4 citations · 11 across the 15 of their papers we have counts for
15 papers
SCE: Scalable Consistency Ensembles Make Blackbox Large Language Model Generation More Reliable
Jiaxin Zhang, Zhuohang Li, Wendi Cui +3
Large language models (LLMs) have demonstrated remarkable performance, yet their diverse strengths and weaknesses prevent any single LLM from achieving dominance across all tasks.…
Learning to Search Effective Example Sequences for In-Context Learning
Xiang Gao, Ankita Sinha, Kamalika Das
Large language models (LLMs) demonstrate impressive few-shot learning capabilities, but their performance varies widely based on the sequence of in-context examples. Key factors in…
Gradient-guided Attention Map Editing: Towards Efficient Contextual Hallucination Mitigation
Yu Wang, Kamalika Das, Xiang Gao +3
In tasks like summarization and open-book question answering (QA), Large Language Models (LLMs) often encounter "contextual hallucination", where they produce irrelevant or incorre…
A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm
Wendi Cui, Zhuohang Li, Hao Sun +5
Recent advances in Large Language Models have led to remarkable achievements across a variety of Natural Language Processing tasks, making prompt engineering increasingly central t…
Survival of the Safest: Towards Secure Prompt Optimization through Interleaved Multi-Objective Evolution
Ankita Sinha, Wendi Cui, Kamalika Das +1
Large language models (LLMs) have demonstrated remarkable capabilities; however, the optimization of their prompts has historically prioritized performance metrics at the expense o…
Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models
Jiaxin Zhang, Wendi Cui, Yiran Huang +2
Large language models (LLMs) are proficient in capturing factual knowledge across various domains. However, refining their capabilities on previously seen knowledge or integrating…
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation
Zhuohang Li, Jiaxin Zhang, Chao Yan +4
Language models (LMs) are known to suffer from hallucinations and misinformation. Retrieval augmented generation (RAG) that retrieves verifiable information from an external knowle…
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
Xiang Gao, Jiaxin Zhang, Lalla Mouatadid +1
In recent years, large language models (LLMs) have become increasingly prevalent, offering remarkable text generation capabilities. However, a pressing challenge is their tendency…
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods
Jiaxin Zhang, Kamalika Das, Sricharan Kumar
Uncertainty estimation is a crucial aspect of deploying dependable deep learning models in safety-critical systems. In this study, we introduce a novel and efficient method for det…
SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization
Wendi Cui, Zhuohang Li, Hao Sun +5
Designing optimal prompts for Large Language Models (LLMs) is a complicated and resource-intensive task, often requiring substantial human expertise and effort. Existing approaches…
DCR-Consistency: Divide-Conquer-Reasoning for Consistency Evaluation and Improvement of Large Language Models
Wendi Cui, Jiaxin Zhang, Zhuohang Li +4
Evaluating the quality and variability of text generated by Large Language Models (LLMs) poses a significant, yet unresolved research challenge. Traditional evaluation methods, suc…
Customizing Language Model Responses with Contrastive In-Context Learning
Xiang Gao, Kamalika Das
Large language models (LLMs) are becoming increasingly important for machine learning applications. However, it can be challenging to align LLMs with our intent, particularly when…
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models
Jiaxin Zhang, Joy Rimchala, Lalla Mouatadid +2
The performance of optical character recognition (OCR) heavily relies on document image quality, which is crucial for automatic document processing and document intelligence. Howev…
SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency
Jiaxin Zhang, Zhuohang Li, Kamalika Das +2
Hallucination detection is a critical step toward understanding the trustworthiness of modern language models (LMs). To achieve this goal, we re-examine existing detection approach…
Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision
Jiaxin Zhang, Zhuohang Li, Kamalika Das +1
Large language models (LLMs) have demonstrated remarkable capabilities in various tasks. However, their suitability for domain-specific tasks, is limited due to their immense scale…