1 citations · 3 across the 11 of their papers we have counts for
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PoLi-RL: A Point-to-List Reinforcement Learning Framework for Conditional Semantic Textual Similarity
Zixin Song, Bowen Zhang, Qian-Wen Zhang +3
Conditional Semantic Textual Similarity (C-STS) measures the semantic proximity between text segments under a specific condition, thereby overcoming the ambiguity inherent in tradi…
CoDiEmb: A Collaborative yet Distinct Framework for Unified Representation Learning in Information Retrieval and Semantic Textual Similarity
Bowen Zhang, Zixin Song, Chunquan Chen +3
Learning unified text embeddings that excel across diverse downstream tasks is a central goal in representation learning, yet negative transfer remains a persistent obstacle. This…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generation
Yilin Xiao, Junnan Dong, Chuang Zhou +5
Graph Retrieval Augmented Generation (GraphRAG) has garnered increasing recognition for its potential to enhance large language models (LLMs) by structurally organizing domain-spec…
Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard
Qian-Wen Zhang, Biao Liu, Fang Li +5
Despite the success of Large Language Models (LLMs) in long-context tasks, their reliability in recognizing unanswerable questions remains a critical challenge. In information-seek…
Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts
Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao +7
Evaluating the ability of large language models (LLMs) to process lengthy contexts is critical, especially for retrieving query-relevant information embedded within them. We introd…
SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models
Shujuan Zhao, Lingfeng Qiao, Kangyang Luo +3
Large language models (LLMs) have become powerful tools for advancing natural language processing applications in the financial industry. However, existing financial LLMs often fac…