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- Hai ZhaoSuggested from co-authorship
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Works182 from public data
- Multimodal Chain-of-Thought Reasoning in Language Models966
This work proposes Multimodal-CoT that incorporates language (text) and vision (images) modalities into a two-stage framework that separates rationale generation and answer inference, and indicates that it offers the advantages of mitigating hallucination and enhancing convergence speed.
- Is ChatGPT a General-Purpose Natural Language Processing Task Solver?894
It is found that ChatGPT performs well on many tasks favoring reasoning capabilities while it still faces challenges when solving specific tasks such as sequence tagging, and with extensive empirical studies, both the effectiveness and limitations of the current version of ChatG PT are demonstrated.
- Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs586
This paper introduces novel efficiency metrics from both outcome and process perspectives to evaluate the rational use of computational resources by o1-like models, and proposes strategies to mitigate overthinking, streamlining reasoning processes without compromising accuracy.
- Semantics-Aware BERT for Language Understanding407
This work proposes to incorporate explicit contextual semantics from pre-trained semantic role labeling, and introduces an improved language representation model, Semantics-aware BERT (SemBERT), which is capable of explicitly absorbing contextual semantics over a BERT backbone.
- DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning282
DeepMath-103K is introduced, a large-scale mathematical dataset designed with high difficulty, rigorous decontamination against numerous benchmarks, and verifiable answers for rule-based RL reward that fosters the development of generalizable and advancing reasoning.
- Retrospective Reader for Machine Reading Comprehension241
A retrospective reader (Retro-Reader) that integrates two stages of reading and verification strategies: 1) sketchy reading that briefly investigates the overall interactions of passage and question, and yields an initial judgment; 2) intensive reading that verifies the answer and gives the final prediction.
- 232
- SG-Net: Syntax-Guided Machine Reading Comprehension199
This work uses syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanism for better linguistically motivated word representations and shows that the proposed SG-Net design helps achieve substantial performance improvement over strong baselines.
- Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs172
A decoding strategy with thought switching penalty TIP that discourages premature transitions between thoughts, encouraging deeper exploration of each reasoning path and contributes to understanding reasoning inefficiencies in o1-like LLMs and offers a practical solution to enhance their problem-solving capabilities.
- DCMN+: Dual Co-Matching Network for Multi-Choice Reading Comprehension141
This work proposes dual co-matching network (DCMN) which models the relationship among passage, question and answer options bidirectionally and integrates two reading strategies into the model, inspired by how humans solve multi-choice questions.
- 135
- Igniting Language Intelligence: The Hitchhiker’s Guide from Chain-of-Thought Reasoning to Language Agents126
A thorough discourse is orchestrated, penetrating vital research dimensions, encompassing the foundational mechanics of CoT techniques, with a focus on elucidating the circumstances and justification behind its efficacy; the paradigm shift in CoT; and the burgeoning of language agents fortified by CoT approaches.
- Neural Machine Translation with Universal Visual Representation124
A universal visual representation learned over the monolingual corpora with image annotations is presented, which overcomes the lack of large-scale bilingual sentence-image pairs, thereby extending image applicability in NMT.
- Exploring Human-Like Translation Strategy with Large Language Models118
This work proposes the MAPS framework, which stands for Multi-Aspect Prompting and Selection, and enables LLMs first to analyze the given source sentence and induce three aspects of translation-related knowledge to guide the final translation process.
- Dependency or Span, End-to-End Uniform Semantic Role Labeling102
This paper presents an end-to-end model for both dependency and span SRL with a unified argument representation to deal with two different types of argument annotations in a uniform fashion and jointly predict all predicates and arguments.
- Risks of AI scientists: prioritizing safeguarding over autonomy98
The authors examine the vulnerabilities of AI scientists, propose a risk taxonomy based on user intent and impact domains, and develop a triadic safeguarding framework emphasizing human regulation, agent alignment, and environmental feedback understanding.
- Topic-Aware Multi-turn Dialogue Modeling97
This paper presents a novel topic-aware solution for multi-turn dialogue modeling, which segments and extracts topic- aware utterances in an unsupervised way, so that the resulted model is capable of capturing salient topic shift at discourse-level in need and thus effectively track topic flow during multi- turn conversation.
- Flooding spread of manipulated knowledge in LLM-based multi-agent communities95
This paper proposes a novel two-stage attack method involving Persuasiveness Injection and Manipulated Knowledge Injection to systematically explore the potential for manipulated knowledge to spread without explicit prompt manipulation and demonstrates that it can successfully induce LLM-based agents to spread both counterfactual and toxic knowledge without degrading their foundational capabilities during agent communication.
- A Unified Syntax-aware Framework for Semantic Role Labeling91
This work extends existing models and proposes a unified framework to comprehensively explore the role of syntax for SRL task, and confirms that high-quality syntactic parse could still effectively enhance syntactically-driven SRL.
- 90
- TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models87
This paper proposes TrojanRAG, which employs a joint backdoor attack in the Retrieval-Augmented Generation, thereby manipulating LLMs in universal attack scenarios and shows that TrojanRAG exhibits versatility threats while maintaining retrieval capabilities on normal queries.
- Self-Prompting Large Language Models for Zero-Shot Open-Domain QA74
A Self-Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of LLMs and their strong instruction understanding abilities and even achieves comparable performance with various customized fine-tuned models on full training data.
- Data-dependent Gaussian Prior Objective for Language Generation70
Experimental results show that the proposed data-dependent Gaussian prior objective (D2GPo) can effectively make use of more detailed prior in the data and significantly improve the performance of typical language generation tasks, including supervised and unsupervised machine translation, text summarization, storytelling, and image caption.
- Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue57
A novel model is proposed to fill the gap in retrieval-based multi-turn dialogue modeling by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history by decouple the contextualized word representations by masking mechanisms in Transformer-based PrLM.
- SAFELearning: Secure Aggregation in Federated Learning With Backdoor Detectability55
A new federated learning technique SAFELearning is proposed to support backdoor detection for secure aggregation and is robust against malicious and faulty participants, whilst being more efficient than the state-of-art secure aggregation protocol in terms of both communication and computation costs.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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