Papers by Yunxuan Li
Learning to Ask Denotative and Connotative Questions for Knowledge-based VQA (2024.findings-emnlp)
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| Challenge: | Large language models have attracted increasing attention due to their prominent performance on various tasks. |
| Approach: | They propose to let LLMs learn to ask informative questions to collect visual information . they introduce concepts of denotation and connotation to promote image and question understanding . |
| Outcome: | The proposed model can generate high-quality questions and efficiently collect required information without expensive training or annotations. |
Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning (2024.findings-emnlp)
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Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Kumar Dubey, Alexandre Rame, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Leonard Hussenot, Olivier Bachem, Edouard Leurent
| Challenge: | Existing approaches for multi-objective Reinforcement Learning (RL) are difficult due to plurality of preferences and applications. |
| Approach: | They propose a framework for finetuning language models on multiple objectives using conditional language policy. |
| Outcome: | The proposed framework outperforms and Pareto-dominates existing approaches for multi-objective Reinforcement Learning (RL) it does not require training or maintaining multiple models to achieve different trade-offs between the objectives. |
Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision (2024.findings-emnlp)
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| Challenge: | a method for process supervision has shown significant improvements in multi-step problem solving . despite the advances in process supervision, there are still easily observable mistakes in state-of-the-art LLMs. |
| Approach: | They propose a method for automating data curation by using a trained verifier to evaluate intermediate steps generated by a reasoner. |
| Outcome: | The proposed method improves the performance of PaLM 2 on math and coding tasks. |
Dynamically Fused Graph Network for Multi-hop Reasoning (P19-1)
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| Challenge: | Text-based question answering (TBQA) has been studied extensively in recent years. |
| Approach: | They propose a Dynamically Fused Graph Network to answer questions requiring multiple scattered evidence and reasoning over them. |
| Outcome: | The proposed method achieves competitive results on a public TBQA dataset and produces interpretable reasoning chains. |
Improving Multi-Agent Debate with Sparse Communication Topology (2024.findings-emnlp)
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| Challenge: | Existing approaches to multi-agent debates use a brute force algorithm, resulting in a computationally intensive process. |
| Approach: | They propose to extend the multi-agent debate framework to multi-modal reasoning and alignment labeling tasks, showcasing its broad applicability and effectiveness. |
| Outcome: | The proposed framework can achieve comparable or superior performance while significantly reducing computational costs. |