Zhengliang Shi

Orcid: 0000-0002-9658-4906

According to our database¹, Zhengliang Shi authored at least 22 papers between 2023 and 2025.

Collaborative distances:

Dijkstra number² of five.
Erdős number³ of four.

Timeline

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On csauthors.net:

Bibliography

2025

Evolution without Large Models: Training Language Model with Task Principles.

[BibT_eX]

[DOI]

CoRR, July, 2025

Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization.

[BibT_eX]

[DOI]

CoRR, June, 2025

Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers.

[BibT_eX]

[DOI]

CoRR, May, 2025

TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation.

[BibT_eX]

[DOI]

CoRR, May, 2025

Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models.

[BibT_eX]

[DOI]

CoRR, May, 2025

Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents.

[BibT_eX]

[DOI]

Proceedings of the ACM on Web Conference 2025, 2025

Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation.

[BibT_eX]

[DOI]

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics, 2025

2024

MAIR: A Massive Benchmark for Evaluating Instructed Retrieval.

[BibT_eX]

[DOI]

CoRR, 2024

What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks.

[BibT_eX]

[DOI]

CoRR, 2024

Chain of Tools: Large Language Model is an Automatic Multi-tool Learner.

[BibT_eX]

[DOI]

CoRR, 2024

360{\deg}REA: Towards A Reusable Experience Accumulation with 360{\deg} Assessment for Multi-Agent System.

[BibT_eX]

[DOI]

CoRR, 2024

Learning to Use Tools via Cooperative and Interactive Agents.

[BibT_eX]

[DOI]

CoRR, 2024

Learning to Use Tools via Cooperative and Interactive Agents.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

MAIR: A Massive Benchmark for Evaluating Instructed Retrieval.

[BibT_eX]

[DOI]

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering.

[BibT_eX]

[DOI]

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

360°REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics, 2024

Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum.

[BibT_eX]

[DOI]

CoRR, 2023

Towards a Unified Framework for Reference Retrieval and Related Work Generation.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue.

[BibT_eX]

[DOI]

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

Contrastive Learning Reduces Hallucination in Conversations.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Zhengliang Shi

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