1 citations · 1 across the 6 of their papers we have counts for
8 papers
Guardrail-Agnostic Societal Bias Evaluation in Large Vision-Language Models
Yusuke Hirota, Michael Ross Boone, Arun George Zachariah +4
We propose a societal bias evaluation method for large vision-language models (LVLMs) in the era of strong safety guardrails. Existing benchmarks rely on prompts that ask models to…
How LLMs Fail and Generalize in RTL Coding for Hardware Design?
Guan-Ting Liu, Chao-Han Huck Yang, Chenhui Deng +3
Translating sequential programming priors into the parallel temporal logic of hardware design remains a crucial bottleneck for large language models(LLM). To investigate this, we i…
Summarize Before You Speak with ARACH: A Training-Free Inference-Time Plug-In for Enhancing LLMs via Global Attention Reallocation
Jingtao Wang, Yucong Wang, Jun Ding +2
Large language models (LLMs) achieve remarkable performance, yet further gains often require costly training. This has motivated growing interest in post-training techniques-especi…
Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluation
Yusuke Hirota, Ryo Hachiuma, Boyi Li +9
Gender bias in vision-language foundation models (VLMs) raises concerns about their safe deployment and is typically evaluated using benchmarks with gender annotations on real-worl…
LOTUS: A Leaderboard for Detailed Image Captioning from Quality to Societal Bias and User Preferences
Yusuke Hirota, Boyi Li, Ryo Hachiuma +7
Large Vision-Language Models (LVLMs) have transformed image captioning, shifting from concise captions to detailed descriptions. We introduce LOTUS, a leaderboard for evaluating de…
DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment
Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +25
We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following. Recent LALMs augment Large Langu…
UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation
Alexander H. Liu, Sang-gil Lee, Chao-Han Huck Yang +5
Pre-training and representation learning have been playing an increasingly important role in modern speech processing. Nevertheless, different applications have been relying on dif…
Test-Time Alignment for Large Language Models via Textual Model Predictive Control
Kuang-Da Wang, Teng-Ruei Chen, Yu Heng Hung +7
Aligning Large Language Models (LLMs) with human preferences through finetuning is resource-intensive, motivating lightweight alternatives at test time. We address test-time alignm…