71 citations · 177 across the 44 of their papers we have counts for
44 papers
ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation
Xingshan Zeng, Lingzhi Wang, Weiwen Liu +5
Current test-time scaling (TTS) techniques enhance large language model (LLM) performance by allocating additional computation at inference time, yet they remain insufficient for a…
ReliableMath: Benchmark of Reliable Mathematical Reasoning on Large Language Models
Boyang Xue, Qi Zhu, Rui Wang +8
Although demonstrating remarkable performance on reasoning tasks, Large Language Models (LLMs) still tend to fabricate unreliable responses when confronted with problems that are u…
Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification
Chengwu Liu, Ye Yuan, Yichun Yin +7
Chain-of-Thought (CoT) prompting has become the de facto method to elicit reasoning capabilities from large language models (LLMs). However, to mitigate hallucinations in CoT that…
Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs' Reasoning
Zezhong Wang, Xingshan Zeng, Weiwen Liu +7
Mathematical reasoning through Chain-of-Thought (CoT) has emerged as a powerful capability of Large Language Models (LLMs), which can be further enhanced through Test-Time Scaling…
Instruction-Tuning Data Synthesis from Scratch via Web Reconstruction
Yuxin Jiang, Yufei Wang, Chuhan Wu +8
The improvement of LLMs' instruction-following capabilities depends critically on the availability of high-quality instruction-response pairs. While existing automatic data synthet…
DAST: Difficulty-Aware Self-Training on Large Language Models
Boyang Xue, Qi Zhu, Hongru Wang +8
Present Large Language Models (LLM) self-training methods always under-sample on challenging queries, leading to inadequate learning on difficult problems which limits LLMs' abilit…
DocPuzzle: A Process-Aware Benchmark for Evaluating Realistic Long-Context Reasoning Capabilities
Tianyi Zhuang, Chuqiao Kuang, Xiaoguang Li +4
We present DocPuzzle, a rigorously constructed benchmark for evaluating long-context reasoning capabilities in large language models (LLMs). This benchmark comprises 100 expert-lev…
Crowd Comparative Reasoning: Unlocking Comprehensive Evaluations for LLM-as-a-Judge
Qiyuan Zhang, Yufei Wang, Yuxin Jiang +8
LLM-as-a-Judge, which generates chain-of-thought (CoT) judgments, has become a widely adopted auto-evaluation method. However, its reliability is compromised by the CoT reasoning's…
Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving
Xin Xu, Yan Xu, Tianhao Chen +9
Existing approaches to mathematical reasoning with large language models (LLMs) rely on Chain-of-Thought (CoT) for generalizability or Tool-Integrated Reasoning (TIR) for precise c…
NILE: Internal Consistency Alignment in Large Language Models
Minda Hu, Qiyuan Zhang, Yufei Wang +7
As a crucial step to enhance LLMs alignment with human intentions, Instruction Fine-Tuning (IFT) has a high demand on dataset quality. However, existing IFT datasets often contain…
ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis
Zezhong Wang, Xingshan Zeng, Weiwen Liu +6
Supervised fine-tuning (SFT) is a common method to enhance the tool calling capabilities of Large Language Models (LLMs), with the training data often being synthesized. The curren…
RevisEval: Improving LLM-as-a-Judge via Response-Adapted References
Qiyuan Zhang, Yufei Wang, Tiezheng YU +9
With significant efforts in recent studies, LLM-as-a-Judge has become a cost-effective alternative to human evaluation for assessing text generation quality in a wide range of task…
Flat-LoRA: Low-Rank Adaptation over a Flat Loss Landscape
Tao Li, Zhengbao He, Yujun Li +3
Fine-tuning large-scale pre-trained models is prohibitively expensive in terms of computation and memory costs. Low-Rank Adaptation (LoRA), a popular Parameter-Efficient Fine-Tunin…
Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization
Yuxin Jiang, Bo Huang, Yufei Wang +7
Direct preference optimization (DPO), a widely adopted offline preference optimization algorithm, aims to align large language models (LLMs) with human-desired behaviors using pair…
Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation
Yiwei Li, Fei Mi, Yitong Li +4
Stochastic sampling strategies such as top-k and top-p have been widely used in dialogue generation task. However, as an open-domain chatting system, there will be two different co…
Chain-of-Probe: Examining the Necessity and Accuracy of CoT Step-by-Step
Zezhong Wang, Xingshan Zeng, Weiwen Liu +7
Current research found the issue of Early Answering in large language models (LLMs), where the models already have an answer before generating the Chain-of-Thought (CoT). This phen…
UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval
Hongru Wang, Boyang Xue, Baohang Zhou +5
Conversational retrieval refers to an information retrieval system that operates in an iterative and interactive manner, requiring the retrieval of various external resources, such…
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions
Xuming Hu, Xiaochuan Li, Junzhe Chen +8
Generative search engines have the potential to transform how people seek information online, but generated responses from existing large language models (LLMs)-backed generative s…
YODA: Teacher-Student Progressive Learning for Language Models
Jianqiao Lu, Wanjun Zhong, Yufei Wang +10
Although large language models (LLMs) have demonstrated adeptness in a range of tasks, they still lag behind human learning efficiency. This disparity is often linked to the inhere…
PROXYQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models
Haochen Tan, Zhijiang Guo, Zhan Shi +8
Large Language Models (LLMs) have succeeded remarkably in understanding long-form contents. However, exploring their capability for generating long-form contents, such as reports a…
Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment
Boyang Xue, Weichao Wang, Hongru Wang +7
Pretrained language models (PLMs) based knowledge-grounded dialogue systems are prone to generate responses that are factually inconsistent with the provided knowledge source. In s…
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue
Hongru Wang, Minda Hu, Yang Deng +7
Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue sys…
DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering
Pei Ke, Fei Huang, Fei Mi +4
Existing evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability. Specifically, most of the well-performed…
Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video
Zenan Xu, Xiaojun Meng, Yasheng Wang +4
Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our…