10 citations · 26 across the 13 of their papers we have counts for
13 papers
Neurosymbolic LoRA: Why and When to Tune Weights vs. Rewrite Prompts
Kevin Wang, Neel P. Bhatt, Cong Liu +7
Large language models (LLMs) can be adapted either through numerical updates that alter model parameters or symbolic manipulations that work on discrete prompts or logical constrai…
Spark-Prover-X1: Formal Theorem Proving Through Diverse Data Training
Xinyuan Zhou, Yi Lei, Xiaoyu Zhou +7
Large Language Models (LLMs) have shown significant promise in automated theorem proving, yet progress is often constrained by the scarcity of diverse and high-quality formal langu…
TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection
Xixiang He, Hao Yu, Qiyao Sun +4
Instruction Fine-Tuning (IFT) is crucial for aligning large language models (LLMs) with human preferences, and selecting a small yet representative subset from massive data signifi…
ChatMusician: Understanding and Generating Music Intrinsically with LLM
Ruibin Yuan, Hanfeng Lin, Yi Wang +32
While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative langua…
Generative Input: Towards Next-Generation Input Methods Paradigm
Keyu Ding, Yongcan Wang, Zihang Xu +4
Since the release of ChatGPT, generative models have achieved tremendous success and become the de facto approach for various NLP tasks. However, its application in the field of in…
Evaluating the Reversal Curse in Model Editing
Hao-Xiang Xu, Jun-Yu Ma, Zhen-Hua Ling +3
Large language models (LLMs) are prone to hallucinate unintended text due to false or outdated knowledge. Since retraining LLMs is resource intensive, there has been a growing inte…
JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving
Wayne Xin Zhao, Kun Zhou, Beichen Zhang +8
Although pre-trained language models~(PLMs) have recently advanced the research progress in mathematical reasoning, they are not specially designed as a capable multi-task solver,…
SHINE: Syntax-augmented Hierarchical Interactive Encoder for Zero-shot Cross-lingual Information Extraction
Jun-Yu Ma, Jia-Chen Gu, Zhen-Hua Ling +3
Zero-shot cross-lingual information extraction(IE) aims at constructing an IE model for some low-resource target languages, given annotations exclusively in some rich-resource lang…
MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation
Jia-Chen Gu, Chao-Hong Tan, Caiyuan Chu +4
Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods r…
GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding
Jia-Chen Gu, Zhen-Hua Ling, Quan Liu +2
Addressing the issues of who saying what to whom in multi-party conversations (MPCs) has recently attracted a lot of research attention. However, existing methods on MPC understand…
WIDER & CLOSER: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition
Jun-Yu Ma, Beiduo Chen, Jia-Chen Gu +5
Zero-shot cross-lingual named entity recognition (NER) aims at transferring knowledge from annotated and rich-resource data in source languages to unlabeled and lean-resource data…
JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding
Wayne Xin Zhao, Kun Zhou, Zheng Gong +7
This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model~(PLM) for effectively understanding and…
InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities
Baoxin Wang, Qingye Meng, Ziyue Wang +6
Knowledge graph embedding (KGE) models learn the representation of entities and relations in knowledge graphs. Distance-based methods show promising performance on link prediction…