Papers by Robert Lo
Dynamic Stashing Quantization for Efficient Transformer Training (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive performance on a range of Natural Language Processing (NLP) tasks. |
| Approach: | They propose a dynamic quantization strategy that reduces the amount of memory operations and reduces arithmetic cost by 20.95 on two translation tasks and three classification tasks. |
| Outcome: | The proposed model reduces the amount of arithmetic operations by 20.95 and the number of DRAM operations by 2.55 on two translation tasks and three classification tasks. |
VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks (2024.acl-long)
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Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Russ Salakhutdinov, Daniel Fried
| Challenge: | Existing benchmarks focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. |
| Approach: | They propose a benchmark to assess the performance of multimodal web agents . they use visual and textual inputs to process and interpret natural language instructions . |
| Outcome: | a new benchmark assesses the performance of multimodal agents on visually grounded tasks . the benchmark identifies limitations of text-only agents and offers insights towards building stronger agents for the web . |
Hierarchical Prompting Assists Large Language Model on Web Navigation (2023.findings-emnlp)
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| Challenge: | Large language models struggle on processing complicated observations in interactive decision making tasks. |
| Approach: | They propose a hierarchical prompting approach that constructs an action-aware observation and a Summarizer prompt. |
| Outcome: | The proposed method outperforms the current state-of-the-art prompting mechanism by 6.2% on task success rate. |