Search Results for author:
Found 18 papers, 8 papers with code
HIPPO: Enhancing the Table Understanding Capability of Large Language Models through Hybrid-Modal Preference Optimization
Tabular data contains rich structural semantics and plays a crucial role in organizing and manipulating information.
TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators
Despite advances in large language models (LLMs) for conventional code generation, these models struggle to generate accurate, performance-optimized Triton code, as they lack awareness of its specifications and the complexities of GPU programming.
ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation
: (1) Low executability and poor restoration of chart details in the generated code and (2) Lack of large-scale and diverse training data.
Uncommon Belief in Rationality
Common knowledge/belief in rationality is the traditional standard assumption in analysing interaction among agents.
Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring
Existing black-box jailbreak methods often rely on model feedback, repeatedly submitting queries with detectable malicious instructions during the attack search process.
LLM$\times$MapReduce: Simplified Long-Sequence Processing using Large Language Models
The proposed LLM$\times$MapReduce framework splits the entire document into several chunks for LLMs to read and then aggregates the intermediate answers to produce the final output.
Research on Image Processing and Vectorization Storage Based on Garage Electronic Maps
For the purpose of achieving a more precise definition and data analysis of images, this study conducted a research on vectorization and rasterization storage of electronic maps, focusing on a large underground parking garage map.
Agentive Permissions in Multiagent Systems
This paper proposes to distinguish four forms of agentive permissions in multiagent settings.
Exploring Hybrid Question Answering via Program-based Prompting
Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heterogeneous data.
Responsibility in Extensive Form Games
This paper shows that the newly proposed notion of responsibility and counterfactual responsibility are not definable through each other and studies the responsibility gap for these two forms of responsibility.
Explanation Graph Generation via Generative Pre-training over Synthetic Graphs
The generation of explanation graphs is a significant task that aims to produce explanation graphs in response to user input, revealing the internal reasoning process.
TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering
In this paper, we aim to address the above limitation by leveraging the implicit knowledge stored in PrLMs and propose a two-stage prompt-based unsupervised commonsense question answering framework (TSGP).
Truth Set Algebra: A New Way to Prove Undefinability
The article proposes a new technique for proving the undefinability of logical connectives through each other and illustrates the technique with several examples.
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training
In this work, we propose LEMON, a general framework for language-based environment manipulation tasks.
Social Sourcing: Incorporating Social Networks Into Crowdsourcing Contest Design
The second mechanism has an asymmetric Bayesian Nash equilibrium, and agents' behaviors in equilibrium show a vast diversity which is strongly related to their social relations.
JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering
Specifically, JointLK performs joint reasoning between LM and GNN through a novel dense bidirectional attention module, in which each question token attends on KG nodes and each KG node attends on question tokens, and the two modal representations fuse and update mutually by multi-step interactions.
Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification
Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table.
Learn to Combine Linguistic and Symbolic Information for Table-based Fact Verification
Table-based fact verification is expected to perform both linguistic reasoning and symbolic reasoning.
