Papers by Linjing Li
POSITION BIAS MITIGATES POSITION BIAS: Mitigate Position Bias Through Inter-Position Knowledge Distillation (2025.emnlp-main)
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| Challenge: | Positional bias (PB) manifests as non-uniform sensitivity across contextual locations . previous studies have addressed PB by modifying the underlying architectures or employing extensive contextual awareness training. |
| Approach: | They propose a position-to-position knowledge distillation framework that leverages position-induced disparities to counteract PB. |
| Outcome: | The proposed framework reduces positional bias and improves performance on retrieval and reasoning tasks. |
Entropy Scheduling in Reinforcement Learning for Large Language Models (2026.findings-acl)
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| Challenge: | entropy in reinforcement learning functions analogously to the learning rate in LLMs. |
| Approach: | They propose an entropy scheduling system that optimizes different pre-set goals by controlling and scheduling entropicy at each step of the RL process. |
| Outcome: | The proposed method improves AIME2024 from 50.9 to 54.9 within 40 training steps. |
LDM2: A Large Decision Model Imitating Human Cognition with Dynamic Memory Enhancement (2023.findings-emnlp)
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| Challenge: | Extensive experiments conducted in two interactive environments have shown that our LDM2 outperforms the baselines in terms of both score and success rate. |
| Approach: | They propose a large decision model with memory that leverages a dynamic memory mechanism to construct dynamic prompts, guiding the LLMs in making proper decisions according to the faced state. |
| Outcome: | The proposed model outperforms baseline models in two interactive environments in terms of score and success rate. |
Unearthing Gems from Stones: Policy Optimization with Negative Sample Augmentation for LLM Reasoning (2025.findings-emnlp)
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| Challenge: | Recent advances in reasoning language models have witnessed a paradigm shift from short to long CoT pattern. |
| Approach: | They propose a behavior-constrained policy gradient with negative sample augmented (BCPG-NSA) negative steps are valuable components in long CoT models, authors argue . |
| Outcome: | The proposed framework outperforms baselines on math/coding reasoning benchmarks using the same training dataset. |
Beyond the First Error: Process Reward Models for Reflective Mathematical Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for training effective PRMs focus on the first incorrect step and all preceding steps, assuming that all subsequent steps are incorrect. |
| Approach: | They propose a data annotation method specifically designed to score the long CoT reasoning process by using an LLM-based judger for annotation. |
| Outcome: | The proposed method improves PRMs' ability to identify effective self-correction behaviors and reasoning based on erroneous steps. |
Spec-o3: A Tool-Augmented Vision-Language Agent for Rare Celestial Object Candidate Vetting via Automated Spectral Inspection (2026.acl-long)
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| Challenge: | Spec-o3 is a tool-augmented vision-language agent that performs astronomer-aligned spectral inspection. |
| Approach: | They propose a tool-augmented vision-language agent that performs astronomer-aligned spectral inspection via interleaved multimodal chain-of-thought reasoning. |
| Outcome: | Spec-o3 outperforms traditional visual inspection methods on rare-object inspection tasks. |
Unveiling Factual Recall Behaviors of Large Language Models through Knowledge Neurons (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models have underscored their exceptional reasoning prowess with natural language understanding across a broad spectrum of tasks. |
| Approach: | They examine whether Large Language Models actively recall or retrieve their internal repositories of factual knowledge when faced with reasoning tasks. |
| Outcome: | The proposed model improves reasoning performance while suppressing it leads to notable degradation. |
Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs (2020.coling-main)
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| Challenge: | Existing approaches to natural language inference rely on semantic knowledge, but background knowledge is limited to a few specific types. |
| Approach: | They propose a Knowledge Graph-enhanced NLI model that leverages background knowledge stored in knowledge graphs to facilitate inference. |
| Outcome: | The proposed model can leverage background knowledge stored in knowledge graphs to perform the task. |
Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models? (2025.findings-acl)
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| Challenge: | Recent advances in handling long sequences have unlocked new possibilities for long-context in-contact learning (ICL). |
| Approach: | They investigate how increased examples influence predictive uncertainty . they quantify uncertainty across different “shot” configurations and focus on EU . |
| Outcome: | The proposed model reduces uncertainty in simple and complex tasks by injecting task-specific knowledge. |
Evaluating Generalization Capability of Language Models across Abductive, Deductive and Inductive Logical Reasoning (2025.coling-main)
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| Challenge: | Recent research in language models (LMs) have demonstrated remarkable performance on many natural language tasks, yet to what extent LMs possess the capability of generalizing to unseen logical rules remains unclear. |
| Approach: | They propose to use a dataset to assess the generalization capabilities of LMs on ADI reasoning to assess their generalization abilities. |
| Outcome: | The proposed dataset shows that LMs perform poorly on ADI reasoning tasks and lacks generalization capabilities. |