activity
20232026
collaborators

8 papers

cs.LG2026

Value-Aware Stochastic KV Cache Eviction for Reasoning Models

Ting-Yun Chang, Harvey Yiyun Fu, Deqing Fu +3

Reasoning models improve accuracy through extended chains of thought, but their long outputs create a memory and compute bottleneck. KV cache eviction methods reduce this cost by e…

cs.RO2025

PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models

Wang Bill Zhu, Miaosen Chai, Ishika Singh +2

We propose PSALM-V, the first autonomous neuro-symbolic learning system able to induce symbolic action semantics (i.e., pre- and post-conditions) in visual environments through int…

cs.LG2025

Why Do Some Inputs Break Low-Bit LLM Quantization?

Ting-Yun Chang, Muru Zhang, Jesse Thomason +1

Low-bit weight-only quantization significantly reduces the memory footprint of large language models (LLMs), but disproportionately affects certain examples. We analyze diverse 3-4…

cs.CL2024

When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models

Ting-Yun Chang, Jesse Thomason, Robin Jia

This paper studies in-context learning by decomposing the output of large language models into the individual contributions of attention heads and MLPs (components). We observe cur…

cs.AI2024

Language Models can Infer Action Semantics for Symbolic Planners from Environment Feedback

Wang Zhu, Ishika Singh, Robin Jia +1

Symbolic planners can discover a sequence of actions from initial to goal states given expert-defined, domain-specific logical action semantics. Large Language Models (LLMs) can di…

cs.CL2023

Does VLN Pretraining Work with Nonsensical or Irrelevant Instructions?

Wang Zhu, Ishika Singh, Yuan Huang +2

Data augmentation via back-translation is common when pretraining Vision-and-Language Navigation (VLN) models, even though the generated instructions are noisy. But: does that nois…

cs.CV2023

Efficient End-to-End Visual Document Understanding with Rationale Distillation

Wang Zhu, Alekh Agarwal, Mandar Joshi +3

Understanding visually situated language requires interpreting complex layouts of textual and visual elements. Pre-processing tools, such as optical character recognition (OCR), ca…

cs.CL2023

Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks

Ting-Yun Chang, Jesse Thomason, Robin Jia

The concept of localization in LLMs is often mentioned in prior work; however, methods for localization have never been systematically and directly evaluated. We propose two comple…