6 citations · 18 across the 20 of their papers we have counts for
7 papers · 2 filters
Knowledge Boundary of Large Language Models: A Survey
Moxin Li, Yong Zhao, Wenxuan Zhang +5
Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, lead…
Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction
Xiaoyuan Li, Wenjie Wang, Moxin Li +3
The rapid advancement of Large Language Models (LLMs) in the realm of mathematical reasoning necessitates comprehensive evaluations to gauge progress and inspire future directions.…
Dual-Phase Accelerated Prompt Optimization
Muchen Yang, Moxin Li, Yongle Li +5
Gradient-free prompt optimization methods have made significant strides in enhancing the performance of closed-source Large Language Models (LLMs) across a wide range of tasks. How…
Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs
Yi Fang, Moxin Li, Wenjie Wang +2
Large Language Models (LLMs) excel in various natural language processing tasks but struggle with hallucination issues. Existing solutions have considered utilizing LLMs' inherent…
Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection
Moxin Li, Wenjie Wang, Fuli Feng +3
Self-detection for Large Language Models (LLMs) seeks to evaluate the trustworthiness of the LLM's output by leveraging its own capabilities, thereby alleviating the issue of outpu…
Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations
Yang Deng, Yong Zhao, Moxin Li +2
Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have…
TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data
Fengbin Zhu, Ziyang Liu, Fuli Feng +3
In this work, we address question answering (QA) over a hybrid of tabular and textual data that are very common content on the Web (e.g. SEC filings), where discrete reasoning capa…