1 citations · 5 across the 11 of their papers we have counts for
11 papers
Precise measurement of the form factors in and observation of
BESIII Collaboration, M. Ablikim, M. N. Achasov +680
We report a study of the semileptonic decays (where ) based on a sample of of annihilation…
ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data
Yu Zhang, Ruijie Yu, Jidong Tian +5
With the increasing interest in robotic synthesis in the context of organic chemistry, the automated extraction of chemical procedures from literature is critical. However, this ta…
Large Language Models to Accelerate Organic Chemistry Synthesis
Yu Zhang, Yang Han, Shuai Chen +10
Chemical synthesis, as a foundational methodology in the creation of transformative molecules, exerts substantial influence across diverse sectors from life sciences to materials a…
Hypothesis Testing Prompting Improves Deductive Reasoning in Large Language Models
Yitian Li, Jidong Tian, Hao He +1
Combining different forms of prompts with pre-trained large language models has yielded remarkable results on reasoning tasks (e.g. Chain-of-Thought prompting). However, along with…
Logical Negation Augmenting and Debiasing for Prompt-based Methods
Yitian Li, Jidong Tian, Hao He +1
Prompt-based methods have gained increasing attention on NLP and shown validity on many downstream tasks. Many works have focused on mining these methods' potential for knowledge e…
Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models
Haoran Liao, Jidong Tian, Shaohua Hu +2
Large language models (LLMs) still grapple with complex tasks like mathematical reasoning. Despite significant efforts invested in improving prefix prompts or reasoning process, th…
Comparable Demonstrations are Important in In-Context Learning: A Novel Perspective on Demonstration Selection
Caoyun Fan, Jidong Tian, Yitian Li +2
In-Context Learning (ICL) is an important paradigm for adapting Large Language Models (LLMs) to downstream tasks through a few demonstrations. Despite the great success of ICL, the…
Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent
Haoran Liao, Qinyi Du, Shaohua Hu +4
Large language models (LLMs) face challenges in solving complex mathematical problems that require comprehensive capacities to parse the statements, associate domain knowledge, per…
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding
Caoyun Fan, Jidong Tian, Yitian Li +3
Chain-of-Thought (CoT) is a technique that guides Large Language Models (LLMs) to decompose complex tasks into multi-step reasoning through intermediate steps in natural language f…
Unlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalization
Caoyun Fan, Wenqing Chen, Jidong Tian +3
Counterfactually-Augmented Data (CAD) -- minimal editing of sentences to flip the corresponding labels -- has the potential to improve the Out-Of-Distribution (OOD) generalization…
Dependent Multi-Task Learning with Causal Intervention for Image Captioning
Wenqing Chen, Jidong Tian, Caoyun Fan +2
Recent work for image captioning mainly followed an extract-then-generate paradigm, pre-extracting a sequence of object-based features and then formulating image captioning as a si…