Research 🔬
I am currently working on new studies concerning (1) Better Offline Decision Transformer (2) Innovative method for LLM Reward Modeling in light of RLHF framework (3) Some comparative working of XGBoost and Large Language Models (4) Better Web Agent based on LLM and relative technique. ðŸ”
Published in , 2024
Automatic retrieval techniques are playing a more and more role in legal processes. COLIEE, well-known as an international competition interested in novel methods in legal text retrieval, releases Legal Case Retrieval as their task1 and Statute Law Retrieval as their task3. This paper reports team BM24’s methods to solve task1 and task3. We use matched case / statute pairs provided in the training set to fine tune an embedding model which is then used to build a vector store for semantic retrieval. Specifically, for task 1, we summarize the long case text into segments and choose one informative segment to represent the whole case text to preprocess the long case texts.
Recommended citation:
Published in arxiv preprint, 2023
Large Language Models (LLMs) have emerged as promising agents for web navigation tasks, interpreting objectives and interacting with web pages. However, the efficiency of spliced prompts for such tasks remains underexplored. We introduces AllTogether, a standardized prompt template that enhances task context representation, thereby improving LLMs’ performance in HTML-based web navigation. We evaluate the efficacy of this approach through prompt learning and instruction finetuning based on open-source Llama-2 and API-accessible GPT models. Our results reveal that models like GPT-4 outperform smaller models in web navigation tasks. Additionally, we find that the length of HTML snippet and history trajectory significantly influence performance, and prior step-by-step instructions prove less effective than real-time environmental feedback. Overall, we believe our work provides valuable insights for future research in LLM-driven web agents.
Recommended citation: Liu, Jiarun, Wentao Hu, and Chunhong Zhang. "AllTogether: Investigating the Efficacy of Spliced Prompt for Web Navigation using Large Language Models." arXiv preprint arXiv:2310.18331 (2023). https://arxiv.org/pdf/2310.18331v1.pdf
Published in arxiv preprint, 2023
In the field of few-shot learning (FSL), extensive research has focused on improving network structures and training strategies. However, the role of data processing modules has not been fully explored. Therefore, in this paper, we propose Meta-DM, a generalized data processing module for FSL problems based on diffusion models. Meta-DM is a simple yet effective module that can be easily integrated with existing FSL methods, leading to significant performance improvements in both supervised and unsupervised settings. We provide a theoretical analysis of Meta-DM and evaluate its performance on several algorithms. Our experiments show that combining Meta-DM with certain methods achieves state-of-the-art results.
Recommended citation: Hu, W., Jiang, X., Liu, J., Yang, Y., & Tian, H. (2023). Meta-DM: Applications of Diffusion Models on Few-Shot Learning. arXiv preprint arXiv:2305.08092. https://arxiv.org/pdf/2305.08092.pdf
