2 citations · 6 across the 16 of their papers we have counts for
5 papers · 1 filter
When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors
Yuqing Yang, Qi Zhu, Zhen Han +5
While large language models (LLMs) perform well on table tasks, they still make data referencing errors (DREs), i.e., incorrectly citing or omitting table values, despite understan…
CrEst: Credibility Estimation for Contexts in LLMs via Weak Supervision
Dyah Adila, Shuai Zhang, Boran Han +2
The integration of contextual information has significantly enhanced the performance of large language models (LLMs) on knowledge-intensive tasks. However, existing methods often o…
Effectively Steer LLM To Follow Preference via Building Confident Directions
Bingqing Song, Boran Han, Shuai Zhang +5
Having an LLM that aligns with human preferences is essential for accommodating individual needs, such as maintaining writing style or generating specific topics of interest. The m…
CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving
Pei Chen, Boran Han, Shuai Zhang
Large Language Models (LLMs) have shown great ability in solving traditional natural language tasks and elementary reasoning tasks with appropriate prompting techniques. However, t…
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design
Wenqi Jiang, Shuai Zhang, Boran Han +3
Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases. However, retrievals from large da…