…

Cited by 47SourcePDFScholar
2021

What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

EMNLP 2021main

GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less reported by the GPT-3 paper, such as a non-English LM, the performances of different sized models, and the effect of recently…