1 citations · 1 across the 2 of their papers we have counts for
9 papers
Finding RELIEF: Shaping Reasoning Behavior without Reasoning Supervision via Belief Engineering
Chak Tou Leong, Dingwei Chen, Heming Xia +4
Large reasoning models (LRMs) have achieved remarkable success in complex problem-solving, yet they often suffer from computational redundancy or reasoning unfaithfulness. Current…
LLM-REVal: Can We Trust LLM Reviewers Yet?
Rui Li, Jia-Chen Gu, Po-Nien Kung +5
The rapid advancement of large language models (LLMs) has inspired researchers to integrate them extensively into the academic workflow, potentially reshaping how research is pract…
From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs
Haonan Bian, Yutao Qi, Rui Yang +4
Large Language Models (LLMs), despite their success in question answering, exhibit limitations in complex multi-hop question answering (MQA) tasks that necessitate non-linear, stru…
KNN-SSD: Enabling Dynamic Self-Speculative Decoding via Nearest Neighbor Layer Set Optimization
Mingbo Song, Heming Xia, Jun Zhang +4
Speculative Decoding (SD) has emerged as a widely used paradigm to accelerate the inference of large language models (LLMs) without compromising generation quality. It works by eff…
Tutorial Proposal: Speculative Decoding for Efficient LLM Inference
Heming Xia, Cunxiao Du, Yongqi Li +2
This tutorial presents a comprehensive introduction to Speculative Decoding (SD), an advanced technique for LLM inference acceleration that has garnered significant research intere…
PEToolLLM: Towards Personalized Tool Learning in Large Language Models
Qiancheng Xu, Yongqi Li, Heming Xia +3
Tool learning has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools. Existing tool learning studies primarily focus on th…