activity
20232026
most citedSycophancy Is Not One Thing: Causal Separation of Sycophantic Behaviors in LLMs

1 citations · 2 across the 20 of their papers we have counts for

collaborators

20 papers

cs.CL2026

Chiaroscuro for Emotions: A Contrastive Emotion Benchmark Grounded in Appraisal Theory

Divyesh Bommana, Mohammad Saim, Tianyu Jiang

Emotion recognition benchmarks often predict one emotion per text, missing many real-world scenarios where two people arrive at opposing emotions from a single shared event. For ex…

cs.CL2026

Emotional Labor Strategy Preferences in LLM Personas

Mohammad Saim, Tianyu Jiang

Emotional labor is the effortful management of emotional displays to meet social or professional expectations. Personality traits have been correlated with emotional labor strategi…

cs.CL2026

Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing

Yiming Liu, Ziyue Zhang, Zhichao Xu +4

Rewriting inputs to improve frozen downstream models has become a common strategy in modern NLP pipelines. Prior work on incremental dialogue discourse parsing (DDP) shows that sup…

cs.CL2026

Sycophantic Praise: Evaluating Excessive Praise in Language Models

Daniel Vennemeyer, Phan Anh Duong, Meryl Ye +2

Sycophancy in language models is typically studied as excessive agreement or validation, while explicit praise and flattery have received comparatively little attention. We argue t…

cs.CL2026

Cross-Lingual Steering for Figurative Language Generation

Linfeng Liu, Tiffany Zhan, Louie Hong Yao +2

Cross-lingual activation steering has been studied mainly for language-agnostic properties such as factual knowledge and reasoning. Its transferability remains unclear for figurati…

cs.CL2026

Do Emotions Influence Moral Judgment in Large Language Models?

Mohammad Saim, Tianyu Jiang

Large language models have been extensively studied for emotion recognition and moral reasoning as distinct capabilities, yet the extent to which emotions influence moral judgment…

cs.CL2026

Rhetorical Questions in LLM Representations: A Linear Probing Study

Louie Hong Yao, Vishesh Anand, Yuan Zhuang +1

Rhetorical questions are asked not to seek information but to persuade or signal stance. How large language models internally represent them remains unclear. We analyze rhetorical…

cs.CL2026

Exploring Concreteness Through a Figurative Lens

Saptarshi Ghosh, Tianyu Jiang

Static concreteness ratings are widely used in NLP, yet a word's concreteness can shift with context, especially in figurative language such as metaphor, where common concrete noun…

cs.CL2026

MetFuse: Figurative Fusion between Metonymy and Metaphor

Saptarshi Ghosh, Tianyu Jiang

Metonymy and metaphor often co-occur in natural language, yet computational work has studied them largely in isolation. We introduce a framework that transforms a literal sentence…

cs.CL2026

A Computational Approach to Visual Metonymy

Saptarshi Ghosh, Linfeng Liu, Tianyu Jiang

Images often communicate more than they literally depict: a set of tools can suggest an occupation and a cultural artifact can suggest a tradition. This kind of indirect visual ref…

cs.CL2025

Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models

Mohammad Saim, Phan Anh Duong, Cat Luong +2

The embodiment of emotional reactions from body parts contains rich information about our affective experiences. We propose a framework that utilizes state-of-the-art large vision-…

cs.CL2025

GuessingGame: Measuring the Informativeness of Open-Ended Questions in Large Language Models

Dylan Hutson, Daniel Vennemeyer, Aneesh Deshmukh +2

We introduce GuessingGame, a protocol for evaluating large language models (LLMs) as strategic question-askers in open-ended, open-domain settings. A Guesser LLM identifies a hidde…

cs.CL2025

Do LLMs Encode Frame Semantics? Evidence from Frame Identification

Jayanth Krishna Chundru, Rudrashis Poddar, Jie Cao +1

We investigate whether large language models encode latent knowledge of frame semantics, focusing on frame identification, a core challenge in frame semantic parsing that involves…

cs.AI2025

JE-IRT: A Geometric Lens on LLM Abilities through Joint Embedding Item Response Theory

Louie Hong Yao, Nicholas Jarvis, Tiffany Zhan +3

Standard LLM evaluation practices compress diverse abilities into single scores, obscuring their inherently multidimensional nature. We present JE-IRT, a geometric item-response fr…

cs.CL2025★ 1 cited

Sycophancy Is Not One Thing: Causal Separation of Sycophantic Behaviors in LLMs

Daniel Vennemeyer, Phan Anh Duong, Tiffany Zhan +1

Large language models (LLMs) often exhibit sycophantic behaviors -- such as excessive agreement with or flattery of the user -- but it is unclear whether these behaviors arise from…

cs.CL2025

Evaluating the Impact of Verbal Multiword Expressions on Machine Translation

Linfeng Liu, Saptarshi Ghosh, Tianyu Jiang

Verbal multiword expressions (VMWEs) remain difficult for machine translation because their meanings are often not recoverable from their component words. In this study, we analyze…

cs.CL2025

Towards Robust Evaluation of Visual Activity Recognition: Resolving Verb Ambiguity with Sense Clustering

Louie Hong Yao, Nicholas Jarvis, Tianyu Jiang

Evaluating visual activity recognition systems is challenging due to inherent ambiguities in verb semantics and image interpretation. When describing actions in images, synonymous…

cs.CL2025★ 1 cited

CHEER-Ekman: Fine-grained Embodied Emotion Classification

Phan Anh Duong, Cat Luong, Divyesh Bommana +1

Emotions manifest through physical experiences and bodily reactions, yet identifying such embodied emotions in text remains understudied. We present an embodied emotion classificat…

cs.CL2025

ConMeC: A Dataset for Metonymy Resolution with Common Nouns

Saptarshi Ghosh, Tianyu Jiang

Metonymy plays an important role in our daily communication. People naturally think about things using their most salient properties or commonly related concepts. For example, by s…

cs.CL2023

Classifying Organizations for Food System Ontologies using Natural Language Processing

Tianyu Jiang, Sonia Vinogradova, Nathan Stringham +7

Our research explores the use of natural language processing (NLP) methods to automatically classify entities for the purpose of knowledge graph population and integration with foo…