activity
20232026
most citedChain of Agents: Large Language Models Collaborating on Long-Context Tasks

9 citations · 20 across the 17 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CL2024★ 2 cited

Verbosity Veracity: Demystify Verbosity Compensation Behavior of Large Language Models

Yusen Zhang, Sarkar Snigdha Sarathi Das, Rui Zhang

Although Large Language Models (LLMs) have demonstrated their strong capabilities in various tasks, recent work has revealed LLMs also exhibit undesirable behaviors, such as halluc…

cs.CL2024

GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers

Sarkar Snigdha Sarathi Das, Ryo Kamoi, Bo Pang +3

The effectiveness of large language models (LLMs) is closely tied to the design of prompts, making prompt optimization essential for enhancing their performance across a wide range…

cs.CL2024

Coverage-based Fairness in Multi-document Summarization

Haoyuan Li, Yusen Zhang, Rui Zhang +1

Fairness in multi-document summarization (MDS) measures whether a system can generate a summary fairly representing information from documents with different social attribute value…

cs.CL2024

VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information

Ryo Kamoi, Yusen Zhang, Sarkar Snigdha Sarathi Das +2

Large Vision Language Models (LVLMs) have achieved remarkable performance in various vision-language tasks. However, it is still unclear how accurately LVLMs can perceive visual in…

cs.IR2024

From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning

Ranran Haoran Zhang, Bensu Uçar, Soumik Dey +3

Open-vocabulary Extreme Multi-label Classification (OXMC) extends traditional XMC by allowing prediction beyond an extremely large, predefined label set (typically to $10^{1…

cs.CL2024★ 9 cited

Chain of Agents: Large Language Models Collaborating on Long-Context Tasks

Yusen Zhang, Ruoxi Sun, Yanfei Chen +3

Addressing the challenge of effectively processing long contexts has become a critical issue for Large Language Models (LLMs). Two common strategies have emerged: 1) reducing the i…