294 citations · 307 across the 17 of their papers we have counts for
18 papers
Can LLMs Tell Stories Out of Order? A Corpus and Benchmark for Nonlinear Narrative Generation
Syed Rifat Raiyan, Md. Tariquzzaman, Md Farhan Ishmam +1
Every story has two orders: the order in which its events happen and the order in which they are told. A story is nonlinear when the two differ. We introduce a benchmark of 83 publ…
GTA: Generating Long-Horizon Tasks for Web Agents at Scale
Tenghao Huang, Kung-Hsiang Huang, Prafulla Kumar Choubey +4
Web agents, which couple language models with browsing and tool-use capabilities, show promise as open web assistants. Yet progress is increasingly limited by the lack of scalable,…
Cooking Up Risks: Benchmarking and Reducing Food Safety Risks in Large Language Models
Weidi Luo, Xiaofei Wen, Tenghao Huang +6
Food safety directly affects human health in daily life, as unsafe preparation or storage can lead to contamination and foodborne illness. PwC's 2025 consumer survey reports that 4…
Optimizing Diversity and Quality through Base-Aligned Model Collaboration
Yichen Wang, Chenghao Yang, Tenghao Huang +3
Alignment has greatly improved large language models (LLMs)' output quality at the cost of diversity, yielding highly similar outputs across generations, especially in open-ended g…
Teaching Language Models To Gather Information Proactively
Tenghao Huang, Sihao Chen, Muhao Chen +4
Large language models (LLMs) are increasingly expected to function as collaborative partners, engaging in back-and-forth dialogue to solve complex, ambiguous problems. However, cur…
DiscoSum: Discourse-aware News Summarization
Alexander Spangher, Tenghao Huang, Jialiang Gu +2
Recent advances in text summarization have predominantly leveraged large language models to generate concise summaries. However, language models often do not maintain long-term dis…
R2D2: Remembering, Replaying and Dynamic Decision Making with a Reflective Agentic Memory
Tenghao Huang, Kinjal Basu, Ibrahim Abdelaziz +3
The proliferation of web agents necessitates advanced navigation and interaction strategies within complex web environments. Current models often struggle with efficient navigation…
FoodPuzzle: Developing Large Language Model Agents as Flavor Scientists
Tenghao Huang, Donghee Lee, John Sweeney +5
Flavor development in the food industry is increasingly challenged by the need for rapid innovation and precise flavor profile creation. Traditional flavor research methods typical…
Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation
Dongwon Jung, Qin Liu, Tenghao Huang +2
Retrieval-augmented generation (RAG) improves large language models (LMs) by incorporating non-parametric knowledge through evidence retrieved from external sources. However, it of…
Are Large Language Models Capable of Generating Human-Level Narratives?
Yufei Tian, Tenghao Huang, Miri Liu +5
This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression. We introduce a novel computational framework to analyze narr…
Red Teaming Language Models for Processing Contradictory Dialogues
Xiaofei Wen, Bangzheng Li, Tenghao Huang +1
Most language models currently available are prone to self-contradiction during dialogues. To mitigate this issue, this study explores a novel contradictory dialogue processing tas…
Planning and Editing What You Retrieve for Enhanced Tool Learning
Tenghao Huang, Dongwon Jung, Muhao Chen
Recent advancements in integrating external tools with Large Language Models (LLMs) have opened new frontiers, with applications in mathematical reasoning, code generators, and sma…
Affective and Dynamic Beam Search for Story Generation
Tenghao Huang, Ehsan Qasemi, Bangzheng Li +4
Storytelling's captivating potential makes it a fascinating research area, with implications for entertainment, education, therapy, and cognitive studies. In this paper, we propose…
Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models
Nikhil Kandpal, Brian Lester, Mohammed Muqeeth +6
Currently, most machine learning models are trained by centralized teams and are rarely updated. In contrast, open-source software development involves the iterative development of…
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach
Chao Zhao, Faeze Brahman, Tenghao Huang +1
Pre-trained models (PTMs) have lead to great improvements in natural language generation (NLG). However, it is still unclear how much commonsense knowledge they possess. With the g…
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
Haokun Liu, Derek Tam, Mohammed Muqeeth +4
Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training…
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury +3
A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-do…
Uncovering Implicit Gender Bias in Narratives through Commonsense Inference
Tenghao Huang, Faeze Brahman, Vered Shwartz +1
Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation. We study gender biases associated with…