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20232026
most citedMAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL

8 citations · 10 across the 22 of their papers we have counts for

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Showing 2025 · cs.CLShow all

6 papers · 2 filters

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025★ 1 cited

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following

Junru Lu, Jiazheng Li, Guodong Shen +5

Role-playing is important for Large Language Models (LLMs) to follow diverse instructions while maintaining role identity and the role's pre-defined ability limits. Existing role-p…