Papers by Zicheng Zhou
Rank-Awareness and Angular Constraints: A New Perspective on Learning Sentence Embeddings from NLI Data (2025.emnlp-main)
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| Challenge: | High-quality sentence embeddings are critical for advancing a wide range of Natural Language Processing tasks. |
| Approach: | They propose a framework that leverages the full NLI dataset augmented with pre-computed continuous similarity scores (S) they employ a Rank Margin objective that enforces rank consistency against S using an explicit margin and a Gated Angular objective that conditionally refines embedding geometry based on NLI label (L) and S score agreement. |
| Outcome: | The proposed framework outperforms baseline models on STS and the MTEB benchmarks. |
SCVQ: Sparse-Compensated Vector Quantization for Large Language Models (2026.acl-long)
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| Challenge: | Existing vector quantization methods incur inference overhead due to massive codebook storage and intensive index lookups. |
| Approach: | They propose a framework for vector quantization that incorporates a salience-aware weighted K-means clustering scheme with symmetry constraints to reduce codebook size and indexing costs. |
| Outcome: | The proposed framework achieves a perplexity of 5.78 on WikiText-2 for LLaMA-2-7B at 2-bit quantization while delivering a 1.4 speedup over existing baselines. |
Reliable Use of Lemmas via Eligibility Reasoning and Section-Aware Reinforcement Learning (2026.acl-short)
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| Challenge: | Recent large language models (LLMs) perform strongly on mathematical benchmarks but often import conclusions without validating assumptions. |
| Approach: | They propose a model that encodes a lemma specification and trains with reinforcement learning and section-aware loss masking to assign penalty to the section responsible for errors. |
| Outcome: | The proposed model performs well on benchmarks but often misapplyes lemmas . the model is able to encode the specification and train with reinforcement learning . |
Pre-trained Token-replaced Detection Model as Few-shot Learner (2022.coling-1)
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| Challenge: | Pre-trained masked language models have demonstrated remarkable few-shot learning ability . a novel approach to few- shot learning with pre-tried token-replaced detection models is proposed . |
| Approach: | They propose a method to reformulate a classification or regression task as a token-replaced detection problem by using pre-trained token-based models. |
| Outcome: | The proposed approach outperforms pre-trained masked language models in learning tasks . it can learn models with a few examples and generalize well from limited examples like humans . |