256 citations · 323 across the 39 of their papers we have counts for
22 papers · 1 filter
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis
Qiushi Sun, Jingyang Gong, Yang Liu +14
Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is burgeoning, as models are increas…
HyperLogic: A Hard, Forward-Authored Chinese Logical Reasoning Benchmark with Execution-Derived Answers
Ming Zhang, Qiyuan Peng, Yinxi Wei +16
Existing logic benchmarks primarily measure models' ability to answer reasoning questions directly. Scalable benchmarks often generate text from formal structures, which makes answ…
CL-bench Life: Can Language Models Learn from Real-Life Context?
Shihan Dou, Yujiong Shen, Chenhao Huang +35
Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move be…
CL-bench: A Benchmark for Context Learning
Shihan Dou, Ming Zhang, Zhangyue Yin +24
Current language models (LMs) excel at reasoning over prompts using pre-trained knowledge. However, real-world tasks are far more complex and context-dependent: models must learn f…
OdysseyArena: Benchmarking Large Language Models For Long-Horizon, Active and Inductive Interactions
Hang Yan, Fangzhi Xu, Qiushi Sun +14
The advancement of Large Language Models (LLMs) has catalyzed the development of autonomous agents capable of navigating complex environments. However, existing evaluations primari…
Dynamic and Generalizable Process Reward Modeling
Zhangyue Yin, Qiushi Sun, Zhiyuan Zeng +3
Process Reward Models (PRMs) are crucial for guiding Large Language Models (LLMs) in complex scenarios by providing dense reward signals. However, existing PRMs primarily rely on h…
R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning
Yuan Li, Qi Luo, Xiaonan Li +7
Retrieval-Augmented Generation (RAG) integrates external knowledge with Large Language Models (LLMs) to enhance factual correctness and mitigate hallucination. However, dense retri…
Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning
Jingqi Tong, Jixin Tang, Hangcheng Li +21
Vision-language reinforcement learning (RL) has primarily focused on narrow domains (e.g. geometry or chart reasoning). This leaves broader training scenarios and resources underex…
Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework
Yuhong Sun, Zhangyue Yin, Xuanjing Huang +2
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. Math Word Problems (MWPs) serve as a crucial benchmark for evaluating LLMs' reasoning…
Unified Active Retrieval for Retrieval Augmented Generation
Qinyuan Cheng, Xiaonan Li, Shimin Li +7
In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and applying it to every instruction is sub-optimal. Therefore, determining whether to retrieve is crucial…
Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models
Zhangyue Yin, Qiushi Sun, Qipeng Guo +9
Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances…
Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem
Yuhong Sun, Zhangyue Yin, Qipeng Guo +3
Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous…
Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs
Yuhang Zhou, Yuchen Ni, Yunhui Gan +7
Large Language Models (LLMs) are increasingly adopted in financial analysis for interpreting complex market data and trends. However, their use is challenged by intrinsic biases (e…
Can AI Assistants Know What They Don't Know?
Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu +7
Recently, AI assistants based on large language models (LLMs) show surprising performance in many tasks, such as dialogue, solving math problems, writing code, and using tools. Alt…
Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication
Zhangyue Yin, Qiushi Sun, Cheng Chang +4
Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique. Despite this progress, their reasoning is oft…
LLatrieval: LLM-Verified Retrieval for Verifiable Generation
Xiaonan Li, Changtai Zhu, Linyang Li +3
Verifiable generation aims to let the large language model (LLM) generate text with supporting documents, which enables the user to flexibly verify the answer and makes the LLM's o…
-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL
Yuhang Zhou, Yu He, Siyu Tian +8
While current tasks of converting natural language to SQL (NL2SQL) using Foundation Models have shown impressive achievements, adapting these approaches for converting natural lang…
Evaluating Hallucinations in Chinese Large Language Models
Qinyuan Cheng, Tianxiang Sun, Wenwei Zhang +8
In this paper, we establish a benchmark named HalluQA (Chinese Hallucination Question-Answering) to measure the hallucination phenomenon in Chinese large language models. HalluQA c…
Secrets of RLHF in Large Language Models Part I: PPO
Rui Zheng, Shihan Dou, Songyang Gao +24
Large language models (LLMs) have formulated a blueprint for the advancement of artificial general intelligence. Its primary objective is to function as a human-centric (helpful, h…
Do Large Language Models Know What They Don't Know?
Zhangyue Yin, Qiushi Sun, Qipeng Guo +3
Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks. Current research focuses on enhancing their pe…
Rethinking Label Smoothing on Multi-hop Question Answering
Zhangyue Yin, Yuxin Wang, Xiannian Hu +6
Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiring multiple reasoning components, including document retrieval, supporting sentence predicti…
TURNER: The Uncertainty-based Retrieval Framework for Chinese NER
Zhichao Geng, Hang Yan, Zhangyue Yin +2
Chinese NER is a difficult undertaking due to the ambiguity of Chinese characters and the absence of word boundaries. Previous work on Chinese NER focus on lexicon-based methods to…