4 citations · 8 across the 31 of their papers we have counts for
11 papers · 1 filter
DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
Yiming Huang, Jianwen Luo, Yan Yu +8
We introduce DA-Code, a code generation benchmark specifically designed to assess LLMs on agent-based data science tasks. This benchmark features three core elements: First, the ta…
CITI: Enhancing Tool Utilizing Ability in Large Language Models without Sacrificing General Performance
Yupu Hao, Pengfei Cao, Zhuoran Jin +4
Tool learning enables the Large Language Models (LLMs) to interact with the external environment by invoking tools, enriching the accuracy and capability scope of LLMs. However, pr…
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…
Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models
Wangtao Sun, Chenxiang Zhang, XueYou Zhang +7
Although Large Language Models (LLMs) have demonstrated strong ability, they are further supposed to be controlled and guided by in real-world scenarios to be safe, accurate, and i…
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…
ItD: Large Language Models Can Teach Themselves Induction through Deduction
Wangtao Sun, Haotian Xu, Xuanqing Yu +4
Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited a…
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…
From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs
Wangtao Sun, Shizhu He, Jun Zhao +1
With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support. However, existing studies…
MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models
Tongxu Luo, Jiahe Lei, Fangyu Lei +4
Fine-tuning is often necessary to enhance the adaptability of Large Language Models (LLM) to downstream tasks. Nonetheless, the process of updating billions of parameters demands s…
ControlLM: Crafting Diverse Personalities for Language Models
Yixuan Weng, Shizhu He, Kang Liu +2
As language models continue to scale in size and capability, they display an array of emerging behaviors, both beneficial and concerning. This heightens the need to control model b…