1 citations · 1 across the 12 of their papers we have counts for
14 papers
Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives
Changgeon Ko, Jisu Shin, Hoyun Song +3
Large language model (LLM) agents are increasingly acting as human delegates in multi-agent environments, where a representative agent integrates diverse peer perspectives to make…
Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation
Huije Lee, Jisu Shin, Hoyun Song +2
Static benchmarks for harmful content detection face limitations in scalability and diversity, and may also be affected by contamination from web-scale pre-training corpora. To add…
RoleConflictBench: A Benchmark of Role Conflict Scenarios for Evaluating LLMs' Contextual Sensitivity
Jisu Shin, Hoyun Song, Juhyun Oh +4
People often encounter role conflicts -- social dilemmas where the expectations of multiple roles clash and cannot be simultaneously fulfilled. As large language models (LLMs) incr…
Temporal Information Retrieval via Time-Specifier Model Merging
SeungYoon Han, Taeho Hwang, Sukmin Cho +4
The rapid expansion of digital information and knowledge across structured and unstructured sources has heightened the importance of Information Retrieval (IR). While dense retriev…
Spotting Out-of-Character Behavior: Atomic-Level Evaluation of Persona Fidelity in Open-Ended Generation
Jisu Shin, Juhyun Oh, Eunsu Kim +2
Ensuring persona fidelity in large language models (LLMs) is essential for maintaining coherent and engaging human-AI interactions. However, LLMs often exhibit Out-of-Character (OO…
Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation
Hoyun Song, Huije Lee, Jisu Shin +3
The detection of mental health problems from social media and the interpretation of these results have been extensively explored. Research has shown that incorporating clinical sym…
A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings
Fitsum Gaim, Hoyun Song, Huije Lee +3
Content moderation research has recently made significant advances, but remains limited in serving the majority of the world's languages due to the lack of resources, leaving milli…
Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding
Sukmin Cho, Sangjin Choi, Taeho Hwang +6
Accelerating inference in Large Language Models (LLMs) is critical for real-time interactions, as they have been widely incorporated into real-world services. Speculative decoding,…
EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation
Taeho Hwang, Sukmin Cho, Soyeong Jeong +3
We introduce EXIT, an extractive context compression framework that enhances both the effectiveness and efficiency of retrieval-augmented generation (RAG) in question answering (QA…
Different Bias Under Different Criteria: Assessing Bias in LLMs with a Fact-Based Approach
Changgeon Ko, Jisu Shin, Hoyun Song +2
Large language models (LLMs) often reflect real-world biases, leading to efforts to mitigate these effects and make the models unbiased. Achieving this goal requires defining clear…
Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling
Huije Lee, Hoyun Song, Jisu Shin +3
Trolling in online communities typically involves disruptive behaviors such as provoking anger and manipulating discussions, leading to a polarized atmosphere and emotional distres…
Ask LLMs Directly, "What shapes your bias?": Measuring Social Bias in Large Language Models
Jisu Shin, Hoyun Song, Huije Lee +2
Social bias is shaped by the accumulation of social perceptions towards targets across various demographic identities. To fully understand such social bias in large language models…
A Simple and Flexible Modeling for Mental Disorder Detection by Learning from Clinical Questionnaires
Hoyun Song, Jisu Shin, Huije Lee +1
Social media is one of the most highly sought resources for analyzing characteristics of the language by its users. In particular, many researchers utilized various linguistic feat…
ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls
Huije Lee, Young Ju NA, Hoyun Song +2
Online trolls increase social costs and cause psychological damage to individuals. With the proliferation of automated accounts making use of bots for trolling, it is difficult for…