5 citations · 13 across the 13 of their papers we have counts for
12 papers · 1 filter
Neural-Symbolic Collaborative Distillation: Advancing Small Language Models for Complex Reasoning Tasks
Huanxuan Liao, Shizhu He, Yao Xu +3
In this paper, we propose ural-mbolic ollaborative istillation (), a novel knowledge distillation method for lear…
: Internalizing Symbolic Knowledge for Distilling Better CoT Capabilities into Small Language Models
Huanxuan Liao, Shizhu He, Yupu Hao +4
Small Language Models (SLMs) are attracting attention due to the high computational demands and privacy concerns of Large Language Models (LLMs). Some studies fine-tune SLMs using…
Does Knowledge Localization Hold True? Surprising Differences Between Entity and Relation Perspectives in Language Models
Yifan Wei, Xiaoyan Yu, Yixuan Weng +4
Large language models encapsulate knowledge and have demonstrated superior performance on various natural language processing tasks. Recent studies have localized this knowledge to…
WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models
Kangyun Ning, Yisong Su, Xueqiang Lv +4
Although Large Language Models (LLMs) excel in NLP tasks, they still need external tools to extend their ability. Current research on tool learning with LLMs often assumes mandator…
From Instance Training to Instruction Learning: Task Adapters Generation from Instructions
Huanxuan Liao, Shizhu He, Yao Xu +5
Large language models (LLMs) have acquired the ability to solve general tasks by utilizing instruction finetuning (IFT). However, IFT still relies heavily on instance training of e…
Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering
Huanxuan Liao, Shizhu He, Yao Xu +4
Retrieval-Augmented-Generation and Generation-Augmented-Generation have been proposed to enhance the knowledge required for question answering with Large Language Models (LLMs) by…
Generative Calibration for In-context Learning
Zhongtao Jiang, Yuanzhe Zhang, Cao Liu +2
As one of the most exciting features of large language models (LLMs), in-context learning is a mixed blessing. While it allows users to fast-prototype a task solver with only a few…
MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models
Yifan Wei, Yisong Su, Huanhuan Ma +5
Large language models (LLMs) have shown nearly saturated performance on many natural language processing (NLP) tasks. As a result, it is natural for people to believe that LLMs hav…
Interpreting Sentiment Composition with Latent Semantic Tree
Zhongtao Jiang, Yuanzhe Zhang, Cao Liu +3
As the key to sentiment analysis, sentiment composition considers the classification of a constituent via classifications of its contained sub-constituents and rules operated on th…
Unsupervised Text Style Transfer with Deep Generative Models
Zhongtao Jiang, Yuanzhe Zhang, Yiming Ju +1
We present a general framework for unsupervised text style transfer with deep generative models. The framework models each sentence-label pair in the non-parallel corpus as partial…
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question
Yifan Wei, Fangyu Lei, Yuanzhe Zhang +2
Hybrid question answering (HybridQA) over the financial report contains both textual and tabular data, and requires the model to select the appropriate evidence for the numerical r…
Generating Hierarchical Explanations on Text Classification Without Connecting Rules
Yiming Ju, Yuanzhe Zhang, Kang Liu +1
The opaqueness of deep NLP models has motivated the development of methods for interpreting how deep models predict. Recently, work has introduced hierarchical attribution, which p…