Papers by Jinglong Gao
ExpeTrans: LLMs Are Experiential Transfer Learners (2025.acl-long)
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| Challenge: | Recent studies provide large language models with textual task-solving experiences via prompts to improve their performance. |
| Approach: | They propose to use prompts to provide LLMs with textual task-solving experiences during their inference stage. |
| Outcome: | The proposed framework improves the performance of large language models on 13 datasets. |
Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have achieved significant performance in various natural language reasoning tasks, but struggle with performing first-order logic reasoning over formal logical theories expressed in natural language. |
| Approach: | They propose a framework which introduces the paradigm of resolution refutation to solve first-order logic reasoning problems by extending reasoning rules and employing the principle of proof by contradiction. |
| Outcome: | The proposed framework outperforms existing models while maintaining performance in simple scenarios. |
Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration (2026.acl-long)
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Yang Zhao, Yangou Ouyang, Xiao Ding, Hepeng Wang, Bibo Cai, Kai Xiong, Jinglong Gao, Zhouhao Sun, Li Du, Bing Qin, Ting Liu
| Challenge: | Existing data arbitration strategies for large language model training rely on surface-level heuristics that fail to diagnose intrinsic learning needs. |
| Approach: | They propose a framework that arbitrates data based on its degree of cognitive conflict with the model's existing knowledge. |
| Outcome: | Extensive experiments on WebShop and ALFWorld show that PRISM outperforms state-of-the-art hybrid methods while reducing computational costs by up to 3.22 . |
Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection (2025.acl-long)
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Yang Zhao, Li Du, Xiao Ding, Yangou Ouyang, Hepeng Wang, Kai Xiong, Jinglong Gao, Zhouhao Sun, Dongliang Xu, Qing Yang, Dongchen Li, Bing Qin, Ting Liu
| Challenge: | Existing methods for selecting training data from general datasets fail to account for the joint distribution of instructions, resulting in inefficient learning and suboptimal knowledge transfer. |
| Approach: | They propose a method that constructs a mixed gradient-based instruction graph to capture the joint distribution and interdependencies among instructions. |
| Outcome: | The proposed method outperforms existing methods on domain adaptation tasks and in complex, data-scarce scenarios. |
MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization (2026.acl-long)
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Yang Zhao, Hepeng Wang, Xiao Ding, Yangou Ouyang, Bibo Cai, Kai Xiong, Jinglong Gao, Zhouhao Sun, Li Du, Bing Qin, Ting Liu
| Challenge: | Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), but its efficacy is confined to domains with verifiable ground truths. |
| Approach: | They propose a meta-cognitive orchestration layer that treats reward scalarization as a dynamic latent policy, leveraging the model’s terminal hidden states as 'a semantic bottleneck' . Across seven benchmarks, MAESTRO consistently outperforms single-reward and static multi-objective baselines while preserving the efficiency advantages of GRPO. |
| Outcome: | The proposed model outperforms single-reward and static multi-objective baselines while preserving efficiency advantages. |
Self-Evolving GPT: A Lifelong Autonomous Experiential Learner (2024.acl-long)
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| Challenge: | Existing approaches to provide LLMs with textual task-solving experience rely on manual efforts to acquire and apply such experience for each task. |
| Approach: | They propose a lifelong autonomous experiential learning framework based on LLMs that learns and accumulates experience through experience transfer and induction. |
| Outcome: | The proposed framework performs reliably in each intermediate step and improves GPT-3.5 and GPT-4 on widely used NLP datasets. |
Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation (2023.findings-emnlp)
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| Challenge: | Existing methods to evaluate ChatGPT's causal reasoning abilities are based on pre-trained language models, but they rely on supervised training. |
| Approach: | They conduct the first comprehensive evaluation of ChatGPT’s causal reasoning capabilities using four state-of-the-art (STA) simulations. |
| Outcome: | The proposed model is not a good causal reasoner, but a great causal interpreter. |
Com2 : A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models (2025.acl-long)
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Kai Xiong, Xiao Ding, Yixin Cao, Yuxiong Yan, Li Du, Yufei Zhang, Jinglong Gao, Jiaqian Liu, Bing Qin, Ting Liu
| Challenge: | Existing works focus on complex tasks like math and code, while complex commonsense reasoning remains underexplored due to its uncertainty and lack of structure. |
| Approach: | They propose to build a benchmark for large language models based on complex commonsense reasoning based upon causal event graphs and causal theory. |
| Outcome: | The proposed benchmark combines a complex commonsense reasoning benchmark with a detective story to achieve a more challenging subset. |