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Jingcheng Deng

7 accepted papers

2026

RLKD: Distilling LLMs’ Reasoning via Reinforcement Learning

AAAI 2026technical

Distilling reasoning paths from teacher to student models via supervised fine-tuning (SFT) provides a shortcut for improving the reasoning ability of the smaller Large Language Models (LLMs). However, the reasoning paths generated by teacher models often reflect only surface-level traces of their un

Cited by 0SourcePDFScholar
2025

Everything is Editable: Extend Knowledge Editing to Unstructured Data in Large Language Models

ICLR 2025poster

Recent knowledge editing methods have primarily focused on modifying structured knowledge in large language models. However, this task setting overlooks the fact that a significant portion of real-world knowledge is stored in an unstructured format, characterized by long-form content, noise, and a c…

2025

FlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models

EMNLP 2025

Unmanned Aerial Vehicle (UAV) Vision-and-Language Navigation (VLN) is vital for applications such as disaster response, logistics delivery, and urban inspection. However, existing methods often struggle with insufficient multimodal fusion, weak generalization, and poor interpretability. To address t

2025

Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment

EMNLP 2025

A new trend uses LLMs as dense text encoders via contrastive learning. However, since LLM embeddings predict the probability distribution of the next token, they are inherently generative and distributive, conflicting with contrastive learning, which requires embeddings to capture full-text semantic

2025

MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models

COLING 2025main

The extensive utilization of large language models (LLMs) underscores the crucial necessity for precise and contemporary knowledge embedded within their intrinsic parameters. Existing research on knowledge editing primarily concentrates on monolingual scenarios, neglecting the complexities presented…

2023

RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling

EMNLP 2023long findings

Retrieval-augmented language models show promise in addressing issues like outdated information and hallucinations in language models (LMs). However, current research faces two main problems: 1) determining what information to retrieve, and 2) effectively combining retrieved information during gener…

Cited by 0SourcecodeScholar
2022

IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection

EMNLP 2022main

The task of response selection in multi-turn dialogue is to find the best option from all candidates. In order to improve the reasoning ability of the model, previous studies pay more attention to using explicit algorithms to model the dependencies between utterances, which are deterministic, limite…