Papers by Jianwei Yin
Probing the Geometry of Truth: Consistency and Generalization of Truth Directions in LLMs Across Logical Transformations and Question Answering Tasks (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are trained on vast corpora that contain substantial knowledge but their outputs often contain confidently stated inaccuracies. |
| Approach: | They propose to encode truthfulness as a distinct linear feature, termed the "truth direction", which can classify truthfulness reliably. |
| Outcome: | The proposed model can generalize to logical transformations, question-answering tasks, in-context learning, and external knowledge sources. |
The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning Capabilities without External Scaffolding? (2025.findings-acl)
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| Challenge: | Existing approaches to self-improvement rely on external supervision signals in the form of seed data and/or assistance from third-party models. |
| Approach: | They propose a framework for generating high-quality synthetic question-answer data in a fully autonomous manner. |
| Outcome: | The proposed framework generates high-quality synthetic question-answer data in a fully autonomous manner. |
GFT: From Imitation to Reward Fine-Tuning with Unbiased Group Advantages and Dynamic Coefficient Rectification (2026.findings-acl)
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| Challenge: | Existing studies have demonstrated that supervised fine-tuning and reinforcement learning are effective in integrating knowledge injection with robust generalization. |
| Approach: | They propose a unified post-training framework that addresses intrinsic limitations of supervised fine-tuning and reinforcement learning. |
| Outcome: | The proposed framework surpasses SFT-based methods and yields policies that integrate more smoothly with subsequent RL training. |
RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated excellent performance in numerous tasks but the parameterized knowledge stored within LLMs may be incomplete and hard to incorporate up-to-date knowledge. |
| Approach: | They propose a framework that iteratively decomposes tasks and processes them in three submodules to enhance the model’s problem-solving capabilities. |
| Outcome: | The proposed method outperforms existing benchmarks on GPT3.5, Llama2 and other large language models significantly enhancing factual reasoning capabilities and reducing hallucinations. |
Explainable Quantum Program Repair with Verifiable Proof Traces (2026.findings-acl)
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| Challenge: | Existing approaches to program repair provide only post-hoc, non-verifiable explanations that are not executable or verifiably. |
| Approach: | They propose a framework that couples repair generation with machine-checkable executable explanations for quantum programs where correctness hinges on subtle semantic properties such as circuit equivalence and fidelity preservation. |
| Outcome: | Experiments on QASMBench with mutation-generated quantum program bugs show that the proposed framework improves both semantic precision and explanation faithfulness over baselines that rely on unconstrained or purely natural-language explanations. |
ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Exploration (2025.emnlp-main)
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| Challenge: | Multimodal Large Language Models (MLLMs) have shown impressive capabilities in vision-language understanding but their visual input remains fixed throughout the reasoning process. |
| Approach: | They propose a model-agnostic tree search algorithm tailored for vision-level reasoning that allows MLLMs to explore textual tokens while visual input remains fixed throughout reasoning process. |
| Outcome: | The proposed algorithm outperforms strong large models such as GPT-4o on high-resolution benchmarks and improves performance on a series of elaborate high-level benchmarks. |
RIPRAG: Hack a Black-box Retrieval-Augmented Generation Question-Answering System with Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing methods to generate RAG documents require knowledge of the target RAG system’s internal composition and implementation details, whereas black-box methods are unable to utilize interactive information. |
| Approach: | They propose a RIPRAG attack framework that treats the target RAG system as a black box and leverages a Reinforcement Learning from Black-box Feedback (RLBF) method to optimize the generation model for poisoned documents. |
| Outcome: | The proposed method achieves an attack success rate (ASR) improvement of up to 0.72 compared to baseline methods. |
CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing (2025.emnlp-main)
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| Challenge: | Open-source code language models (code LMs) are a growing threat for intellectual property protection. |
| Approach: | They propose a black-box code LM watermarking framework that uses rule-based watermarks and utility-preserving injection method for user-level model tracing. |
| Outcome: | The proposed framework shows that it performs well across multiple state-of-the-art code LMs and is harmless compared to existing baselines. |
SAME: Signer-Aware Mixture-of-Experts for Test-Time Adaptation in Sign Language Translation (2026.acl-long)
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| Challenge: | Existing methods for supervised fine-tuning are limited due to labeled data . existing methods require long adaptation times and batch statistics are unavailable in streaming settings . |
| Approach: | They propose a plug-and-play, signer-aware Mixture-of-Experts (MoE) TTA architecture for SLT . they use a combination of lightweight MoE modules and unsupervised regularizers to decouple domain shift . |
| Outcome: | The proposed test-time adaptation outperforms existing TTA methods in sign language translation . the proposed architecture can be used in real-world deployments without labeling . |
An Explainable Toolbox for Evaluating Pre-trained Vision-Language Models (2022.emnlp-demos)
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| Challenge: | Existing studies evaluate VLP models by comparing the fine-tuned downstream task performance with the average downstream task accuracy. |
| Approach: | They propose a toolbox for evaluating Vision-Language Pretraining (VLP) models. |
| Outcome: | The proposed toolbox provides the preliminary datasets that deepen the image-texting ability of a VLP model. |
ToolGate: Contract-Grounded and Verified Tool Execution for LLMs (2026.findings-acl)
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Yanming Liu, Xinyue Peng, Jiannan Cao, Xinyi Wang, Songhang Deng, Jintao Chen, Jianwei Yin, Xuhong Zhang
| Challenge: | Existing frameworks for tool-augmented LLMs rely heavily on natural language reasoning to determine when tools can be invoked and whether their results should be trusted. |
| Approach: | They propose a forward execution framework that provides logical safety guarantees and verifiable state evolution for LLM tool calling. |
| Outcome: | The proposed framework improves the reliability and verifiability of tool-augmented LLM systems while maintaining competitive performance on multi-step reasoning tasks. |
SecCoder: Towards Generalizable and Robust Secure Code Generation (2024.emnlp-main)
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| Challenge: | Existing secure code generation methods have limited generalizability to unseen test cases and poor robustness against the attacked model, leading to safety failures in code generation. |
| Approach: | They propose a generalizable and robust secure code generation method SecCoder by using in-context learning and the safe demonstration. |
| Outcome: | The proposed method achieves a significant security improvement of 7.20% on unseen test cases and better robustness against the attacked model. |
ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis (2024.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable in-context learning capabilities in various natural language processing tasks. |
| Approach: | They propose a novel approach ERA-CoT which aids LLMs in understanding context by capturing relationships between entities and supports the reasoning of diverse tasks through Chain-of-Thoughts (CoT). |
| Outcome: | The proposed method improves on GPT3.5 and previous SOTA prompting methods by an average of 5.1% compared to previous prompting approaches. |