7 citations · 21 across the 12 of their papers we have counts for
12 papers
Harnesses for Inference-Time Alignment over Execution Trajectories
Boyuan Wang, Bochao Li, Minghan Wang +2
Harness engineering has emerged as an important inference-time technique for large language model (LLM) agents, aiming to improve long-term performance through task decomposition a…
GTS: Inference-Time Scaling of Latent Reasoning with a Learnable Gaussian Thought Sampler
Minghan Wang, Ye Bai, Thuy-Trang Vu +2
Inference-time scaling (ITS) in latent reasoning models typically relies on heuristic perturbations, such as dropout or fixed Gaussian noise, to generate diverse candidate trajecto…
NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions
Haolin Yang, Yuxing Long, Zhuoyuan Yu +8
Instruction-following navigation is a key step toward embodied intelligence. Prior benchmarks mainly focus on semantic understanding but overlook systematically evaluating navigati…
Qorgau: Evaluating LLM Safety in Kazakh-Russian Bilingual Contexts
Maiya Goloburda, Nurkhan Laiyk, Diana Turmakhan +11
Large language models (LLMs) are known to have the potential to generate harmful content, posing risks to users. While significant progress has been made in developing taxonomies f…
Loki: An Open-Source Tool for Fact Verification
Haonan Li, Xudong Han, Hao Wang +7
We introduce Loki, an open-source tool designed to address the growing problem of misinformation. Loki adopts a human-centered approach, striking a balance between the quality of f…
OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs
Hasan Iqbal, Yuxia Wang, Minghan Wang +4
The increased use of large language models (LLMs) across a variety of real-world applications calls for automatic tools to check the factual accuracy of their outputs, as LLMs ofte…
Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs
Muhammad Arslan Manzoor, Yuxia Wang, Minghan Wang +1
Empathy plays a pivotal role in fostering prosocial behavior, often triggered by the sharing of personal experiences through narratives. However, modeling empathy using NLP approac…
Exploring the Potential of Multimodal LLM with Knowledge-Intensive Multimodal ASR
Minghan Wang, Yuxia Wang, Thuy-Trang Vu +2
Recent advancements in multimodal large language models (MLLMs) have made significant progress in integrating information across various modalities, yet real-world applications in…
OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs
Yuxia Wang, Minghan Wang, Hasan Iqbal +3
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs. Difficulties lie…
Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
Lizhi Lin, Honglin Mu, Zenan Zhai +9
Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed. In li…
Factuality of Large Language Models: A Survey
Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor +4
Large language models (LLMs), especially when instruction-tuned for chat, have become part of our daily lives, freeing people from the process of searching, extracting, and integra…
Rethinking STS and NLI in Large Language Models
Yuxia Wang, Minghan Wang, Preslav Nakov
Recent years have seen the rise of large language models (LLMs), where practitioners use task-specific prompts; this was shown to be effective for a variety of tasks. However, when…