3 citations · 8 across the 7 of their papers we have counts for
7 papers
InfiniteICL: Breaking the Limit of Context Window Size via Long Short-term Memory Transformation
Bowen Cao, Deng Cai, Wai Lam
In-context learning (ICL) is critical for large language models (LLMs), but its effectiveness is constrained by finite context windows, particularly in ultra-long contexts. To over…
A Survey on the Honesty of Large Language Models
Siheng Li, Cheng Yang, Taiqiang Wu +12
Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to fai…
On the Worst Prompt Performance of Large Language Models
Bowen Cao, Deng Cai, Zhisong Zhang +2
The performance of large language models (LLMs) is acutely sensitive to the phrasing of prompts, which raises significant concerns about their reliability in real-world scenarios.…
Consecutive Batch Model Editing with HooK Layers
Shuaiyi Li, Yang Deng, Deng Cai +3
As the typical retraining paradigm is unacceptably time- and resource-consuming, researchers are turning to model editing to find an effective way that supports both consecutive an…
A Thorough Examination of Decoding Methods in the Era of LLMs
Chufan Shi, Haoran Yang, Deng Cai +4
Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers. Prior research on decoding methods, primarily focu…
Reasons to Reject? Aligning Language Models with Judgments
Weiwen Xu, Deng Cai, Zhisong Zhang +2
As humans, we consistently interact with our peers and receive feedback in the form of natural language. This language feedback allows us to maintain appropriate behavior, and rect…
StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving
Chang Gao, Haiyun Jiang, Deng Cai +2
Most existing prompting methods suffer from the issues of generalizability and consistency, as they often rely on instance-specific solutions that may not be applicable to other in…