1 citations · 4 across the 18 of their papers we have counts for
18 papers
PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails
Seungbin Yang, Chaewoon Ki, Dohyun Lee +2
Recent advances in large language models have enabled web agents to autonomously execute complex tasks. In practice, users frequently provide underspecified instructions, requiring…
LiveWeb-IE: A Benchmark For Online Web Information Extraction
Seungbin Yang, Jihwan Kim, Jaemin Choi +4
Web information extraction (WIE) is the task of automatically extracting data from web pages, offering high utility for various applications. The evaluation of WIE systems has trad…
Not the Example, but the Process: How Self-Generated Examples Enhance LLM Reasoning
Daehoon Gwak, Minseo Jung, Junwoo Park +4
Recent studies have shown that Large Language Models (LLMs) can improve their reasoning performance through self-generated few-shot examples, achieving results comparable to manual…
Reward-Weighted Sampling: Enhancing Non-Autoregressive Characteristics in Masked Diffusion LLMs
Daehoon Gwak, Minseo Jung, Junwoo Park +4
Masked diffusion models (MDMs) offer a promising non-autoregressive alternative for large language modeling. Standard decoding methods for MDMs, such as confidence-based sampling,…
AMRG: Extend Vision Language Models for Automatic Mammography Report Generation
Nak-Jun Sung, Donghyun Lee, Bo Hwa Choi +1
Mammography report generation is a critical yet underexplored task in medical AI, characterized by challenges such as multiview image reasoning, high-resolution visual cues, and un…
Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling
Daehoon Gwak, Junwoo Park, Minho Park +4
Predicting future international events from textual information, such as news articles, has tremendous potential for applications in global policy, strategic decision-making, and g…
Breaking Chains: Unraveling the Links in Multi-Hop Knowledge Unlearning
Minseok Choi, ChaeHun Park, Dohyun Lee +1
Large language models (LLMs) serve as giant information stores, often including personal or copyrighted data, and retraining them from scratch is not a viable option. This has led…
Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts
ChaeHun Park, Hojun Cho, Jaegul Choo
This paper explores integrating Automatic Speech Recognition (ASR) into natural language query systems to improve weather forecasting efficiency for Korean meteorologists. We addre…
Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering
ChaeHun Park, Koanho Lee, Hyesu Lim +5
Building a reliable visual question answering~(VQA) system across different languages is a challenging problem, primarily due to the lack of abundant samples for training. To addre…
Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration
ChaeHun Park, Yujin Baek, Jaeseok Kim +3
To create culturally inclusive vision-language models (VLMs), developing a benchmark that tests their ability to address culturally relevant questions is essential. Existing approa…
The Comparative Trap: Pairwise Comparisons Amplifies Biased Preferences of LLM Evaluators
Hawon Jeong, ChaeHun Park, Jimin Hong +2
As large language models (LLMs) are increasingly used as evaluators for natural language generation tasks, ensuring unbiased assessments is essential. However, LLM evaluators often…
Can Tool-augmented Large Language Models be Aware of Incomplete Conditions?
Seungbin Yang, ChaeHun Park, Taehee Kim +1
Recent advancements in integrating large language models (LLMs) with tools have allowed the models to interact with real-world environments. However, these tool-augmented LLMs ofte…
PairEval: Open-domain Dialogue Evaluation with Pairwise Comparison
ChaeHun Park, Minseok Choi, Dohyun Lee +1
Building a reliable and automated evaluation metric is a necessary but challenging problem for open-domain dialogue systems. Recent studies proposed evaluation metrics that assess…
Learning to Diversify Neural Text Generation via Degenerative Model
Jimin Hong, ChaeHun Park, Jaegul Choo
Neural language models often fail to generate diverse and informative texts, limiting their applicability in real-world problems. While previous approaches have proposed to address…
DEnsity: Open-domain Dialogue Evaluation Metric using Density Estimation
ChaeHun Park, Seungil Chad Lee, Daniel Rim +1
Despite the recent advances in open-domain dialogue systems, building a reliable evaluation metric is still a challenging problem. Recent studies proposed learnable metrics based o…
Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection Task
Nyoungwoo Lee, ChaeHun Park, Ho-Jin Choi +1
In retrieval-based dialogue systems, a response selection model acts as a ranker to select the most appropriate response among several candidates. However, such selection models te…
Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
Soyeong Jeong, Jinheon Baek, ChaeHun Park +1
One of the challenges in information retrieval (IR) is the vocabulary mismatch problem, which happens when the terms between queries and documents are lexically different but seman…
An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation
ChaeHun Park, Sangwoo Seo
Measuring the similarity between two different sentential arguments is an important task in argument mining. However, one of the challenges in this field is that the dataset must b…