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- A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems80
This work proposes a novel Unified MultI-goal conversational recommeNDer system (UniMIND), which unify these four tasks with different formulations into the same sequence-to-sequence paradigm and achieves state of the art performance on all tasks with a unified model.
- Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering41
Results on two scientific multi-hop QA datasets show that the proposed framework can surpass recent approaches including those using additional knowledge graphs while maintaining high explainability on OpenBookQA and achieve a new state-of-the-art result on ARC-Challenge in a computationally practicable setting.
- Verification of the Generic Architecture of a Memory Circuit Using Parametric Timed Automata38
Using a variant of Clariso-Cortadella’s parametric method for verifying asynchronous circuits, a set of linear constraints are formally derived that ensure the correctness of some crucial timing behaviours of the architecture of SPSMALL memory.
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- Nonfactoid Question Answering as Query-Focused Summarization With Graph-Enhanced Multihop Inference34
A novel query-focused summarization method, namely a graph-enhanced multihop query- focused summarizer (GMQS), to tackle the nonfactoid QA problem and possesses the capability of performing explainable multihip reasoning.
- Exploiting Reasoning Chains for Multi-hop Science Question Answering32
The proposed Chain Guided Retriever-reader framework allows the retriever to capture step-by-step clues of the entire reasoning process, which is not only shown to be effective on two challenging multi-hop Science QA tasks, namely OpenBookQA and ARC-Challenge, but also favors explainability.
- Lingshu: Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning17
This work proposes a comprehensive data-curation procedure that efficiently acquires rich medical knowledge data not only from medical imaging but also from extensive medical texts and general domain data and develops a medical-specialized MLLM, Lingshu-Med, which consistently outperforms existing multimodal baselines in most tasks.
- SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages13
- SeaLLMs-Audio: Large Audio-Language Models for Southeast Asia13
The first large audio-language model (LALM) tailored for multiple Southeast Asian languages-Indonesian, Thai, Vietnamese, English, and Chinese-alongside English and Chinese is introduced, showing that SeaLLMs-Audio achieves competitive performance compared with other LALMs on SEA languages.
- Addressing the Vulnerability of NMT in Input Perturbations11
This paper improves the robustness of NMT models by reducing the effect of noisy words through a Context-Enhanced Reconstruction (CER) approach and demonstrates robustness improvement on both news and social media text.
- Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach9
A supervised alignment model for translation error detection (AlignDet) is built based on a simple Alignment Triangle strategy to set the benchmark for automatic error detection task and discusses the difficulties and benefits of this task for existing evaluation metrics.
- Timing analysis of an embedded memory: SPSMALL6
A high-level formalism, called Abstract Functional and Timing Graph (AFTG), is proposed, for describing a memory architecture, which combines logical functionality and timing, and the response times of the modeled memory are computed.
- Automatic Complexity Analysis for Programs Extracted from Coq Proof5
An automatic complexity analysis mechanism for programs extracted from proofs carried out with the proof assistant Coq, which generates a description of the time-complexity of a MiniML program in terms of the number of steps needed for its execution.
- Beyond Entropy: Correctness-Aware Advantage Shaping via Contrastive Policy Optimization1
Contrastive Policy Optimization is proposed, which uses token-level contrastive disagreement between reference-guided and vanilla generation distributions for correctness-aware advantage shaping and shows that correct and incorrect responses naturally support exploration and exploitation respectively, and balancing both leads to the best performance.
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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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