activity
20182026
most citedKLUE: Korean Language Understanding Evaluation

77 citations · 172 across the 20 of their papers we have counts for

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13 papers · 1 filter

cs.CL2026

XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?

Akhila Yerukola, Jena D. Hwang, Mingqian Zheng +5

When non-expert users ask LLMs for assistance, their queries can often have misconceptions (e.g., "How do I parse XML with regex?"). In such cases, often referred to as the XY-prob…

cs.CL2024★ 2 cited

SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs

Yuling Gu, Oyvind Tafjord, Hyunwoo Kim +4

Large language models (LLMs) are increasingly tested for a "Theory of Mind" (ToM) - the ability to attribute mental states to oneself and others. Yet most evaluations stop at expli…

cs.CL2024

Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models

Chani Jung, Dongkwan Kim, Jiho Jin +5

While humans naturally develop theory of mind (ToM), the capability to understand other people's mental states and beliefs, state-of-the-art large language models (LLMs) underperfo…

cs.CL2024★ 2 cited

Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs

Xuhui Zhou, Zhe Su, Tiwalayo Eisape +2

Recent advances in large language models (LLM) have enabled richer social simulations, allowing for the study of various social phenomena. However, most recent work has used a more…

cs.CL2024★ 1 cited

Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs

Aly M. Kassem, Omar Mahmoud, Niloofar Mireshghallah +5

In this paper, we introduce a black-box prompt optimization method that uses an attacker LLM agent to uncover higher levels of memorization in a victim agent, compared to what is r…

cs.CL2024

Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models

Anthony Sicilia, Hyunwoo Kim, Khyathi Raghavi Chandu +2

Effective interlocutors account for the uncertain goals, beliefs, and emotions of others. But even the best human conversationalist cannot perfectly anticipate the trajectory of a…

cs.CL2023★ 1 cited

FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions

Hyunwoo Kim, Melanie Sclar, Xuhui Zhou +4

Theory of mind (ToM) evaluations currently focus on testing models using passive narratives that inherently lack interactivity. We introduce FANToM, a new benchmark designed to str…

cs.CL2022★ 3 cited

SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization

Hyunwoo Kim, Jack Hessel, Liwei Jiang +9

Data scarcity has been a long standing issue in the field of open-domain social dialogue. To quench this thirst, we present SODA: the first publicly available, million-scale high-q…

cs.CL2022★ 8 cited

ProsocialDialog: A Prosocial Backbone for Conversational Agents

Hyunwoo Kim, Youngjae Yu, Liwei Jiang +5

Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduc…

cs.CL2021

Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes

Hyunwoo Kim, Byeongchang Kim, Gunhee Kim

Empathy is a complex cognitive ability based on the reasoning of others' affective states. In order to better understand others and express stronger empathy in dialogues, we argue…

cs.CL2021★ 77 cited

KLUE: Korean Language Understanding Evaluation

Sungjoon Park, Jihyung Moon, Sungdong Kim +28

We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification,…

cs.CL2020★ 4 cited

Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness

Hyunwoo Kim, Byeongchang Kim, Gunhee Kim

We explore the task of improving persona consistency of dialogue agents. Recent models tackling consistency often train with additional Natural Language Inference (NLI) labels or a…

cs.CL2018★ 60 cited

Abstractive Summarization of Reddit Posts with Multi-level Memory Networks

Byeongchang Kim, Hyunwoo Kim, Gunhee Kim

We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts fr…